"It doesn't matter how beautiful your idea is, it doesn't matter how smart or important you are. If the idea doesn't agree with reality, it's wrong", Richard Feynman (paraphrased)
It’s 2045. Earth is green again. Free from cellular towers and the terrestrial radiation of yet another G, no longer needed to justify endless telecom upgrades. Humanity has finally transcended its communication needs to the sky, fully served by swarms of Low Earth Orbit (LEO) satellites.
Millions of mobile towers have vanished. No more steel skeletons cluttering skylines and nature in general. In their place: millions of beams from tireless LEO satellites, now whispering directly into our pockets from orbit.
More than 1,200 MHz of once terrestrially-bound cellular spectrum below the C-band had been uplifted to LEO satellites. Nearly 1,500 MHz between 3 and 6 GHz had likewise been liberated from its earthly confines, now aggressively pursued by the buzzing broadband constellations above.
It all works without a single modification to people’s beloved mobile devices. Everyone enjoyed the same, or better, cellular service than in those wretched days of clinging to terrestrial-based infrastructure.
So, how did this remarkable transformation come about?
THE COVERAGE.
First, let’s talk about coverage. The chart below tells the story of orbital ambition through three very grounded curves. On the x-axis, we have the inclination angle, which is the degree to which your satellites are encouraged to tilt away from the equator to perform their job. On the y-axis: how much of the planet (and its people) they’re actually covering. The orange line gives us land area coverage. It starts low, as expected, tropical satellites don’t care much for Greenland. But as the inclination rises, so does their sense of duty to the extremes (the poles that is). The yellow line represents population coverage, which grows faster than land, maybe because humans prefer to live near each other (or they like the scenery). By the time you reach ~53° inclination, you’re covering about 94% of humanity and 84% of land areas. The dashed white line represents mobile cell coverage, the real estate of telecom towers. A constellation at a 53° inclination would cover nearly 98% of all mobile site infrastructure. It serves as a proxy for economic interest. It closely follows the population curve, but adds just a bit of spice, reflecting urban density and tower sprawl.
This chart illustrates the cumulative global coverage achieved at varying orbital inclination angles for three key metrics: land area (orange), population (yellow), and estimated terrestrial mobile cell sites (dashed white). As inclination increases from equatorial (0°) to polar (90°), the percentage of global land and population coverage rises accordingly. Notably, population coverage reaches approximately 94% at ~53° inclination, a critical threshold for satellite constellations aiming to maximize global user reach without the complexity of polar orbits. The mobile cell coverage curve reflects infrastructure density and aligns closely with population distribution.
The satellite constellation’s beams have replaced traditional terrestrial cells, providing a one-to-one coverage substitution. They not only replicate coverage in former legacy cellular areas but also extend service to regions that previously lacked connectivity due to low commercial priority from telecom operators. Today, over 3 million beams substitute obsolete mobile cells, delivering comparable service across densely populated areas. An additional 1 million beams have been deployed to cover previously unserved land areas, primarily rural and remote regions, using broader, lower-capacity beams with radii up to 10 kilometers. While these rural beams do not match the density or indoor penetration of urban cellular coverage, they represent a cost-effective means of achieving global service continuity, especially for basic connectivity and outdoor access in sparsely populated zones.
Conclusion? If you want to build a global satellite mobile network, you don’t need to orbit the whole planet. Just tilt your constellation enough to touch the crowded parts, and leave the tundra to the poets. However, this was the “original sin” of LEO Direct-2-Cellular satellites.
THE DEMAND.
Although global mobile traffic growth slowed notably after the early 2020s, and the terrestrial telecom industry drifted toward its “end of history” moment, the orbital network above inherited a double burden. Not only did satellite constellations need to deliver continuous, planet-wide coverage, a milestone legacy telecoms had never reached, despite millions of ground sites, but they also had to absorb globally converging traffic demands as billions of users crept steadily toward the throughput mean.
This chart shows the projected DL traffic across a full day (UTC), based on regions where local time falls within the evening Busy Hour window (17:00–22:00) and are within satellite coverage (minimum elevation ≥ 25°). The BH population is calculated hourly, taking into account time zone alignment and visibility, with a 20% concurrency rate applied. Each active user is assumed to consume 500 Mbps downlink in 2045. The peak, reaching overThis chart shows the uplink traffic demand experienced across a full day (UTC), based on regions under Busy Hour conditions (17:00–22:00 local time) and visible to the satellite constellation (with a minimum elevation angle of 25°). For each UTC hour, the BH population within coverage is calculated using global time zone mapping. Assuming a 20% concurrency rate and an average uplink throughput of 50 Mbps per active user, the total UL traffic is derived. The resulting curve reflects how demand shifts in response to the Earth’s rotation beneath the orbital band. The peak, reaching over
The radio access uplink architecture relies on low round-trip times for proper scheduling, timing alignment, and HARQ (Hybrid Automatic Repeat Request) feedback cycles. The propagation delay at 350 km yields a round-trip time of about 2.5 to 3 milliseconds, which falls within the bounds of what current specifications can accommodate. This is particularly important for latency-sensitive applications such as voice, video, and interactive services that require low jitter and reliable feedback mechanisms. In contrast, orbits at 550 km or above push latency closer to the edge of what NR protocols can tolerate, which could hinder performance or require non-standard adaptations. The beam geometry also plays a central role. At lower altitudes, satellite beams projected to the ground are inherently smaller. This smaller footprint translates into tighter beam patterns with narrower 3 dB cut-offs, which significantly improves frequency reuse and spatial isolation. These attributes are important for deploying high-capacity networks in densely populated urban environments, where interference and spectrum efficiency are paramount. Narrower beams allow D2C operators to steer coverage toward demand centers while minimizing adjacent-beam interference dynamically. Operating at 350 km is not without drawbacks. The satellite’s ground footprint at this altitude is smaller, meaning that more satellites are required to achieve full Earth coverage. Additionally, satellites at this altitude are exposed to greater atmospheric drag, resulting in shorter orbital lifespans unless they are equipped with more powerful or efficient propulsion systems to maintain altitude. The current design aims for a 5-year orbital lifespan. Despite this, the shorter lifespan has an upside, as it reduces the long-term risks of space debris. Deorbiting occurs naturally and quickly at lower altitudes, making the constellation more sustainable in the long term.
THE CONSTELLATION.
The satellite-to-cellular infrastructure has now fully matured into a global-scale system capable of delivering mobile broadband services that are not only on par with, but in many regions surpass, the performance of terrestrial cellular networks. At its core lies a constellation of low Earth orbit satellites operating at an altitude of 350 kilometers, engineered to provide seamless, high-quality indoor coverage for both uplink and downlink, even in densely urban environments.
To meet the evolving expectations of mobile users, each satellite beam delivers a minimum of 50 Mbps of uplink capacity and 500 Mbps of downlink capacity per user, ensuring full indoor quality even in highly cluttered environments. Uplink transmissions utilize the 600 MHz to 1800 MHz band, providing 1200 MHz of aggregated bandwidth. Downlink channels span 1500 MHz of spectrum, ranging from 2100 MHz to the upper edge of the C-band. At the network’s busiest hour (e.g., around 20:00 local time) across the most densely populated regions south of 53° latitude, the system supports a peak throughput of 60,000 Tbps for downlink and 6,000 Tbps for uplink. To guarantee reliability under real-world utilization, the system is engineered with a 25% capacity overhead, raising the design thresholds to 75,000 Tbps for DL and 7,500 Tbps for UL during peak demand.
Each satellite beam is optimized for high spectral efficiency, leveraging advanced beamforming, adaptive coding, and cutting-edge modulation. Under these conditions, downlink beams deliver 4.5 Gbps, while uplink beams, facing more challenging reception constraints, achieve 1.8 Gbps. Meeting the adjusted peak-hour demand requires approximately 16.7 million active DL beams and 4.2 million UL beams, amounting to over 20.8 million simultaneous beams concentrated over the peak demand region.
Thanks to significant advances in onboard processing and power systems, each satellite now supports up to 5,000 independent beams simultaneously. This capability reduces the number of satellites required to meet regional peak demand to approximately 4,200. These satellites are positioned over a region spanning an estimated 45 million square kilometers, covering the evening-side urban and suburban areas of the Americas, Europe, Africa, and Asia. This configuration yields a beam density of nearly 0.46 beams per square kilometer, equivalent to one active beam for every 2 square kilometers, densely overlaid to provide continuous, per-user, indoor-grade connectivity. In urban cores, beam radii are typically below 1 km, whereas in lower-density suburban and rural areas, the system adjusts by using larger beams without compromising throughput.
Because peak demand rotates longitudinally with the Earth’s rotation, only a portion of the entire constellation is positioned over this high-demand region at any given time. To ensure 4,200 satellites are always present over the region during peak usage, the total constellation comprises approximately 20,800 satellites, distributed across several hundred orbital planes. These planes are inclined and phased to optimize temporal availability, revisit frequency, and coverage uniformity while minimizing latency and handover complexity.
The resulting Direct-to-Cellular satellite constellation and system of today is among the most ambitious communications infrastructures ever created. With more than 20 million simultaneous beams dynamically allocated across the globe, it has effectively supplanted traditional mobile towers in many regions, delivering reliable, high-speed, indoor-capable broadband connectivity precisely where and when people need it.
When Telcos Said ‘Not Worth It,’ Satellites Said ‘Hold My Beam. In the world of 2045, even the last village at the end of the dirt road streams at 500 Mbps. No tower in sight, just orbiting compassion and economic logic finally aligned.
THE SATELLITE.
The Cellular Device to Satellite Path.
The uplink antennas aboard the Direct-to-Cellular satellites have been specifically engineered to reliably receive indoor-quality transmissions from standard (unmodified) mobile devices operating within the 600 MHz to 1800 MHz band. Each device is expected to deliver a minimum of 50 Mbps uplink throughput, even when used indoors in heavily cluttered urban environments. This performance is made possible through a combination of wideband spectrum utilization, precise beamforming, and extremely sensitive receiving systems in orbit. The satellite uplink system operates across 1200 MHz of aggregated bandwidth (e.g., 60 channels of 20 MHz), spanning the entire upper UHF and lower S-band. Because uplink signals originate from indoor environments, where wall and structural penetration losses can exceed 20 dB, the satellite link budget must compensate for the combined effects of indoor attenuation and free-space propagation at a 350 km orbital altitude. At 600 MHz, which represents the lowest frequency in the UL band, the free space path loss alone is approximately 133 dB. When this is compounded with indoor clutter and penetration losses, the total attenuation the satellite must overcome reaches approximately 153 dB or more.
Rather than specifying the antenna system at a mid-band average frequency, such as 900 MHz (i.e., the mid-band of the 600 MHz to 1800 MHz range), the system has been conservatively engineered for worst-case performance at 600 MHz. This design philosophy ensures that the antenna will meet or exceed performance requirements across the entire uplink band, with higher frequencies benefiting from naturally improved gain and narrower beamwidths. This choice guarantees that even the least favorable channels, those near 600 MHz, support reliable indoor-grade uplink service at 50 Mbps, with a minimum required SNR of 10 dB to sustain up to 16-QAM modulation. Achieving this level of performance at 600 MHz necessitated a large physical aperture. The uplink receive arrays on these satellites have grown to approximately 700 to 750 m² in area, and are constructed using modular, lightweight phased-array tiles that unfold in orbit. This aperture size enables the satellite to achieve a receive gain of approximately 45 dBi at 600 MHz, which is essential for detecting low-power uplink transmissions with high spectral efficiency, even from users deep indoors and under cluttered conditions.
Unlike earlier systems, such as AST SpaceMobile’s BlueBird 1, launched in the mid-2020s with an aperture of around 900 m² and challenged by the need to acquire indoor uplink signals, today’s Direct-to-Cellular (D2C) satellites optimize the uplink and downlink arrays separately. This separation allows each aperture to be custom-designed for its frequency and link budget requirements. The uplink arrays incorporate wideband, dual-polarized elements, such as log-periodic or Vivaldi structures, backed by high-dynamic-range low-noise amplifiers and a distributed digital beamforming backend. Assisted by real-time AI beam management, each satellite can simultaneously support and track up to 2,500 uplink beams, dynamically allocating them across the active coverage region.
Despite their size, these receive arrays are designed for compact launch configurations and efficient in-orbit deployment. Technologies such as inflatable booms, rigidizable mesh structures, and ultralight composite materials allow the arrays to unfold into large apertures while maintaining structural stability and minimizing mass. Because these arrays are passive receivers, thermal loads are significantly lower than those of transmit systems. Heat generation is primarily limited to the digital backend and front-end amplification chains, which are distributed across the array surface to facilitate efficient thermal dissipation.
The Satellite to Cellular Device Path.
The downlink communication path aboard Direct-to-Cellular satellites is engineered as a fully independent system, physically and functionally separated from the uplink antenna. This separation reflects a mature architectural philosophy that has been developed over decades of iteration. The downlink and uplink systems serve fundamentally different roles and operate across vastly different frequency bands, with their power, thermal, and antenna constraints. The downlink system operates in the frequency range from 2100 MHz up to the upper end of the C-band, typically around 4200 MHz. This is significantly higher than the uplink range, which extends from 600 to 1800 MHz. Due to this disparity in wavelength, a factor of nearly six between the lowest uplink and highest downlink frequencies, a shared aperture is neither practical nor efficient. It is widely accepted today that integrating transmit and receive functions into a single broadband aperture would compromise performance on both ends. Instead, today’s satellites utilize a dual-aperture approach, with the downlink antenna system optimized exclusively for high-frequency transmission and the uplink array designed independently for low-frequency reception.
In order to deliver 500 Mbps per user with full indoor coverage, each downlink beam must sustain approximately 4.5 Gbps, accounting for spectral reuse and beam overlap. At an orbital altitude of 350 kilometers, downlink beams must remain narrow, typically covering no more than a 1-kilometer radius in urban zones, to match uplink geometry and maintain beam-level concurrency. The antenna gain required to meet these demands is in the range of 50 to 55 dBi, which the satellites achieve using high-frequency phased arrays with a physical aperture of approximately 100 to 200 m². Because the downlink system is responsible for high-power transmission, the antenna tiles incorporate GaN-based solid-state power amplifiers (SSPAs), which deliver hundreds of watts per panel. This results in an overall effective isotropic radiated power (EIRP) of 50 to 60 dBW per beam, sufficient to reach deep indoor devices even at the upper end of the C-band. The power-intensive nature of the downlink system introduces thermal management challenges (describe below in the next section), which are addressed by physically isolating the transmit arrays from the receiver surfaces. The downlink and uplink arrays are positioned on opposite sides of the spacecraft bus or thermally decoupled through deployable booms and shielding layers.
The downlink beamforming is fully digital, allowing real-time adaptation of beam patterns, power levels, and modulation schemes. Each satellite can form and manage up to 2,500 independent downlink beams, which are coordinated with their uplink counterparts to ensure tight spatial and temporal alignment. Advanced AI algorithms help shape beams based on environmental context, usage density, and user motion, thereby further improving indoor delivery performance. The modulation schemes used on the downlink frequently reach 256-QAM and beyond, with spectral efficiencies of six to eight bits per second per Hz in favorable conditions.
The physical deployment of the downlink antenna varies by platform, but most commonly consists of front-facing phased array panels or cylindrical surfaces fitted with azimuthally distributed tiles. These panels can be either fixed or mounted on articulated platforms that allow active directional steering during orbit, depending on the beam coverage strategy, an arrangement also called gumballed.
No Bars? Not on This Planet. In 2045, even the Icebears will have broadband. When satellites replaced cell towers, the Arctic became just another neighborhood in the global gigabit grid.
Satellite System Architecture.
The Direct-to-Cellular satellites have evolved into high-performance, orbital base stations that far surpass the capabilities of early systems, such as AST SpaceMobile’s Bluebird 1 or SpaceX’s Starlink V2 Mini. These satellites are engineered not merely to relay signals, but to deliver full-featured indoor mobile broadband connectivity directly to standard handheld devices, anywhere on Earth, including deep urban cores and rural regions that have been historically underserved by terrestrial infrastructure.
As described earlier, today’s D2C satellite supports up to 5,000 simultaneous beams, enabling real-time uplink and downlink with mobile users across a broad frequency range. The uplink phased array, designed to capture low-power, deep-indoor signals at 600 MHz, occupies approximately 750 m². The DL array, optimized for high-frequency, high-power transmission, spans 150 to 200 m². Unlike early designs, such as Bluebird 1, which used a single, large combined antenna, today’s satellites separate the uplink and downlink arrays to optimize each for performance, thermal behavior, and mechanical deployment. These two systems are typically mounted on opposite sides of the satellite and thermally isolated from one another.
Thermal management is one of the defining challenges of this architecture. While AST’s Bluebird 1 (i.e., from mid-2020s) boasted a large antenna aperture approaching 900 m², its internal systems generated significantly less heat. Bluebird 1 operated with a total power budget of approximately 10 to 12 kilowatts, primarily dedicated to a handful of downlink beams and limited onboard processing. In contrast, today’s D2C satellite requires a continuous power supply of 25 to 35 kilowatts, much of which must be dissipated as heat in orbit. This includes over 10 kilowatts of sustained RF power dissipation from the DL system alone, in addition to thermal loads from the digital beamforming hardware, AI-assisted compute stack, and onboard routing logic. The key difference lies in beam concurrency and onboard intelligence. The satellite manages thousands of simultaneous, high-throughput beams, each dynamically scheduled and modulated using advanced schemes such as 256-QAM and beyond. It must also process real-time uplink signals from cluttered environments, allocate spectral and spatial resources, and make AI-driven decisions about beam shape, handovers, and interference mitigation. All of this requires a compute infrastructure capable of delivering 100 to 500 TOPS (tera-operations per second), distributed across radiation-hardened processors, neural accelerators, and programmable FPGAs. Unlike AST’s Bluebird 1, which offloaded most of its protocol stack to the ground, today’s satellites run much of the 5G core network onboard. This includes RAN scheduling, UE mobility management, and segment-level routing for backhaul and gateway links.
This computational load compounds the satellite’s already intense thermal environment. Passive cooling alone is insufficient. To manage thermal flows, the spacecraft employs large radiator panels located on its outer shell, advanced phase-change materials embedded behind the DL tiles, and liquid loop systems that transfer heat from the RF and compute zones to the radiative surfaces. These thermal systems are intricately zoned and actively managed, preventing the heat from interfering with the sensitive UL receive chains, which require low-noise operation under tightly controlled thermal conditions. The DL and UL arrays are thermally decoupled not just to prevent crosstalk, but to maintain stable performance in opposite thermal regimes: one dominated by high-power transmission, the other by low-noise reception.
To meet its power demands, the satellite utilizes a deployable solar sail array that spans 60 to 80 m². These sails are fitted with ultra-high-efficiency solar cells capable of exceeding 30–35% efficiency. They are mounted on articulated booms that track the sun independently from the satellite’s Earth-facing orientation. They provide enough current to sustain continuous operation during daylight periods, while high-capacity batteries, likely based on lithium-sulfur or solid-state chemistry, handle nighttime and eclipse coverage. Compared to the Starlink V2 Mini, which generates around 2.5 to 3.0 kilowatts, and the Bluebird 1, which operates at roughly 10–12 kilowatts. Today’s system requires nearly three times the generation and five times the thermal rejection capability compared to the initial satellites of the mid-2020s.
Structurally, the satellite is designed to support this massive infrastructure. It uses a rigid truss core (i.e., lattice structure) with deployable wings for the DL system and a segmented, mesh-based backing for the UL aperture. Propulsion is provided by Hall-effect or ion thrusters, with 50 to 100 kilograms of inert propellant onboard to support three to five years of orbital station-keeping at an altitude of 350 kilometers. This height is chosen for its latency and spatial reuse advantages, but it also imposes continuous drag, requiring persistent thrust.
The AST Bluebird 1 may have appeared physically imposing in its time due to its large antenna, thermal, computational, and architectural complexity. Today’s D2C satellite, 20 years later, far exceeds anything imagined two decades earlier. The heat generated by its massive beam concurrency, onboard processing, and integrated network core makes its thermal management system not only more severe than Bluebird 1’s but also one of the primary limiting factors in the satellite’s physical and functional design. This thermal constraint, in turn, shapes the layout of its antennas, compute stack, power system, and propulsion.
Mass and Volume Scaling.
The AST’s Bluebird 1, launched in the mid-2020s, had a launch mass of approximately 1,500 kilograms. Its headline feature was a 900 m² unfoldable antenna surface, designed to support direct cellular connectivity from space. However, despite its impressive aperture, the system was constrained by limited beam concurrency, modest onboard computing power, and a reliance on terrestrial cores for most network functions. The bulk of its mass was dominated by structural elements supporting its large antenna surface and the power and thermal subsystems required to drive a relatively small number of simultaneous links. Bluebird’s propulsion was chemical, optimized for initial orbit raising and limited station-keeping, and its stowed volume fit comfortably within standard medium-lift payload fairings. Starlink’s V2 Mini, although smaller in physical aperture, featured a more balanced and compact architecture. Weighing roughly 800 kilograms at launch, it was designed around high-throughput broadband rather than direct-to-cellular use. Its phased array antenna surface was closer to 20–25 m², and it was optimized for efficient manufacturing and high-density orbital deployment. The V2 Mini’s volume was tightly packed, with solar panels, phased arrays, and propulsion modules folded into a relatively low-profile bus optimized for rapid deployment and low-cost launch stacking. Its onboard compute and thermal systems were scaled to match its more modest power budget, which typically hovered around 2.5 to 3.0 kilowatts.
In contrast, today’s satellites occupy an entirely new performance regime. The dry mass of the satellite ranges between 2,500 and 3,500 kilograms, depending on specific configuration, thermal shielding, and structural deployment method. This accounts for its large deployable arrays, high-density digital payload, radiator surfaces, power regulation units, and internal trusses. The wet mass, including onboard fuel reserves for at least 5 years of station-keeping at 350 km altitude, increases by up to 800 kilograms, depending on the propulsion type (e.g., Hall-effect or gridded ion thrusters) and orbital inclination. This brings the total launch mass to approximately 3,000 to 4,500 kilograms, or more than double ATS’s old Bluebird 1 and roughly five times that of SpaceX’s Starlink V2 Mini.
Volume-wise, the satellites require a significantly larger stowed configuration than either AST’s Bluebird 1 or SpaceX’s Starlink V2 Mini. While both of those earlier systems were designed to fit within traditional launch fairings, Bluebird 1 utilizes a folded hinge-based boom structure, and Starlink V2 Mini is optimized for ultra-compact stacking. Today’s satellite demands next-generation fairing geometries, such as 5-meter-class launchers or dual-stack configurations. This is driven by the dual-antenna architecture and radiator arrays, which, although cleverly folded during launch, expand dramatically once deployed in orbit. In its operational configuration, the satellite spans tens of meters across its antenna booms and solar sails. The uplink array, built as a lightweight, mesh-backed surface supported by rigidizing frames or telescoping booms, unfolds to a diameter of approximately 30 to 35 meters, substantially larger than Bluebird 1’s ~20–25 meter maximum span and far beyond the roughly 10-meter unfolded span of Starlink V2 Mini. The downlink panels, although smaller, are arranged for precise gimballed orientation (i.e., a pivoting mechanism allowing rotation or tilt along one or more axes) and integrated thermal control, which further expands the total deployed volume envelope. The volumetric footprint of today’s D2C satellite is not only larger in surface area but also more spatially complex, as its segregated UL and DL arrays, thermal zones, and solar wings must avoid interference while maintaining structural and thermal equilibrium. Compared to the simplified flat-pack layout of Starlink V2 Mini and the monolithic boom-deployed design of Bluebird 1.
The increase in dry mass, wet mass, and deployed volume is not a byproduct of inefficiency, but a direct result of very substantial performance improvements that were required to replace terrestrial mobile towers with orbital systems. Today’s D2C satellites deliver an order of magnitude more beam concurrency, spectral efficiency, and per-user performance than its 2020s predecessors. This is reflected in every subsystem, from power generation and antenna design to propulsion, thermal control, and computing. As such, it represents the emergence of a new class of satellite altogether: not merely a space-based relay or broadband node, but a full-featured, cloud-integrated orbital RAN platform capable of supporting the global cellular fabric from space.
CAN THE FICTION BECOME A REALITY?
From the perspective of 2025, the vision of a global satellite-based mobile network providing seamless, unmodified indoor connectivity at terrestrial-grade uplink and downlink rates, 50 Mbps up, 500 Mbps down, appears extraordinarily ambitious. The technical description from 2045 outlines a constellation of 20,800 LEO satellites, each capable of supporting 5,000 independent full-duplex beams across massive bandwidths, while integrating onboard processing, AI-driven beam control, and a full 5G core stack. To reach such a mature architecture within two decades demands breakthrough progress across multiple fronts.
The most daunting challenge lies in achieving indoor-grade cellular uplink at frequencies as low as 600 MHz from devices never intended to communicate with satellites. Today, even powerful ground-based towers struggle to achieve sub-1 GHz uplink coverage inside urban buildings. For satellites at an altitude of 350 km, the free-space path loss alone at 600 MHz is approximately 133 dB. When combined with clutter, penetration, and polarization mismatches, the system must close a link budget approaching 153–160 dB, from a smartphone transmitting just 23 dBm (200 mW) or less. No satellite today, including AST SpaceMobile’s BlueBird 1, has demonstrated indoor uplink reception at this scale or consistency. To overcome this, the proposed system assumes deployable uplink arrays of 750 m² with gain levels exceeding 45 dBi, supported by hundreds of simultaneously steerable receive beams and ultra-low-noise front-end receivers. From a 2025 lens, the mechanical deployment of such arrays, their thermal stability, calibration, and mass management pose nontrivial risks. Today’s large phased arrays are still in their infancy in space, and adaptive beam tracking from fast-moving LEO platforms remains unproven at the required scale and beam density.
Thermal constraints are also vastly more complex than anything currently deployed. Supporting 5,000 simultaneous beams and radiating tens of kilowatts from compact platforms in LEO requires heat rejection systems that go beyond current radiator technology. Passive radiators must be supplemented with phase-change materials, active fluid loops, and zoned thermal isolation to prevent transmit arrays from degrading the performance of sensitive uplink receivers. This represents a significant leap from today’s satellites, such as Starlink V2 Mini (~3 kW) or BlueBird 1 (~10–12 kW), neither of which operates with a comparable beam count, throughput, or antenna scale.
The required onboard compute is another monumental leap. Running thousands of simultaneous digital beams, performing real-time adaptive beamforming, spectrum assignment, HARQ scheduling, and AI-driven interference mitigation, all on-orbit and without ground-side offloading, demands 100–500 TOPS of radiation-hardened compute. This is far beyond anything that will be flying in 2025. Even state-of-the-art military systems rely heavily on ground computing and centralized control. The 2045 vision implies on-orbit autonomy, local decision-making, and embedded 5G/6G core functionality within each spacecraft, a full software-defined network node in orbit. Realizing such a capability requires not only next-gen processors but also significant progress in space-grade AI inference, thermal packaging, and fault tolerance.
On the power front, generating 25–35 kW per satellite in LEO using 60–80 m² solar sails pushes the boundary of photovoltaic technology and array mechanics. High-efficiency solar cells must achieve conversion rates exceeding 30–35%, while battery systems must maintain high discharge capacity even in complete darkness. Space-based power architectures today are not yet built for this level of sustained output and thermal dissipation.
Even if the individual satellite challenges are solved, the constellation architecture presents another towering hurdle. Achieving seamless beam handover, full spatial reuse, and maintaining beam density over demand centers as the Earth rotates demands near-perfect coordination of tens of thousands of satellites across hundreds of planes. No current LEO operator (including SpaceX) manages a constellation of that complexity, beam concurrency, or spatial density. Furthermore, scaling the manufacturing, testing, launch, and in-orbit commissioning of over 20,000 high-performance satellites will require significant cost reductions, increased factory throughput, and new levels of autonomous deployment.
Regulatory and spectrum allocation are equally formidable barriers. The vision entails the massively complex undertaking of a global reallocation of terrestrial mobile spectrum, particularly in the sub-3 GHz bands, to LEO operators. As of 2025, such a reallocation is politically and commercially fraught, with entrenched mobile operators and national regulators unlikely to cede prime bands without extensive negotiation, incentives, and global coordination. The use of 600–1800 MHz from orbit for direct-to-device is not yet globally harmonized (and may never be), and existing terrestrial rights would need to be either vacated or managed via complex sharing schemes.
From a market perspective, widespread device compatibility without modification implies that standard mobile chipsets, RF chains, and antennas evolve to handle Doppler compensation, extended RTT timing budgets, and tighter synchronization tolerances. While this is not insurmountable, it requires updates to 3GPP standards, baseband silicon, and potentially network registration logic, all of which must be implemented without degrading terrestrial service. Although NTN (non-terrestrial networks) support has begun to emerge in 5G standards, the level of transparency and ubiquity envisioned in 2045 is not yet backed by practical deployments.
While the 2045 architecture described so far assumes a single unified constellation delivering seamless global cellular service from orbit, the political and commercial realities of space infrastructure in 2025 strongly suggest a fragmented outcome. It is unlikely that a single actor, public or private, will be permitted, let alone able, to monopolize the global D2C landscape. Instead, the most plausible trajectory is a competitive and geopolitically segmented orbital environment, with at least one major constellation originating from China (note: I think it is quit likely we may see two major ones), another from the United States, a possible second US-based entrant, and potentially a European-led system aimed at securing sovereign connectivity across the continent. This fracturing of the orbital mobile landscape imposes a profound constraint on the economic and technical scalability of the system. The assumption that a single constellation could achieve massive economies of scale, producing, launching, and managing tens of thousands of high-performance satellites with uniform coverage obligations, begins to collapse under the weight of geopolitical segmentation. Each competitor must now shoulder its own development, manufacturing, and deployment costs, with limited ability to amortize those investments over a unified global user base. Moreover, such duplication of infrastructure risks saturating orbital slots and spectrum allocations, while reducing the density advantage that a unified system would otherwise enjoy. Instead of concentrating thousands of active beams over a demand zone with a single coordinated fleet, separate constellations must compete for orbital visibility and spectral access over the same urban centers. The result is likely to be a decline in per-satellite utilization efficiency, particularly in regions of geopolitical overlap or contested regulatory coordination.
2045: One Vision, Many Launch Pads. The dream of global satellite-to-cellular service may shine bright, but it won’t rise from a single constellation. With China, the U.S., and others racing skyward, the economics of universal LEO coverage could fracture into geopolitical silos, making scale, spectrum, and sustainability more contested than ever.
Finally, the commercial viability of any one constellation diminishes when the global scale is eroded. While a monopoly or globally dominant operator could achieve lower per-unit satellite costs, higher average utilization, and broader roaming revenues, a fractured environment reduces ARPU (average revenue per user). It increases the breakeven threshold for each deployment. Satellite throughput that could have been centrally optimized now risks duplication and redundancy, increasing operational overhead and potentially slowing innovation as vendors attempt to differentiate on proprietary terms. In this light, the architecture described earlier must be seen as an idealized vision. This convergence point may never be achieved in pure form unless global policy, spectrum governance, and commercial alliances move toward more integrated outcomes. While the technological challenges of the 2045 D2C system are significant, the fragmentation of market structure and geopolitical alignment may prove an equally formidable barrier to realizing the full systemic potential. While a monopoly or globally dominant operator could achieve lower per-unit satellite costs, higher average utilization, and broader roaming revenues, a fractured environment reduces ARPU (average revenue per user). It increases the breakeven threshold for each deployment. Satellite throughput that could have been centrally optimized now risks duplication and redundancy, increasing operational overhead and potentially slowing innovation as vendors attempt to differentiate on proprietary terms. In this light, the architecture described earlier must be seen as an idealized vision. This convergence point may never be achieved in pure form unless global policy, spectrum governance, and commercial alliances move toward more integrated outcomes. While the technological challenges of the 2045 D2C system are significant, the fragmentation of market structure and geopolitical alignment may prove an equally formidable barrier to realizing the full systemic potential.
Heavenly Coverage, Hellish Congestion. Even a single mega-constellation turns the sky into premium orbital real estate … and that’s before the neighbors show up with their own fleets. Welcome to the era of broadband traffic … in space.
Despite these barriers, incremental paths forward exist. Demonstration satellites in the late 2020s, followed by regional commercial deployments in the early 2030s, could provide real-world validation. The phased evolution of spectrum use, dual-use handsets, and AI-assisted beam management may mitigate some of the scaling concerns. Regulatory alignment may emerge as rural and unserved regions increasingly depend on space-based access. Ultimately, the achievement of the 2045 architecture relies not only on engineering but also on sustained cross-industry coordination, geopolitical alignment, and commercial viability on a planetary scale. As of 2025, the probability of realizing the complete vision by 2045, in terms of indoor-grade, direct-to-device service via a fully orbital mobile core, is perhaps 40–50%, with a higher probability (~70%) for achieving outdoor-grade or partially integrated hybrid services. The coming decade will reveal whether the industry can fully solve the unique combination of thermal, RF, computational, regulatory, and manufacturing challenges required to replace the terrestrial mobile network with orbital infrastructure.
POSTSCRIPT – THE ECONOMICS.
The Direct-to-Cellular satellite architecture described in this article would reshape not only the technical landscape of mobile communications but also its economic foundation. The very premise of delivering mobile broadband directly from space, bypassing terrestrial towers, fiber backhaul, and urban permitting, undermines one of the most entrenched capital systems of the 20th and early 21st centuries: the mobile infrastructure economy. Once considered irreplaceable, the sprawling ecosystem of rooftop leases, steel towers, field operations, base stations, and fiber rings has been gradually rendered obsolete by a network that floats above geography.
The financial implications of such a shift are enormous. Before such an orbital transition described in this article, the global mobile industry invested well over 300 billion USD annually in network CapEx and Opex, with a large share dedicated to the site infrastructure layer, construction, leasing, energy, security, and upkeep of millions of base stations and their associated land or rooftop assets. Tower companies alone have become multi-billion-dollar REITs (i.e., Real Estate Investment Trusts), profiting from site tenancy and long-term operating contracts. As of the mid-2020s, the global value tied up in the telecom industry’s physical infrastructure is estimated to exceed 2.5 to 3 trillion USD, with tower companies like Cellnex and American Tower collectively managing hundreds of billions of dollars in infrastructure assets. An estimated $300–500 billion USD invested in mobile infrastructure represents approximately 0.75% to 1.5% of total global pension assets and accounts for 15% to 30% of pension fund infrastructure investments. This real estate-based infrastructure model defined mobile economics for decades and has generally been regarded as a reasonably safe haven for investors. In contrast, the 2045 D2C model front-loads its capital burden into satellite manufacturing, launch, and orbital operations. Rather than being geographically bound, capital is concentrated into a fleet of orbital base stations, each capable of dynamically serving users across vast and shifting geographies. This not only eliminates the need for millions of distributed cell sites, but it also breaks the historical tie between infrastructure deployment and national geography. Coverage no longer scales with trenching crews or urban permitting delays but with orbital plane density and beamforming algorithms.
Yet, such a shift does not necessarily mean lower cost, only different economics. Launching and operating tens of thousands of advanced satellites, each capable of supporting thousands of beams and running onboard compute environments, still requires massive capital outlay and ongoing expenditures in space traffic management, spectrum coordination, ground gateways, and constellation replenishment. The difference lies in utilization and marginal reach. Where terrestrial infrastructure often struggles to achieve ROI in rural or low-income markets, orbital systems serve these zones as part of the same beam budget, with no new towers or trenches required.
Importantly, the 2045 model would likely collapse the mobile value chain. Instead of a multi-layered system of operators, tower owners, fiber wholesalers, and regional contractors, a vertically integrated satellite operator can now deliver the full stack of mobile service from orbit, owning the user relationship end-to-end. This disintermediation has significant implications for revenue distribution and regulatory control, and challenges legacy operators to either adapt or exit.
The scale of economic disruption mirrors the scale of technical ambition. This transformation could rewrite the very economics of connectivity. While the promise of seamless global coverage, zero tower density, and instant-on mobility is compelling, it may also signal the end of mobile telecom as a land-based utility.
If this little science fiction story comes true, and there are many good and bad reasons to doubt it, Telcos may not Ascend to the Sky, but take the Stairway to Heaven.
Graveyard of the Tower Titans. This symbolic illustration captures the end of an era, depicting headstones for legacy telecom giants such as American Tower, Crown Castle, and SBA Communications, as well as the broader REIT (Real Estate Investment Trust) infrastructure model that once underpinned the terrestrial mobile network economy. It serves as a metaphor for the systemic shift brought on by Direct-to-Cellular (D2C) satellite networks. What’s fading is not only the mobile tower itself, but also the vast ancillary industry that has grown around it, including power systems, access rights, fiber-infrastructure, maintenance firms, and leasing intermediaries, as well as the telecom business model that relied on physical, ground-based infrastructure. As the skies take over the signal path, the economic pillars of the old telecom world may no longer stand.
I would like to acknowledge my wife, Eva Varadi, for her unwavering support, patience, and understanding throughout the creative process of writing this article.
Over the last three years, I have extensively covered the details of the Western European telecom sector’s capital expense levels and the drivers behind telecom companies’ capital investments. These accounts can be found in “The Nature of Telecom Capex—a 2023 Update” from 2023 and my initial article from 2022. This new version of “The Nature of Telecom Capex – a 2024 Update” is also different compared to the issues of 2022 and 2023 in that it focuses on the near future Capex demands from 2024 to 2030 and what we may expect from our Industry capital spending over the next 7 years.
For Western Europe, Capex levels in 2023 were lower than in 2022, a relatively rare but not unique occurrence that led many industry analysts to conclude the “End of Capex” and that from now on, “Capex will surely decline.” The compelling and logical explanations were also evident, pointing out that “data traffic (growth) is in decline”, “overproduction of bandwidth”, “5G is not what it was heralded to be”, “No interest in 6G”, “Capital is too expensive” and so forth. These “End to Capex” conclusions were often made on either aggregated data or selected data, depending on the availability of data.
Having worked on Capex planning and budgeting since the early 2000s for one of the biggest telecom companies in Europe, Deutsche Telecom AG, building what has been described as best-practice Capex models, my outlook is slightly less “optimistic” about the decline and “End” of Capex spending by the Industry. Indeed, for those expecting that a Telco’s capital planning is only impacted by hyper-rational insights glued to real-world tangibles and driven by clear strategic business objectives, I beg you to modify that belief somewhat.
Figure 1 illustrates the actual telecom Capex development for Western Europe between 2017 and 2023, with projected growth from 2024 (with the first two quarters’ actual Capex levels) to 2026, represented by the orange-colored dashed lines. The light dashed line illustrates the annual baseline Capex level before 5G and fiber deployment acceleration. The light solid line shows the corresponding Telco Capex to Revenue development, including an assessment for 2024 to 2026, with an annual increase of ca. 500 million euros. Source:New Street Research European Quarterly Review, covering 15 Western European countries (see references at the end of the blog) and 56+ telcos from 2017 to 2024, with 2024 covering the year’s first two quarters.
Western Europe’s telecommunications Capex fell between 2022 and 2023 for the first time in some years, from the peak of 51 billion euros in 2022. The overall development from 2017 to 2023 is illustrated below, including a projected Capex development covering 2024 to 2026 using each Telco’s revenue projections as a simple driver for the expected Capex level (i.e., inherently assuming that the planned Capex level is correlated to the anticipated, or targeted, revenue of the subsequent year).
The reduction in Capex between 2022 and 2023 comes from 29 out of 56 Telcos reducing their Capex level in 2023 compared to 2022. In 8 out of 15 countries, the Telco Capex levels were decreased by ca. 2.3 billion euros compared to their 2022 Capex levels. Likewise, 7 countries spent approximately 650 million euros more than their 2022 levels together. If we compared the 1st and 2nd half of 2023 with 2022, there was an unprecedented Capex reduction in the 2nd half of 2023 compared to any other year from 2017 to 2023. It really gives the impression that many ( at least 36 out of 56) Telcos put their feet on the break in 2023. 29 Telcos out of the 36 broke their spending in the last half of 2023 and ended the year with an overall lower spending than in 2022. Of the 8 countries with a lower Capex spend in 2023, the UK, France, Italy, and Spain make up more than 80%. Of the countries with a higher Capex in 2023, Germany, Netherlands, Belgium, and Austria make up more than 80%.
For a few of the countries with lower Capex levels in 2023, one could argue that they more or less finished their 5G rollout and have so high fiber-to-the-home penetration levels that more fiber is on account of overbuilt and of a substantially smaller scale than in the past (e.g., France, Norway, Spain, Portugal, Denmark, and Sweden). For other countries with a lower investment level than the previous year, such as the UK, Italy, and Greece, 2022 and 2023 saw substantial consolidation activity in the markets (e.g., Vodafone UK & C.K. Hutchinson 3, Wind Hellas rounded up in Nova Greece, …). In fact, Spain (e.g., Masmovil), Norway (e.g., Ice Group), and Denmark (e.g., Telia DK) also experienced consolidation activities that will generally lower companies’ spending levels initially. One would expect, as to some extent visible in the first half of 2024, that countries that spend less due to consolidation activities would increase their Capex levels in the next two to three years after an initial replanning period.
WESTERN EUROPE – THE BIG CAPEX OVERVIEW.
Figure 2 Shows on a country-level the 5-year average Capex spend (over the period 2019 to 2023) and the Capex in 2023. Source:New Street Research European Quarterly Review 2017 to 2024 (Q2).
When attempting to understand Telco Capex, or any Capex with a “built-in” cyclicity, one really should look at more than one or two years. Figure 2 above provides the comparison with the average Capex spend over the period 2019 to 2023 and the Capex spend in 2023. The five year Capex average captures the initial stages of 5G deployment in Europe, 5G deployment in general, COVID capacity investments (in fixed networks), the acceleration of Fiber rollout in many countries in Europe (e.g., Germany, UK, Netherlands, …), the financial (inflationary) crisis of increasing costly capital, and so forth. In my opinion 2023 is a reflection of the 2021-2022 financial crisis and that most of the 5G has been deployed to cover current market needs. As we have seen before, Telco investments are often 12 to 18 month out of synch with financial crisis years, and thus it is from that perspective also not surprising that 2023 might be a lower Capex year than in the past. Although, as is also evident from Figure 2, only 5 countries had a lower Capex level in 2023 than the previous 5 years average level.
Figure 3 Illustrates the Capex development over the last 5 years from 2019 to 2023 with the color Green showing years where the subsequent year had a higher Capex level, and color Red that the subsequent year had a lower Capex level. From a Western Europe perspective only 2023 had a lower Capex level than the previous year (compared to the last 5 years). Source:New Street Research European Quarterly Review 2017 to 2024 (Q2).
Using Capex to Revenue ratios of the Telco industry are prone to some uncertainty. This is particular the case when individual Telcos are compared. In general, I recommend to make comparisons over a given period of time, like 3 or 5 year periods, as it averages out some of the natural variation between Telcos and countries (e.g., one country or Telco may have started its 5G deployment earlier than others). Even that approach has to be taken with some caution as some Telcos may fully incur Capex for fiber deployments and others may make wholesale agreements with open Fiberco’s (for example) and only incur last-mile access or connection Capex. Although, of smaller relative Capex scale nowadays, Telcos increasingly have Towercos managing and building their passive infrastructure for their cell site demand. Some may still fully build their own cell sites, incurring proportionally higher Capex per new site deployed, which of course may lead to structural Capex differences between such Telcos. Having these cautionary remarks in mind, I believe that Capex to Revenue ratios does provide a means of comparing Countries or Telcos and it does give provide a picture of the capital investment intensity compared to the market performance. A country comparison of the 5-year (period: 2019 to 2023) average Capex to Revenue ratio is illustrated in Figure 3 below for the 15 markets considered in this blog.
Figure 4 Shows on a country-level the 5-year average Capex to Revenue ratios over the period 2019 to 2023. Source:New Street Research European Quarterly Review 2017 to 2024 (Q2).
Comparing Capex per capita and Capex as a percentage of GDP may offer insights into how capital investments are prioritized in relation to population size and economic output. These two metrics could highlight different aspects of investment strategies, providing a more comprehensive understanding of national economic priorities and critical infrastructure development levels. Such a comparison is show in Figure 15 below.
Capex per capita, shown in Figure 5 left hand side, measures the average amount of investment allocated to each person within a country. This metric is particularly useful for understanding the intensity of investment relative to the population, indicating how much infrastructure, technology, or other capital resources are being made available on a per-person basis. A higher Capex per capita suggests significant investment in areas like public services, infrastructure, or economic development, which could improve quality of life or boost productivity. Comparing this measure across countries helps identify disparities in investment levels, revealing which nations are placing greater emphasis on infrastructure development or economic expansion. For example, a country with a high Capex per capita likely prioritizes public goods such as transportation, energy, or digital infrastructure, potentially leading to better economic outcomes and higher living standards over time. The 5-year average Capex level does show a strong positive linear relationship with the Country population (R² = 0.9318, chart not shown), suggesting that ca. 93% of the variation in Capex can be explained by the variation in population. The trend implies that as the population increases, Capex also tends to increase, likely reflecting higher investment needs to accommodate larger populations. It should be noted that that a countries surface area is not a significant factor influencing Capex. While some countries with larger land areas might exhibit a higher Capex level, the overall trend is not strong.
Capex as a percentage of GDP, shown in Figure 5 right hand side, measures the proportion of a country’s economic output devoted to capital investment. This ratio provides context for understanding investment levels relative to the size of the economy, showing how much emphasis is placed on growth and development. A higher Capex-to-GDP ratio can indicate an aggressive investment strategy, commonly seen in developing economies or countries undergoing significant infrastructure expansion. Conversely, a lower ratio might suggest efficient capital allocation or, in some cases, underinvestment that could constrain future economic growth. This metric helps assess the sustainability of investment levels and reflects economic priorities. For instance, a high Capex-to-GDP ratio in a developed country could indicate a focus on upgrading existing infrastructure, whereas in a developing economy, it may signify efforts to close infrastructure gaps, modernization efforts (e.g., optical fiber replacing copper infrastructure per fixed broadband transformation) and accelerating growth. The 5-year average Capex level does show a strong positive linear relationship with the Country GDP (R² = 0.9389, chart not shown), suggesting that ca. 94% of the variation in Capex can be explained by the variation in the country GDP. While a few data points show some deviation from this trend, the overall fit is very strong, reinforcing the notion that larger economies generally allocate more resources to capital investments.
The insights gained from both Capex per capita and Capex as a percentage of GDP are complementary, providing a fuller picture of a country’s investment strategy. While Capex per capita reflects individual investment levels, Capex as a percentage of GDP reveals the scale of investment in relation to the overall economy. For example, a country with high Capex per capita but a low Capex-to-GDP ratio (e.g., Denmark, Norway, …) may have a wealthy population where individual investment levels are significant, but the size of the economy is such that these investments constitute a relatively small portion of total economic activity. Conversely, a country with a high Capex-to-GDP ratio but low Capex per capita (e.g., Greece) may be dedicating a substantial portion of its economic resources to infrastructure in an effort to drive growth, even if the per-person investment remains modest.
Figure 5 Illustrates two charts that compare the average capital expenditures over a 5-year period from 2019 to 2023. The left chart shows Capex per capita in euros, with Switzerland leading at 230 euros, while Spain has the lowest at 75 euros. The right chart depicts Capex as a percentage of GDP, where Greece tops the list at 0.47%, and Sweden is at the bottom with 0.16%. These metrics provide insights into how different countries allocate investments relative to their population size and economic output, revealing varying levels of investment intensity and economic priorities. It should be noted that Capex levels are strongly correlated with both the size of the population and the size of the economy as measured by the GDP. Source:New Street Research European Quarterly Review 2017 to 2024 (Q2).
FORWARD TO THE PAST.
Almost 15 years ago, I gave a presentation at the “4G World China” conference in Beijing titled “Economics of 4G Introduction in Growth Markets”. The idea was that a mobile operator’s capital demand would cycle between 8% (minimum) and 13% (maximum), usually with one replacement cycle before migrating to the next-generation radio access technology. This insight was backed up by best-practice capital demand models considering market strategy and growth Capex drivers. It involved also involved the insights of many expert discussions.
Figure 6 illustrates my expectations of how Capex would relate before, during, and after LTE deployment in Western Europe. Source:“Economics of 4G Introduction in Growth Markets” at “4G World China”, 2011.
For the careful observer, you will see that I expected, back in 2011, the typical Capex maintenance cycle in Western European markets between infrastructure and technology modernization periods to be no more than 8% and that Capex in the maintenance years would be 30% lower than required in the peak periods. I have yet to see a mobile operation with such a low capital intensity unless they effectively share their radio access network and/or by cost-structure “magic” (i.e., cost transformation), move typical mobile Capex items to Opex (by sourcing or optimizing the cost structure between fixed and mobile business units).
I retrospectively underestimated the industry’s willingness to continue increasing capital investments in existing networks, often ignoring the obvious optimization possibilities between their fixed and mobile broadband networks (due to organizational politics) and, of course, what has and still is a major industrial contagious infliction: “Metus Crescendi Exponentialis” (i.e., the fear of the exponential growth aka the opportunity to spend increasingly lots of Capex). From 2000 to today, the Western European Capex to Revenue ratio has been approximately between 11% and 21%, although it has been growing since around 2012 (see details in “The Nature of Telecom Capex—a 2023 Update”).
CAPEX DEVELOPMENT FROM 2024 TO 2026.
From the above Figure 1, it should be no surprise that I do not expect Capex to continue to decline substantially over the next couple of years, as we saw between 2022 and 2023. In fact, I anticipate that 2024 will be around the level of 2023, after which we will experience modest annual increases of 600 to 700 million euros. Countries with high 5G and Fiber-to-the-Home (FTTH) coverage (e.g., France, Netherlands, Norway, Spain, Portugal, Denmark, and Sweden) will keep their Capex levels possible with some modest declines with single-digit percentage points. Countries such as Germany, the UK, Austria, Belgium, and Greece are still European laggards in terms of FTTH coverage, being far below the 80+% of other Western European countries such as France, Spain, Portugal, Netherlands, Denmark, Sweden, and Norway. Such countries may be expected to continue to increase their Capex as they close the FTTH coverage gap. Here, it is worth remembering that several fiber acquisition strategies aiming at connecting homes with fiber result in a lower Capex than if a Telco aims to build all the required fiber infrastructure.
Consolidation Capex.
Telecom companies tend to scale back Capex during consolidation due to uncertainty, the desire to avoid redundancy, and the need to preserve cash. However, after regulatory approval and the deal’s closing, Capex typically rises as the company embarks on network integration, system migration, and infrastructure upgrades necessary to realize the merger’s benefits. This post-merger increase in Capex is crucial for achieving operational synergies, enhancing network performance, and maintaining a competitive edge in the telecom market.
If we look at the period 2021 to 2024, we have had the following consolidation and acquisition examples:
UK: In May 2021, Virgin Media and the O2 (Telefonica) UK merger was approved. They announced the intention to consolidate on May 7th, 2020.
UK: Vodafone UK and Three UK announced their intention to merge in June 2023. The final decision is expected by the end of 2024.
Spain: Orange and MasMovil announced their intent to consolidate in July 2023. Merger approval was given in February 2024. Conditions were imposed on the deal for MasMovil to divestitures its frequency spectrum.
Italy: The potential merger between Telecom Italia (TIM) and Open Fiber was first discussed in 2020 when the idea emerged to create a national fiber network in Italy by merging TIM’s fixed access unit, FiberCop, with Open Fiber. a Memorandum of Understanding was signed in May 2022.
Greece: Wind Hellas acquisition by United Group (Nova) was announced in August 2021 and finalized in January 2022 (with EU approval in December 2021).
Denmark: Norlys’s acquisition of Telia Denmark was first announced on April 25, 2023, and approved by the Danish competition authority in February 2024.
Thus, we should also expect that the bigger in-market consolidations may, in the short term (next 2+ years), lead to increased Capex spending during the consolidation phase, after which Capex (& Opex) synergies hopefully kick in. Typically, 2 budgetary cycles minimum before this would be expected to be observed. Consolidation Capex usually amounts to a couple of percentage points of total consolidated revenue, with some other bigger items being postponed to the tail end of a consolidation unless it is synergetic with the required integration.
The High-risk Suppler Challenge to Western Europe’s Telcos.
When assessing whether Capex will increase or decrease over the next few years (e.g., up to 2030), we cannot ignore the substantial Capex amounts associated with replacing high-risk suppliers (e.g., Huawei, ZTE) from Western European telecom networks. Today, the impact is mainly on mobile critical infrastructure, which is “limited” to core networks and 5G radio access networks (although some EU member states may have extended the reach beyond purely 5G). Particularly if (or when?) the current European Commission’s 5G Toolbox (legal) Framework (i.e., “The EU Toolbox for 5G Security”) is extended to all broadband network infrastructure (e.g., optical and IP transport network infrastructure, non-mobile backend networking & IT systems) and possibly beyond to also address Optical Network Terminal (ONT) and Customer Premise Equipment (note: ONT’s can be integrated in the CPE or alternatively separated from the CPE but installed at the customers premise). To an extent, it is thought-provoking that the EU emphasis has only been on 5G-associated critical infrastructure rather than the vast and ongoing investment of fiber-optical, next-generation fixed broadband networks across all European Union member states (and beyond). In particular, this may appear puzzling when the European Union has subsidized these new fiber-optical networks by up to 50%. Considering that the fixed-broadband traffic is 8 to 10 times that of the mobile traffic, and all mobile (and wireless) traffic passes through the fixed broadband network and associated local as well as global internet critical infrastructure.
As far back as 2013, the European Parliament raised some concerns about the degree of involvement (market share) of Chinese companies in the EU’s telecommunications sector. It should be remembered that in 2013, Europe’s sentiment was generally positive and optimistic toward collaboration with China, as evidenced by the European Commission’s report “EU-China 2020 Strategic Agenda for Cooperation” (2013). Historically, the development of the EU’s 5G Toolbox for Security was the result of a series of events from about 2008 (after the financial crisis) to 2019 (and to today), characterized by growing awareness in Europe of China’s strategic ambitions, the expansion of the BRI (Belt and Road Initiative, 2013), DSR (Digital Silk Road, an important part of BRI 2.0, 2015), and China’s National Intelligence Law (2017) requiring Chinese companies to cooperate with the Chinese Government on intelligence matters, as well as several high-profile cybersecurity incidents (e.g., APT, Operation Cloud Hopper, …), and increased scrutiny of Chinese technology providers and their influence on critical communications infrastructure across pretty much the whole of Europe. These factors collectively drove the EU to adopt a more cautious and coordinated approach to addressing security risks in the context of 5G and beyond.
Figure 7 illustrates Western society, including Western Europe, ‘s concern about Chinese technology presence in its digital infrastructure. A substantial “hidden” capital expense (security debt) is tied to Western Telco’s telecom infrastructures, mobile and fixed.
The European Commission’s 2023 second report on the implementation of the EU 5G cybersecurity toolbox offers an in-depth examination of the risks posed by high-risk suppliers, focusing on Chinese-origin infrastructure, such as equipment from Huawei and ZTE. The report outlines the various stages of implementation across EU Member States and provides recommendations on how to mitigate risks associated with Chinese infrastructure. It considers 5G and fixed broadband networks, including Customer Premise Equipment (CPE) devices like modems and routers placed at customer sites.
The EU Commission defines a high-risk supplier in the context of 5G cybersecurity based on several objective criteria to reduce security threats in telecom networks. A supplier may be classified as high-risk if it originates from a non-EU country with strong governmental ties or interference, particularly if its legal and political systems lack democratic safeguards, security protections, or data protection agreements with the EU. Suppliers susceptible to governmental control in such countries pose a higher risk.
A supplier’s ability to maintain a reliable and uninterrupted supply chain is also critical. A supplier may be considered high-risk if it is deemed vulnerable in delivering essential telecom components or ensuring consistent service. Corporate governance is another important aspect. Suppliers with opaque ownership structures or unclear separation from state influence are more likely to be classified as high-risk due to the increased potential for external control or lack of transparency.
A supplier’s cybersecurity practices also play a significant role. If the quality of the supplier’s products and its ability to implement security measures across operations are considered inadequate, this may raise concerns. In some cases, country-specific factors, such as intelligence assessments from national security agencies or evidence of offensive cyber capabilities, might heighten the risk associated with a particular supplier.
Furthermore, suppliers linked to criminal activities or intelligence-gathering operations undermining the EU’s security interests may also be considered high-risk.
To summarize what may make a telecom supplier a high-risk supplier:
Of non-EU origin.
Strong governmental ties.
The country of origin lacks democratic safeguards.
The country of origin lacks security protection or data protection agreements with the EU.
Associated supply chain risks of interruption.
Opaque ownership structure.
Unclear separation from state influence.
Ability to independently implement security measures shielding infrastructure from interference (e.g., sabotage, espionage, …).
These criteria are applied to ensure that telecom operators, and eventually any business with critical infrastructure, become independent of a single supplier, especially those that pose a higher risk to the security and stability of critical infrastructure.
Figure 8 above summarizes the current European legislative framework addressing high-risk suppliers in critical infrastructure, with an initial focus on 5G infrastructure and networks.
Regarding 5G infrastructure, the EU report reiterates the urgency for EU Member States to immediately implement restrictions on high-risk suppliers. The EU policy highlights the risks of state interference and cybersecurity vulnerabilities posed by the close ties between Chinese companies like Huawei and ZTE and the Chinese government. Following groundwork dating back to the 2008s EU Directive on Critical Infrastructure Protection (EPCIP), The EU’s Digital Single Market Strategy (2015), the (first) Network and Information Security (NIS) directive (2016), and early European concern about 5G societal impact and exposure to cybersecurity (2015 – 2017), the EU toolbox published in January 2020 is designed to address these risks by urging Member States to adopt a coordinated approach. As of 2023, a second EU report was published on the member state’s progress in implementing the EU Toolbox for 5G Cybersecurity. While many Member States have established legal frameworks that give national authorities the power to assess supplier risks, only 10 have fully imposed restrictions on high-risk suppliers in their 5G networks. The report criticizes the slow pace of action in some countries, which increases the EU’s collective exposure to security threats.
Germany, having one of the largest, in absolute numbers, Chinese RAN deployments in Western Europe, has been singled out for its apparent reluctance to address the high-risk supplier challenge in the last couple of years (see also notes in “Further Readings” at the back of this blog). Germany introduced its regulation on Chinese high-risk suppliers in July 2024 with a combination of their Telekommunikationsgesetz (TKG) and IT-Sicherheitsgesetz 2.0. The German government announced that starting in 2026, it will ban critical components from Huawei and ZTE in its 5G networks due to national security concerns. This decision aligns Germany with other European countries working to limit reliance on high-risk suppliers. Germany has been slower in implementing such measures than others in the EU, but the regulation marks a significant step towards strengthening its telecom infrastructure security. Light Reading has estimated that a German Huawei ban would cost €2.5B and take years for German telcos. This estimate seems very optimistic and certainly would require very substantial discounts from the supplier that would be chosen to replace, for example, their Huawei installations with, e.g., for Telekom Deutschland that would be ca. 50+% of their ca. 38+ thousand sites, and it is difficult for me to believe that that kind of economy would apply to all telcos in Western Europe with high-risk suppliers. I also believe it ignores de-commissioning costs and changes to the backend O&M systems. I expect telco operators will try to push the timeline for replacement until most of their high-risk supplier infrastructure is written off and ripe for modernization, which for Germany would most likely happen after 2026. One way or another, we should expect an increase in mobile Capex spending towards the end of the decade as the German operators are swapping out their Chinese RAN suppliers (which may only be a small part of their Capital spend if the ban is extended beyond 5G).
The European Commission recommends that restrictions cover critical and highly sensitive assets, such as the Radio Access Network (RAN) and core network functions, and urges member states to define transition periods to phase out existing equipment from high-risk suppliers. The transition periods, however, must be short enough to avoid prolonging dependency on these suppliers. Notably, the report calls for an immediate halt to installing new equipment from high-risk vendors, ensuring that ongoing deployment does not undermine EU security.
When it comes to fixed broadband services, the report extends its concerns beyond 5G. It stresses that many Member States are also taking steps to ensure that the fixed network infrastructure is not reliant on high-risk suppliers. Fourteen (14) member states have either implemented or plan to restrict Chinese-origin equipment in their fixed networks. Furthermore, nine (9) countries have adopted technology-neutral legislation, meaning the restrictions apply across all types of networks, not just 5G. This implies that Chinese-origin infrastructure, including transport network components, will eventually face the same scrutiny and restrictions as 5G networks. While the report does not explicitly call for a total ban on all Chinese-origin equipment, it stresses the need for detailed assessments of supplier risks and restrictions where necessary based on these assessments.
While the EU’s “5G Security Toolbox” focuses on 5G networks, Denmark’s approach, the “Danish Investment Screening Act,” which took effect on the 1st of July 2021, goes much further by addressing the security of fixed broadband, 4G, and transport networks. This broad regulatory focus helps Denmark ensure the security of its entire communications ecosystem, recognizing that vulnerabilities in older or supporting networks could still pose serious risks. A clear example of Denmark’s comprehensive approach to telecommunications security beyond 5G is when the Danish Center for Cybersikkerhed (CFCS) required TDC Net to remove Chinese DWDM equipment from its optical transport network. TDC Net claimed that the consequence of the CFCS requirement would result in substantial costs to TDC Net that they had not considered in their budgets. CFCS has regulatory and legal authority within Denmark, particularly in relation to national cybersecurity. CFCS is part of the Danish Defense Intelligence Service, which places it under the Ministry of Defense. Denmark’s regulatory framework is not only one of the sharpest implementations of the EU’s 5G Toolkit but also one of the most extensive in protecting its national telecom infrastructure across multiple layers and generations of technology. The Danish approach could be a strong candidate to serve as a blueprint for expanded EU regulation beyond 5G high-risk suppliers and thus become applicable to fixed broadband and transport networks, resulting in substantial additional Capex towards the end of the decade.
While not singled out as a unique risk category, customer premises equipment (CPE) from high-risk suppliers is mentioned in the context of broader network security measures. Some Member States have indicated plans to ensure that CPE is subject to strict procurement standards, potentially using EU-wide certification schemes to vet the security of such devices. CPE may be included in future security measures if it presents a significant risk to the network. Many CPEs have been integrated with the optical network terminal, or ONT, which is architecturally a part of the fixed broadband infrastructure, serving as a demarcation point between the fiber optic network and the customer’s internal network. Thus, ONT is highly likely to be considered and included in any high-risk supplier limitations that may come soon. Any CPE replacement program would likely be associated on its own with considerable Capex and cost for operators and their customers in general. The CPE quantum for the European Union (including the UK, cheeky, I know) is between 200 and 250 million CPEs, including various types of CPE devices, such as routers, modems, ONTs, and other network equipment deployed for residential and commercial users. It is estimated that 30% to 40% of these CPEs may be linked to high-risk Chinese suppliers. The financial impact of a systematic CPE replacement program in the EU (including the UK) could be between 5 to 8 billion euros in capital expenses, ignoring the huge operational costs of executing such a replacement program.
The Data Growth Slow Down – An Opportunities for Lower Capex?
How do we identify whether a growth dynamics, such as data growth, is exponential or self-limiting?
Exponential growth dynamics have the same (percentage) growth rate indefinitely. Self-limiting growth dynamics, or s-curve behavior, will have a declining growth rate. Natural systems are generally self-limiting, although they might exhibit exponential growth over a short term, typically in the initial growth phase. So, if you are in doubt (which you should not be), calculate the growth rate of your growth dynamics from the beginning until now. If that growth rate is constant (over several time intervals), your dynamics are exponential in nature (at least over the period you looked at); if not … well, your growth process is most likely self-limiting.
Telco Capex increases, and Telco Capex decreases. Capex is, in nature, cyclic, although increasing over time. Most European markets will have access to 550 to 650 MHz downlink spectrum depending on SDL deployment levels below 4 GHz. Assuming 4 (1) Mbps per DL (UL) MHz per sector effective spectral efficiency, 10 traffic hours per day, and ca. 350 to 400 thousand mobile sites (3 sectors each) across Western Europe, the carrying mobile capacity in Bytes is in the order of 140 Exa Bytes (EB) per Month (note: if I had chosen 2 and 0.5 Mbps per MHz per sector, carrying capacity would be ca. 70 EB/Month). It is clear that this carrying capacity limit will continue to increase with software releases, innovation, advanced antenna deployment with higher order MiMo, and migration from older radio access technologies to the newest (increasing the effective spectral efficiency).
According to Ericsson Mobility Visualizer, Western Europe saw a mobile data demand per month of 11 EB in 2023 (see Figure below). The demand for mobile data in 2023 was almost 10 times lower than the (conservatively) estimated carrying capacity of the underlying mobile networks.
Figure 9 illustrates the actual demanded data volume in EB per month. I have often observed that when planners estimate their budgetary demand for capacity expansions, they use the current YoY growth rate and apply it to the future (assuming their growth dynamics are geometrical). I call this the “Naive Expectations” assumption (fallacy) that obviously leads to the overprovision of network capacity and less efficient use of Capex, as opposed to the “Informed Expectations” approach based on the more realistic S-Curve dynamic growth dynamics. I have rarely seen the “Naive Expectations” fallacy challenged by CFOs or non-technical leadership responsible for the Telco budgets and economic health. Although not a transparent approach, it is a “great” way to add a “bit” of Capex cushion for other Capex uncertainties.
It should be noted that the Ericsson data treats traffic generated by fixed wireless access (FWA) separately (which, by the way, makes sense). Thus, the 11 EB for 2023 does not include FWA traffic. Ericsson only has a global forecast for FWA traffic starting from 2023 (note: it is not clear whether 2023 is actual FWA traffic or estimated). To get an impression of the long-term impact of FWA traffic, we can apply the same S-curve approach as the one used for mobile data traffic above, according to what I call the “Informed expectations” approach. Even with the FWA traffic, it is difficult to see a situation that, on average (at least), would pose any challenge to existing mobile networks. Particularly, the carrying capacity can easily be increased by deploying more advanced antennas (e.g., higher order MiMo), and, in general, it is expected to improve with each new software release forthcoming.
Figure 10 above uses Ericsson’s Mobile Visualizer data for Western Europe’s mobile and fixed wireless access (FWA) traffic. It gives us an idea of the total traffic expectations if the current usage dynamics continue. Ericsson only provides a global FWA forecast from 2023 to 2029. I have assumed WEU takes its proportional mobile share of the FWA traffic. Note: For the period up to and including 2023, it seems a bit rich in its FWA expectations, imo.
So, by all means, the latest and greatest mobile networks are, without much doubt, in most places, over-dimensioned from the perspective of their carrying bytes potential, the volumetric capacity, and what is demanded in terms of data volume. They also appear to remain so for a very long time unless the current demand dynamics fundamentally change (which is, of course, always a possibility, as we have seen historically).
However, that our customers get their volumetric demand satisfied is generally a reflection of the quality in terms of bits per second (a much more fundamental unit than volume) satisfied. Thus, the throughput, or speed, should be good enough for the customer to unhindered enjoy their consumption, which, as a consequence, generates the Bytes that most Telco executives have told themselves they understand and like to base their pricing on (and I would argue judging by my experience outside Europe more often than not maybe really don’t get). It is not uncommon that operators with complex volumetric pricing become more obsessed with data volume rather than optimum quality (that might, in fact, generate even more volume). The figure below is a snapshot from August 2024 of the median speeds customers enjoy in mobile as well as fixed broadband networks in Western Europe. In most cases in Europe, customers today enjoy substantially faster fixed-broadband services than they would get in mobile networks. One should expect that this would change how Telcos (at least integrated Telcos) would design and plan their mobile networks and, consequently, maybe dramatically reduce the amount of Mobile Capex we spend. There is little evidence that this is happening yet. However, I do anticipate, most likely naively, that the Telco industry would revise how mobile networks are architected, designed, and built with 6G.
Figure 11 shows that apart from one Western European country (Greece, also a fixed broadband laggard), all other markets have superior fixed broadband downlink speeds compared to what mobile networks can deliver. Note that the speed measurement data is based on the median statistic. Source:Speedtest Global Index, August 2024.
A Crisis of Too Much of a “Good” Thing?
Analysys Mason recently (July 2024) published a report titled “A Crisis of Overproduction in Bandwidth Means that Telecoms Capex Will Inevitably Fall.” The report explores the evolving dynamics of capital expenditure (Capex) in the telecom industry, highlighting that the industry is facing a turning point. The report argues that the telecom sector has reached a phase of bandwidth overproduction, where the infrastructure built to deliver data has far exceeded demand, leading to a natural decline in Capex over the coming years.
According to the Analysys Mason report, global Capex in the telecom sector has already peaked, with two significant investment surges behind it: the rollout of 5G networks in mobile infrastructure and substantial investments in fiber-to-the-premises (FTTP) networks. Both of these infrastructure developments were seen as essential for future-proofing networks, but now that the peaks in these investments have passed, Capex is expected to fall. The report predicts that by 2030, the Capex intensity (the proportion of revenue spent on capital investments) will drop from around 20% to 12%. This reduction is due to the shift from building new infrastructure to optimizing and maintaining existing networks.
The main messages that I take away from the Analysys Mason report are the following:
Overproduction of bandwidth: Telecom operators have invested heavily in building their networks. However, demand for data and bandwidth is no longer growing at the exponential rates seen in previous years.
Shifting Capex Trends: The telecom industry is experiencing two peaks: one in mobile spending due to the initial 5G coverage rollout and another in fixed broadband due to fiber deployments. Now that these peaks have passed, Capex is expected to decline.
Impact of lower data growth: The stagnation in mobile and fixed data demand, combined with the overproduction of mobile and fixed bandwidth, makes further large-scale investment in network expansion unnecessary.
My take on Analysys Mason’s conclusions is that with the cyclic nature of Telco investments, it is natural to expect that Capex will go up and down. That Capex will cycle between 20% (peak deployment phase) and 12% (maintenance phase) seems very agreeable. However, I would expect that the maintenance level would continue to increase as time goes by unless we fundamentally change how we approach mobile investments.
That network capacity is built up at the beginning of a new technology cycle (e.g., 5G NR, GPON, XGPON, XSGPON-based FTTH), it is also not surprising that the amount of available capacity will appear substantial. I would not call it a bandwidth overproduction crisis (although I agree that the overhead of provisioned carrying capacity compared to demand expectations seems historically high); it manifests the technologies we have developed and deployed today. For 5G NR real-world conditions, users could see peak DL speeds ranging from 200 Mbps to 1 Gbps with median 5G DL speeds of 100+ Mbps. The lower end of this range applies in areas with fewer available resources (e.g., less spectrum, fewer MIMO streams). In comparison, the higher end reflects better conditions, such as when a user is close to the cell tower with optimal signal conditions. The quality of fiber-connected households at current GPON and XGPON technology would be sustainable at 1 to 10 Gbps downstream to the in-home ONT/CPE. However, the in-home quality experienced over WiFi would depend a lot on how the WiFi network has been deployed and how many concurrent users there are at any given time. As backhaul and backbone transmission solutions to mobile and fixed access will be modern and fiber-based, there is no reason to believe that user demand should be limited in any way (anytime soon), given a well-optimized, modern fiber-optic network should be able to reach up to 100 Tbps (e.g., 10 EB per month with 10 traffic hours per day).
Germany, the UK, Belgium, and a few smaller Western countries will continue their fiber deployment for some years to bring their fiber coverage up to the level of countries such as France, Spain, Portugal, and the Netherlands. It is difficult to believe that these countries would not continue to invest substantial money to raise their fiber coverage from their current low levels. Countries with less than 60% fiber-to-the-home coverage have a share of 50+ % of the overall Western European Capex level.
The fact that the Telco industry would eventually experience lower growth rates should not surprise anyone. That has been in the cards since growth began. The figure below takes actual mobile data from Ericsson’s Mobile Visualizer. It applies a simple S-curve growth model dynamics to those data that actually do a very good job of accounting for the behavior. A geometrical growth model (or exponential growth dynamics), while possibly accounting for the early stages of technology adaptation and the resulting data growth, is not a reasonable model to apply here and is not supported by the actual data.
Figure 12 provides the actual Exa Bytes (EB) monthly with a fitted S-Curve extrapolated beyond 2023. The S-Curve is described by the Data Demand Limit (Ls), Growth Rate (k), and the Inflection Year (T0), where growth transitions from acceleration to deceleration. Source:Ericsson Mobile Visualizer resource.
The growth dynamic, applied to the data we extract from the markets shown in the above Figure, indicates that in Western Europe and the CEE (Central Eastern Europe), the inflection point should be expected around 2025. This is the year when the growth rates begin to decline. In Western Europe (and CEE), we would expect the growth rate to become less than 10% by 2030, assuming that no fundamental changes to the growth dynamic occur. The inflection point for the North American markets (i.e., The USA and Canada) is around 2033; this is expected to happen a bit earlier (2030) for Asia. Based on the current growth dynamics, North America will experience growth rates below 10% by 2036. For Asia, this event is expected to take place around 2033. How could FWA traffic growth change these results? The overall behavior would not change. The inflection point may happen later, thus the onset of slower growth rates, and the time when we would expect a growth rate lower than 10% would be a couple of years after the inflection year.
Let us just for fun (usually the best reason) construct a counterfactual situation. Let us assume that data growth continues to follow geometric (exponential) growth indefinitely without reaching a saturation point or encountering any constraints (e.g., resource limits, user behavior limitations). The premise is that user demand for mobile and fixed-line data will continue to grow at a constant, accelerating rate. For mobile data growth, we use the 27% YoY growth of 2023 and use this growth rate for our geometrical growth model. Thus, every ca. 3 years, the demand would double.
If telecom data usage continued to grow geometrically, the implications would (obviously) be profound:
Exponential network demand: Operators would face exponentially increasing demand on their networks, requiring constant and massive investments in capacity to handle growing traffic. Once we reach the limits of the carrying capacity of the network, we have three years (with a CAGR of 27%) until demand has doubled. Obviously, any spectrum position would quickly become insufficient, resulting in massive investments in new infrastructure (sites in mobile and more fiber) would be needed. Capacity would become the growth limiting factor.
Costs: The capital expenditures (Capex) required to keep pace with geometric growth would skyrocket. Operators must continually upgrade or replace network equipment, expand physical infrastructure, and acquire additional spectrum to support the growing data loads. This would lead to unsustainable business models unless prices for services rose dramatically, making such growth scenarios unaffordable for consumers but long before that for the operators themselves.
Environmental and Physical Limits: The physical infrastructure necessary to support geometric growth (cell towers, fiber optic cables, data centers) would also have environmental consequences, such as increased energy consumption and carbon emissions. Additionally, telecom providers would face the law of diminishing returns as building out and maintaining these networks becomes less economically feasible over time.
Consumer Experience: The geometric growth model assumes that user behavior will continue to change dramatically. Consumers would need to find new ways to utilize vast amounts of bandwidth beyond streaming and current data-heavy applications. Continuous innovation in data-hungry applications would be necessary to keep up with the increased data usage.
The counterfactual argument shows that geometric growth, while useful for the early stages of data expansion, becomes unrealistic as it leads to unsustainable economic, physical, and environmental demands. The observed S-curve growth is more appropriate for describing mobile data demand because it accounts for saturation, the limits of user behavior, and the constraints of telecom infrastructure investment.
Back to Analysys Mason’s expected, and quite reasonable, consequence of the (progressively) lower data growth: large-scale investment would become unnecessary.
While the assertion is reasonable, as said, mobile obsolescence hits the industry every 5 to 7 years, regardless of whether there is a new radio access technology (RAT) to take over. I don’t think this will change, or maybe the Industry will spend much more on software annually than previously and less on hardware modernization during obsolescence transformations. Though I suspect that the software would impose increasingly harder requirements on the underlying hardware (whether on-prem or in the cloud), modernization investments into the hardware part would continue to be substantial. This is not even considering the euphoria that may come around the next generation RAT (e.g., 6G).
The fixed broadband fiber infrastructure’s economical and useful life is much longer than that of the mobile infrastructure. The optical transmission equipment is likewise used for access, aggregation, and backbone (although not as long as the optical fiber itself). Additionally, fiber-based fixed broadband networks are operationally (much) more efficient than their mobile counterparts, alluding to the need to re-architect and redesign how they are being built as they are no longer needed inside customer dwellings. Overall, it is not unreasonable to expect that fixed broadband modernization investments will occur less frequently than for mobile networks.
Is Enough Customer Bandwidth a Thing?
Is there an optimum level of bandwidth in bits per second at which a customer is fully (optimized) served? Beyond that, whether the network could provide far more speed or quality does not matter.
For example. for most mobile devices, phones, and tablets, much more than 10 Mbps for streaming would not make much of a viewing difference for the typical customer. Given the assumptions about eyesight and typical viewing distances, more than 90% of people would not notice an improvement in viewing experience on a mobile phone or tablet beyond 1080p resolution. Increasing the resolution beyond that point—such as to 1440p (Quad HD) or 4K would likely not provide a noticeably better experience for most users, as their visual acuity limits their ability to discern finer details on small screens. This means the focus for improving mobile and tablet displays shifts from resolution to other factors like color accuracy, brightness, and contrast rather than chasing higher pixel counts. An optimization strategy that should not necessarily result in higher bandwidth requirements, although moving to higher color depth or more brightness / dynamic range (e.g., HDR vs SDR) would lead to a moderate increase in the required data ranges.
A throughput between 50 and 100 Mbps for fixed broadband TV streaming currently provides an optimum viewing experience. Of course, a fixed broadband household may have many concurrent bandwidth demands that would justify a 1 Gbps fiber to the home or maybe even 10 Gbps downstream to serve the whole household at an optimum experience at any time.
Figure 13 provides the data rate ranges for a streaming format, device type, and typical screen size. The data rate required for streaming video content is determined by various factors, including video resolution, frame rate, compression, and screen size. The data rate calculation (in Mbps) for different streaming formats follows a process that involves estimating the amount of data required to encode each frame and multiplying by the frame rate and compression efficiency. The methodology can be found in many places. See also my blog “5G Economics – An Introduction (Chapter 1)” from Dec. 2016.
Let’s move into high-end and fully immersive virtual reality experiences. The user bandwidth requirement may exceed 100 Mbps and possibly even require a Gbps sustainable bandwidth delivered to the user device to provide an optimum experience. However, jitter and latency performance may not make such full immersion or high-end VR experiences fully optimal over mobile or fixed networks with long distances to the supporting (edge) data centers and cloud servers where the related application may reside. In my opinion, this kind of ultra-high-end specialized service might be better run exclusively on location.
Size Matter.
I once had a CFO who was adamant that an organization’s size on its own would drive a certain amount of Capex. I would, at times, argue that an organization’s size should depend on the number of activities required to support customers (or, more generally, the number of revenue-generating units (RGUs), your given company has or expects to have) and the revenue those generate. In my logic, at the time, the larger a country in terms of surface area, population, and households, the more capex-related activities would be required, thus also resulting in the need for a bigger organization. If you have more RGU, it might also not be too surprising that the organization would be bigger.
Since then, I have scratched my head many times when I look at country characteristics, the RGUs, and Revenues, asking how that can justify a given size of Telco organizations, knowing that there are other Telcos out there that spend the same or more Capex with a substantially smaller organization (also after considering the difference in sourcing strategies). I have never been with an organization that irrespective of its size did not feel pressured work-wise and believed it was too lightly staffed to operate, irrespective of the Capex and activities under management.
Figure 14 illustrates the correlation between the Capex and the number of FTEs in a Telco organization. It should be noted that the upper right point results in a very good correlation of 0.75. Without this point, the correlation would be around 0.25. Note that sourcing does have a minor effect on the correlation.
The above figure illustrates a strong correlation between Capex and the number of people in a Telco organization. However, the correlation would be weaker without the upper right data point. In the data shown here, you will find no correlation between FTEs and a country’s size, such as population or surface area, which is also the case for Capex. There is a weak correlation between FTEs and RGU and a stronger correlation with Revenues. Capex, in general, is very strongly correlated with Revenues. The best multi-linear regression model, chosen by p-value, is a model where Capex relates to FTEs and RGUs. For a Telco with 1000 employees and 1 million RGUs, approximately 50% of the Capex could be explained by the number of FTEs. Of course, in the analysis above, we must remember that correlation does not imply causation. You will have telcos that, in most Capex driver aspects, should be reasonably similar in their investment profiles over time, except the telco with the largest organization will consistently invest more in Capex. While I think this is, in particular, an incumbent vs challenger issue, it is a much broader issue in our industry.
Having spent most of my 20+ year career in Telecom being involved in Capex planning and budgeting, it is clear that the size of an organization plays a role in the size of a Capex budget. Intuitively, it should not be too surprising. Suppose the Capex is lower than the capacity of your organization. In that case, you may have to lay off people with the risk you might be short of resources in the future as you may cycle through modernization or a new technology introduction. On the other hand, if the Capex needs are substantially larger than the organization can cope with, including any sourcing agreements in place, it may not make too much sense to ask for more than what can be managed with the resources available (apart from it being sub-optimal for cash flow optimization).
Telco companies that have fixed and mobile broadband infrastructure in their portfolio with organizations that are poorly optimized and with strict demarcation lines between people working on fixed broadband and mobile broadband will, in general, have much worse Capex efficiencies compared to fully fixed-mobile converged organizations (not to mention suffering from poorer operational efficiencies and work practices compared to integrated organizations). Here, the size of, for example, a mobile organization will drive behavior that rather would spend above and beyond Capex in their Radio Access Network infrastructure than use more clever and proven solutions (e.g., Opanga’s RAIN) to optimize quality and capacity needs across their mobile networks.
In general, the resistance to utilize smarter solutions and clever ideas that may save Capex (and/or Opex) is manifesting in a many-fold of behaviors that I have observed over my 25+ year career (and some I might even have adapted on occasion … but shhhh;-).
Budget heuristics:
𝗦𝗶𝘇𝗲 𝗱𝗼𝗲𝘀𝗻𝘁 𝗺𝗮𝘁𝘁𝗲𝗿 𝗽𝗮𝗿𝗮𝗱𝗶𝗴𝗺 Irrespective of size, my organization will always be busy and understaffed.
𝗧𝗵𝗲 𝗚𝗼𝗹𝗱𝗶𝗹𝗼𝗰𝗸𝘀 𝗙𝗮𝗹𝗹𝗮𝗰𝘆 My organization’s size and structure will determine its optimum Capex spending profile, allowing it to stay busy (and understaffed).
𝗧𝗮𝗻𝗴𝗶𝗯𝗹𝗲 𝗕𝗶𝗮𝘀 A hardware (infrastructure-based) solution is better and more visible than a software solution. I feel more comfortable with my organization being busy with hardware.
𝗧𝗵𝗲 𝗦𝘂𝗻𝗸 𝗖𝗼𝘀𝘁 𝗙𝗮𝗹𝗹𝗮𝗰𝘆 I don’t trust (allegedly) clever software solutions that may lower or postpone my Capex needs and, by that, reduce the need for people in my organization.
𝗕𝘂𝗱𝗴𝗲𝘁 𝗠𝗮𝘅𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗧𝗲𝗻𝗱𝗲𝗻𝗰𝘆 My organization’s importance and my self-importance are measured by how much Capex I have in my budget. I will resist giving part of my budget away to others.
𝗦𝘁𝗮𝘁𝘂𝘀 𝗤𝘂𝗼 𝗕𝗶𝗮𝘀 I will resist innovation that may reduce my Capex budget, even if it may also help reduce my Opex.
𝗝𝗼𝗯 𝗣𝗿𝗼𝘁𝗲𝗰𝘁𝗶𝗼𝗻𝗶𝘀𝗺 I resist innovation that may result in a more effective organization, i.e., fewer FTEs.
𝗖𝗮𝗽𝗮𝗰𝗶𝘁𝘆 𝗖𝗼𝗺𝗳𝗼𝗿𝘁 𝗦𝘆𝗻𝗱𝗿𝗼𝗺𝗲: The more physical capacity I build into my network, the more we can relax. Our goal is a “Zero Worry Network.”
𝗧𝗵𝗲 𝗙𝗲𝗮𝗿 𝗙𝗮𝗰𝘁𝗼𝗿: The leadership is “easy to scare” when arguing for more capacity Capex opposed to the “if-not”-consequences. (e.g., losing best network awards, poorer customer experience, …).
𝗧𝗵𝗲 𝗕𝘂𝗱𝗴𝗲𝘁 𝗜𝗻𝗲𝗿𝘁𝗶𝗮 Return on Investment (ROI) prioritization is rarely considered (rigorously), particularly after a budget has been released.
𝗔 𝘄𝗮𝗿𝗻𝗶𝗻𝗴: although each is observable in the live, the reader should be aware that there is also a fair amount of deliberate ironic provocation in the above heuristics.
We should never underestimate that within companies, two things make you important (including self-important and self-worthy) … It is: (1) The size of your organization and (2) the amount of money, your budget size, you have for your organization to be busy with.
Any innovation that may lower an organization’s size and budget will be met with resistance from that organization.
The Balancing Act of Capex to Opex Transformations.
Telco cost structures and Capex have evolved significantly due to accounting changes, valuation strategies, technological advancements, and economic pressures. While shifts like IFRS (International Financial Reporting Standards), issued by the International Accounting Standards Board (IASB), have altered how costs are reported and managed, changes in business strategies, such as cell site spin-offs, cloud migrations, and the transition to software-defined networks, have reshaped Capex allocations somewhat. At the same time, economic crises and competitive pressures have influenced Telcos to continually reassess their capital investments, balancing the need to optimize value, innovation, and growth with financial diligence.
One of the most significant drivers of change has been the shift in accounting standards, particularly with the introduction of IFRS16, which replaced the older GAAP-based approaches. Under IFRS16, nearly all leases are now recognized on the balance sheet as right-of-use assets and corresponding liabilities. This change has particularly impacted Telcos, which often engage in long-term leases for cell sites, network infrastructure, and equipment. Previously, under GAAP (Generally Accepted Accounting Principles), many leases were treated as operating leases, keeping them off the balance sheet, and their associated costs were considered operational expenditures (Opex). Now, under IFRS16, these leases are capitalized, leading to an increase in reported Capex as assets and liabilities grow to reflect the leased infrastructure. This shift has redefined how Telcos manage and report their Capex, as what was previously categorized as leasing costs now appears as capital investments, altering key financial metrics like EBITDA and debt ratios that would appear stronger post-IFRS16.
Simultaneously, valuation strategies and financial priorities have driven significant shifts in Telco Capex. Telecom companies have increasingly focused on enhancing metrics such as EBITDA and capital efficiency, leading them to adopt strategies to reduce heavy capital investments. One such strategy is the cell site spin-off, where Telcos sell off their tower and infrastructure assets to specialized independent companies or create separate entities that manage these assets. These spin-offs have allowed Telcos to reduce the Capex tied to maintaining physical assets, replacing it with leasing arrangements, which shift costs towards operational expenses. As a result, Capex related to infrastructure declines, freeing up resources for investments in other areas such as technology upgrades, customer services, and digital transformation. The spun-off infrastructures often result in significant cash inflows from sales. The telcos can then use this cash to improve their balance sheets by reducing debt, reinvesting in new technologies, or distributing higher dividends to shareholders. However, this shift may also reduce control over critical network infrastructure and create long-term lease obligations, resulting in substantial operational expenses as telcos will have to pay the rental costs on the spun-off infrastructure, increasing Opex pressure. I regularly see analysts using the tower spin-off as an argument for why Capex requirements of telcos are no longer wholly trustworthy and, in particular, in comparison with the past capital spending as the passive part of the cell site built used to be a substantial share mobile site Capex of up to 50% to 60% for a standard site built and beyond that for special sites. I believe that as not many new cell sites are being built any longer, and certainly not as many as in the 90s and 2000s, this effect is very minor on the overall Capex. Most new sites are built at a maintenance level, covering new residential or white spot areas.
When considering mobile network evolution and the impact of higher frequencies, it is important not to default to the assumption that more cell sites will always be necessary. If all things are equal, the coverage cell range of a high carrier frequency would be shorter (often much shorter) than the coverage range at a lower frequency. However, all things are not equal. This misconception arises from a classical coverage approach, where the frequency spectrum is radiated evenly across the entire cell area. However, modern cellular networks employ advanced technologies such as beamforming, which allows for more precise and efficient distribution of radio energy. Beamforming concentrates signal power in specific directions rather than thinly spreading it across a wide area, effectively increasing reach and signal quality without additional sites. Furthermore, the support for asymmetric downlink (higher) and uplink (lower) carrier frequencies allows for high-quality service downlink and uplink in situations where the uplink might be challenged at higher frequencies.
Moreover, many mobile networks today have already been densified to accommodate coverage needs and capacity demands. This densification often occurred when spectrum resources were scarce, and the solution was to add more sites for improved performance rather than simply increasing coverage. As newer frequency bands become available, networks can leverage beamforming and existing densification efforts to meet coverage and capacity requirements without necessarily expanding the number of cell sites. Thus, the focus should be optimizing the deployment of advanced technologies like beamforming and Massive MIMO rather than increasing the site count by default. In many cases, densified networks are already equipped to handle higher frequencies, making additional sites unnecessary for coverage alone.
The migration to public cloud solutions from, for example, Amazon’s AWS or Microsoft Azure is another factor influencing the Capex of Telcos. Historically, telecom companies relied on significant upfront Capex to build and maintain their own data centers or switching locations (as they were once called, as these were occupied mainly by the big legacy telecom proprietary telco switching infrastructure), network operations centers, and IT (monolithic) infrastructure. However, with the rise of cloud computing, Telcos are increasingly migrating to cloud-based solutions, reducing the need for large-scale physical infrastructure investments. This shift from hardware to cloud services changes the composition of Capex as the need for extensive data center investments declines, and more flexible, subscription-based cloud services are adopted. Although Capex for physical infrastructure decreases, there is a shift towards Opex as Telcos pay for cloud services on a usage basis.
Further, the transition to software-defined networks (SDNs) and software-centric telecom solutions has transformed the nature of Telco Capex. In the past, Telcos heavily depended on proprietary hardware for network management, which required substantial Capex to purchase and maintain physical equipment. However, with the advancement of virtualization and SDNs, telcos have shifted away from hardware-intensive solutions to more software-driven architectures. This transition reduces the need for continuous Capex on physical assets like routers, switches, and servers and increases investment in software development, licensing, and cloud-based platforms. The software-centric model allows, in theory, Telcos to innovate faster and reduce long-term infrastructure costs.
The Role of Capex in Financial Statements.
Capital expenditures play a critical role in shaping a telecommunications company’s financial health, influencing its income statement, balance sheet, and cash flow statements in various ways. At the same time, Telcos establish financial guardrails to manage the impact of Capex spending on dividends, liquidity, and future cash needs.
In the income statement (see Figure 15 below), Capex does not appear directly as an expense when it is incurred. Instead, it is capitalized on the balance sheet and then expensed over time through depreciation (for tangible assets) or amortization (for intangible assets). This gradual recognition of the Capex expenditure leads to higher depreciation or amortization charges over future periods, reducing the company’s net income. While the immediate impact of Capex is not seen on the income statement, the long-term effects can improve revenue when investments enhance capacity and quality, as with technological upgrades like 5G infrastructure. However, these benefits are offset by the fact that depreciation lowers profitability in the short term (as the net profit is lowered). The last couple of radio access technology (RAT) generations have, in general, caused an increase in telcos’ operational expenses (i.e., Opex) as more cell sites are required, heavier site configurations are implemented (e.g., multi-band antennas, massive MiMo antennas), and energy consumption has increased in absolute terms. Despite every new generation having become relatively more energy efficient in terms of the kWh/GB, in absolute terms, this is not the case, and that matters for the income statement and the incurred operational expenses.
Figure 15 illustrates the typical income statement one may find in a telco’s annual report or official financial statements. The purpose here is to show where Capex may have an influence although Capex will not be directly stated in the Income Statement. Note: the numbers in the above financial statement are for illustration only representing a Telco with 35% EBITDA margin, 20% Capex to Revenue Ratio and a Tax rate of 22%.
On the balance sheet (see Figure 16 below), Capex increases the value of a company’s fixed assets, typically recorded as property, plant, and equipment (PP&E). As new assets are added, the company’s overall asset base grows. However, this is balanced by the accumulation of depreciation, which gradually reduces the book value of these assets over time. How Capex is financed also affects the company’s liabilities or equity. If debt is used to finance Capex, the company’s liabilities increase; if equity financing is used, shareholders’ equity increases. The Balance Sheet together with the Depreciation & Amortization (D&A), typically given in the income statement, can help us estimate the amount of Capex a Telco has spend. The capital expense, typically not directly reported in a companies financial statements, can be estimated by adding the changes between subsequent years of PP&E and Intangible Assets to the D&A.
Figure 16 illustrates the balance sheet one may find in a telco’s annual report or official financial statements. The purpose here is to show where Capex may have an influence. Knowing the Depreciation & Amortization (D&A) typically shown in the Income Statement, the change in PP&E and Intangible Assets (between two subsequent years) will provide an estimate of the Capex of the current year. Note: the numbers in the above financial statement are for illustration only representing a Telco with 35% EBITDA margin, 20% Capex to Revenue Ratio and a Tax rate of 22%.
In the cash flow statement, Capex appears as an outflow under the category of cash flows from investing activities, representing the company’s spending on long-term assets. In the short term, this creates a significant reduction in cash. However, well-planned Capex to enhance infrastructure or expand capacity can lead to higher operating cash flows in the future. If Capex is funded through debt or equity issuance, the inflow of funds will be reflected under cash flows from financing activities.
Figure 17 illustrates the Cash Flow Statements one may find in a telco’s annual report or official financial statements (might have a bit more details than what usually would be provided). We would typically get a 70+% impression of a Telco’s Capex level by looking at the “Net Cash Flow Used in Investing Activities”, unless we are offered Purchases of Tangible and Intangible Assets. Note: the numbers in the above financial statement are for illustration only representing a Telco with 35% EBITDA margin, 20% Capex to Revenue Ratio and a Tax rate of 22%.
To ensure Capex does not overly strain the company’s financial health or limit returns to shareholders, Telcos put in place financial guardrails. Regarding dividends, many companies set specific dividend payout ratios, ensuring that a portion of earnings or free cash flow is consistently returned to shareholders. This practice balances returning value to shareholders while retaining sufficient earnings to fund operations and investments. It is also not unusual that Telco’s commit a given dividend level to shareholders, that as a consequence may place a limit on Capex spending or result in Capex tasking within a given planning period, as management must balance cash outflows between shareholder returns and strategic investments. This may lead to prioritizing essential projects, delaying less critical investments, or seeking alternative financing to maintain both Capex and dividend commitments. Additionally, Telcos often use dividend coverage ratios to ensure they can sustain dividend payouts even during periods of heavy capital expenditure.
Some telcos have chosen not to commit dividends to shareholders in order to maximize Capex investments, aiming to reinvest profits into the business to drive long-term growth and create higher shareholder value. This strategy prioritizes network expansion, technological upgrades, and new market opportunities over immediate cash returns, allowing the company to maintain financial flexibility and pursue strategic objectives more aggressively. When a telco decides to start paying dividends, it may indicate that management believes there are fewer high-value investment opportunities that can deliver returns above the company’s cost of capital. The decision to pay dividends often reflects the view that shareholders may derive greater value from the cash than the company could generate by reinvesting it. Often it signals a shift to a higher degree of maturity (e.g., corporate or market wise) from having been a growth focused company (i.e., the Telco has past the inflection point of growth). An example of maturity, and maybe less about growth opportunities, is the case of T-Mobile USA which in 2024 announced that it would start to pay dividend for the first time in its history targeting a 10 percent annually per share (note: Deutsche Telekom AG gained ownership in 2001, the company was founded in 1994).
Liquidity management is another consideration. Companies monitor their liquidity through current or quick ratios to ensure they can meet short-term obligations without cutting dividends or pausing important Capex projects. To provide an additional safety net, Telcos often maintain cash reserves or access to credit lines to handle immediate financial needs without disrupting long-term investment plans.
Regarding debt management, Telcos must carefully balance using debt to finance Capex. Companies often track their debt-to-equity ratio to avoid over-leveraging, which can lead to higher interest expenses and reduced financial flexibility. Another common metric is net debt to EBITDA, which ensures that debt levels remain manageable concerning the company’s earnings. To avoid breaching agreements with lenders, Telcos often operate under covenants that limit the amount they can spend on Capex without negatively affecting their ability to service debt or pay dividends.
Telcos also plan long-term cash flow to ensure Capex investments align with future financial needs. Many companies establish a capital allocation framework that prioritizes projects with the highest returns, ensuring that investments in infrastructure or technology do not jeopardize future cash flow. Free cash flow (FCF) is a particularly important metric in this context, as it represents the amount of cash available after covering operating expenses and Capex. A positive FCF ensures the company can meet future cash needs while returning value to shareholders through dividends or share buybacks.
Capex budgeting and prioritization are also essential tools for managing large investments. Companies assess the expected return on investment (ROI) and the payback period for Capex projects, ensuring that capital is allocated efficiently. Projects with assumed high strategic value, such as 5G infrastructure upgrades, household fiber coverage, or strategic fiber overbuilt, are often prioritized for their potential to drive long-term revenue growth. Monitoring the Capex-to-sales ratio helps ensure that capital investments are aligned with revenue growth, preventing over-investment in infrastructure that may not yield sufficient returns.
CAPEX EXPECTATIONS 2024 to 2026.
Considering all of the 54 telcos, ignoring MasMovil and WindHellas that are in the process of being integrated, in the pool of New Street Research Quarterly review each with their individual as well as country “peculiarities” (e.g., state of 5G deployment, fiber-optical coverage, fiber uptake, merger-resulting integration Capex, general revenue trends, …), it is possible to get a directional idea of how Capex will develop for each individual telco as well as the overall trend. This is illustrated in the Figure below on a Western European level.
I expect that we will not see a Capex reduction in 2024, supported by how Capex in the third and fourth quarters usually behave compared to the first two quarters, and due to integration and transformation Capex that will carry from 2023 into 2024 and possibly with a tail-end in 2024. I expect most telcos will cut back on new mobile investments, even if some might start ripping out radio access infrastructure from Chinese suppliers. However, I also believe that telcos will try to delay replacement to 2026 to 2028, when the first round of 5G modernization activities would be expected (and even overdue for some countries).
While 5G networks have made significant advancements, the rollout of 5G SA remains limited. By the end of 2023, only five of 39 markets analyzed by GSMA have reached near-complete adoption of 5G SA networks. 17 markets had yet to launch 5G SA at all. One of the primary barriers is the high cost of investment required to build the necessary infrastructure. The expansion and densification of 5G networks, such as installing more base stations, are essential to support 5G SA. According to GSMA, many operators are facing financial hurdles, as returns in many markets have been flat, and any increase is mainly due to inflationary price corrections rather than incremental or new usage occurring. I suspect that telcos may also be more conservative (and even more realistic, maybe) in assessing the real economic potential of the features being enabled by migrating to 5G SA, e.g., advanced network slicing, ultra-low latency, and massive IoT capabilities in comparison with the capital investments and efforts that they would need to incur. I should point out that any core network investments supporting 5G SA would not be expected to have a visible impact on telcos Capex budgets as this would be expected to be less than 10% of the mobile capex.
Figure 18 shows the 2022 status of homes covered by fiber in 16 Western European countries, as well as the number of households remaining. It should be noted that a 100% coverage level may be unlikely, and this data does not consider fiber overbuilt (i.e., multiple companies covering the same households with their individual fiber deployments). Fiber overbuilt becomes increasingly likely as the coverage exceeds 80% (on a geographical regional/city basis). The percentages (yellow color) above the chart show the share of Total 2022 Western European Capex for the country, e.g., Germany’s share of the 2022 Capex was 18% and had ca. 19% of all German households covered with fiber. Source: based on Omdia & Point Topic’s “Broadband Coverage in Europe 2013-2022” (EU Commission Report).
In 2022, a bit more than 50% of all Western European households were covered by fiber (see Figure 18 above), which amounts to approximately 85 million households with fiber coverage. This also leaves approximately 80 million households without fiber reach. Almost 60% of households without fiber coverage are in Germany (38%) and the UK (21%). Both Germany and the UK contributed about 40% of the total Western European Capex spend in 2022.
Moreover, I expect there are still Western European markets where the Capex priority is increasing the fiber-optic household coverage. In 2022, there was a peak in new households covered by fiber in Western Europe (see Figure 15 below), with 13+ million households covered according to the European Commission’s report “Broadband Coverage in Europe 2013-2022“. Germany (a fiber laggard) and the UK, which account for more than 35% of the Western European Capex, are expected to continue to invest substantially in fiber coverage until the end of the decade. As Figure 19 below illustrates, there is still a substantial amount of Capex required to close the fixed broadband coverage gap some Western European countries have.
Figure 19 illustrates the number of households covered by fiber (homes passed) and the number of millions of new households covered in a year. The period from 2017 to 2022 is based on actuals. The period from 2023 to 2026 is forecasted for new households covered based on the last 5-year average deployment or the maximum speed over the last 5 years (Urban: e.g., DE, IT, NL, UK,…) with deceleration as coverage reaches 95% for urban areas and 80% for rural (note: may be optimistic for some countries). The fiber deployment model differentiates between Urban and Rural areas. Source: based on Omdia & Point Topic’s “Broadband Coverage in Europe 2013-2022” (EU Commission Report).
I should point out that I am not assuming that telcos would be required over the next couple of years to swap out Chinese suppliers outside the scope of the European Commission “The EU 5G Toolkit for Security” framework that mainly focuses on 5G mobile networks eventually including the radio access network. It should be kept in mind that there is a relatively big share of high-risk suppliers within the Western European (actually in most European Union member states) fixed broadband networks (e.g., core routers & switches, SBCs, OLT/ONTs, MSAPs) that if subjected to “5G Toolkit for Security”-like regulation, such as in effect in Denmark (i.e., “The Danish Investment Screening Act”), would result in substantial increase in telcos fixed capital spend. We may see that some Western European telcos will commence replacement programs as equipment becomes obsolete (or near obsolete), and I would expect that the fixed broadband Capex will remain relatively high for telcos in Western Europe even beyond 2026.
Thus, overall, I think it is not unrealistic to anticipate a decrease in Capex over the next 3 years. Contrary to some analysts’ expectations, I do not see the lower Capex level being persistent but rather what to expect due to the reasons given above in this blog.
Figure 20 illustrates the pace and financial requirements for fiber-to-the-premises (FTTP) deployment across the EU, emphasizing the significant challenges ahead. Germany needs the highest number of households passed per week and the largest investments at €32.9 billion to reach 80% household coverage by 2031. The total investment required to reach 80% household fiber coverage by 2031 is estimated at over €110 billion, with most of this funding allocated to urban areas. Despite progress, more than 57% of Western European households still lack fiber coverage as of 2022. Achieving this goal will require maintaining the current pace of deployment and overcoming historical performance limitations. Source: based on Omdia & Point Topic’s “Broadband Coverage in Europe 2013-2022” (EU Commission Report).
CAPEX EXPECTATIONS TOWARDS 2030.
Taking the above Capex forecasting approach, based on the individual 54 Western European telcos in the New Street Research Quarterly review, it is relatively straightforward, but not per se very accurate, to extend to 2030, as shown in the figure below.
It is worth mentioning that predicting Capex’s reliability over such a relatively long period of ten years is prone to a high degree of uncertainty and can actually only be done with relatively high reliability if very detailed information is available on each telco’s long-term, short-term and strategy as well as their economic outlook. In my experience from working with very detailed bottom-up Capex models covering a five and beyond-year horizon (which is not the approach I have used here simply for lack of information required for such an exercise not to be futile), it is already prone to a relatively high degree of uncertainty even with all the information, solid strategic outlook, and reasonable assumptions up front.
Figure 21 illustrates Western Europe’s projected capital expenditure (Capex) development from 2020 to 2030. The slight increase in Capex towards 2030 is primarily driven by the modernization of 5G radio access networks (RAN), which could potentially incorporate 6G capabilities and further deploy 5G Standalone (SA) networks. Additionally, there is a focus on swapping out high-risk suppliers in the mobile domain and completing heavy fiber household coverage in the remaining laggard countries. Suppose the European Commission’s 5G Security Toolkit should be extended to fixed broadband networks, focusing on excluding high-risk suppliers in the 5G mobile domain. In that case, this scenario has not been factored into the current model represented here. The percentages on the chart represent the overall Capex to Total Revenue ratio development over the period.
The capital expenditure trends in Western Europe from 2020 to 2030, with projections indicating a steady investment curve (remember that this is the aggregation of 54 Western European telcos Capex development over the period).
A noticeable rise in Capex towards 2030 can be attributed to several key factors, primarily the modernization of 5G Radio Access Networks (RAN). This modernization effort will likely include upgrades to the current 5G infrastructure and potential integration of 6G (or renamed 5G SA) capabilities as Europe prepares for the next generation of mobile technology, which I still believe is an unavoidable direction. Additionally, deploying or expanding 5G Standalone (SA) networks, which offer more advanced features such as network slicing and ultra-low latency, will further drive investments.
Another significant factor contributing to the increased Capex is the planned replacement of high-risk suppliers in the mobile domain. Countries across Western Europe are expected to phase out network equipment from suppliers deemed risky for national security, aligning with broader EU efforts to ensure a secure telecommunications infrastructure. I expect a very strong push from some member state regulators and the European Commission to finish the replacement by 2027/2028. I also expect impacted telcos (of a certain size) to push back and attempt to time a high-risk supplier swap out with their regular mobile infrastructure obsolescence program and introduction of 6G in their networks towards and after 2030.
Figure 22 shows the projections for 2023 and 2030 for the number of homes covered by fiber in Western European countries and the number of households remaining. It should be noted that a 100% coverage level may be unlikely, and this data does not consider fiber overbuilt (i.e., multiple companies covering the same households with their individual fiber deployments). Fiber overbuilt becomes increasingly likely as the coverage exceeds 80% (on a geographical regional/city basis). Source: based on Omdia & Point Topic’s “Broadband Coverage in Europe 2013-2022” (EU Commission Report).
Simultaneously, Western Europe is expected to complete the extensive rollout of fiber-to-the-home (FTTH) networks, as illustrated by Figure 20 above, particularly in countries lagging behind in fiber deployment, such as Germany, the UK, Belgium, Austria, and Greece. These EU member states will likely have finished covering the majority of households (80+%) with high-speed fiber by the end of the decade. On this topic, we should remember that telcos are using various fiber deployment models that minimize (and optimize) their capital investment levels. By 2030 I would expect that almost 80% of all Western European households will be covered with fiber and thus most consumers and businesses will have easy access to gigabit services to their homes by then (and for most countries long before 2030). Germany is still expected to be the Western European fiber laggard by 20230, with an increased share of 50+% of German households not being covered by fiber (note: in 2022, this was 38%). Most other countries will have reached and exceeded 80% fiber household coverage.
It is also important to note that my Capex model does not assume the extension of the European Commission’s 5G Security Toolkit, which focuses on excluding high-risk suppliers in the 5G domain to fixed broadband networks. If the legal framework were to be applied to the fixed broadband sector as well, an event that I see to be very likely, forcing the removal of high-risk suppliers from fiber broadband networks, Capex requirements would likely increase significantly beyond the projections represented in my assessment with the last years of the decade focused on high-risk supplier replacement in Western European Telcos fixed broadband transport and IP networks. While it is I don’t see a (medium-high) risk that all CPEs would be included in a high-risk supplier ban. However, I do believe that CPEs with the ONT integrated may be required to replace their installed CPE base. If a high-risk supplier ban were to include the ONT, there would be several implications.
Any CPEs that use components from the banned supplier would need to be replaced or retrofitted to ensure compliance. This would require swapping the integrated CPE/ONT units for separate CPE and ONT devices from approved suppliers, which could add to installation costs and increase deployment time. Service providers would also need to reassess their network equipment supply chain, ensuring that new ONTs and CPEs meet regulatory standards for security and compliance. Moreover, replacing equipment could potentially disrupt existing service, necessitating careful planning to manage the transition without major outages for customers. This situation would likely also require updates to the network configuration, as replacing an integrated CPE/ONT device could involve reconfiguring customer devices to work seamlessly with the new setup. I believe it is very likely that telcos eventually will offer fixed broadband service, including CPEs and home gateways, that are free of high-risk suppliers end-2-end (e.g., for B2B and public institutions, e.g., defense and other critically sensitive areas). This may extend to requirements that employees working in or with sensitive areas will need a certificate of high-risk supplier-free end-2-end fixed broadband connection to be allowed to work from home or receive any job-related information (this could extend to mobile devices as well). Again, substantial Capex (and maybe a fair amount of time as well) would be required to reach such a high-risk supplier reduction.
AN ALTERNATE REALITY.
I am unsure whether William Webb’s idea of “The End of Telecoms History” (I really recommend you get his book) will have the same profound impact as Francis Fukuyama’s marvelously thought-provoking book “The End of History and the Last Man“ or be more “right” than Fukuyama’s book. However, I think it may be an oversimplification of his ideas to say that he has been proven wrong. The world of Man may have proven more resistant to “boredom” than the book assumed (as Fukuyama conceded in subsequent writing). Nevertheless, I do not believe history can be over unless the history makers and writers are all gone (which may happen sooner rather than later). History may have long and “boring” periods where little new and disruptive things happen. Still, historically, something so far has always disrupted the hiatus of history, followed by a quieter period (e.g., Pax Romana, European Feudalism, Ming Dynasty, 19th century’s European balance of power, …). The nature of history is cyclic. Stability and disruption are not opposing forces but part of an ongoing dynamic. I don’t think telecommunication would be that different. Parts of what we define as telecom may reach a natural end and settle until it is disrupted again; for example, the fixed telephony services on copper lines were disrupted by emerging mobile technologies driven by radio access technology innovation back in the 90s and until today. Or, like circuit-switched voice-centric technologies, which have been replaced by data-centric packet-switched technologies, putting an “end” to the classical voice-based business model of the incumbent telecommunication corporations.
At some point in the not-so-distant future (2030-2040), all Western European households will be covered by optical fiber and have a fiber-optic access connection with indoor services being served by ultra-WiFi coverage (remember approx. 80% of mobile consumption happens indoors). Mobile broadband networks have by then been redesigned to mainly provide outdoor coverage in urban and suburban areas. These are being modernized at minimum 10-year cycles as the need for innovation is relatively minor and more focused on energy efficiency and CO2 footprint reductions. Direct-to-cell (D2C) LEO satellite or stratospheric drone constellations utilizing a cellular spectrum above 1800 MHz serve outdoor coverage of rural regions, as opposed to the current D2C use of low-frequency bands such as 600 – 800 MHz (as higher frequency bands are occupied terrestrially and difficult to coordinate with LEO Satellite D2C providers). Let’s dream that the telco IT landscape, Core, transport, and routing networks will be fully converged (i.e., no fixed silo, no mobile silo) and autonomous network operations deal with most technical issues, including planning and optimization.
In this alternate reality, you pay for and get a broadband service enabled by a fully integrated broadband network. Not a mobile service served by a mobile broadband network (including own mobile backhaul, mobile aggregation, mobile backbone, and mobile core), and, not a fixed service served by a fixed broadband network different from the mobile infrastructure.
Given the Western European countries addressed in this report (i.e., see details in Further Reading #1), we would need to cover a surface area of 3.6 million square kilometers. To ensure outdoor coverage in urban areas and road networks, we may not need more than about 50,000 cell sites compared to today’s 300 – 400 thousand. If the cellular infrastructure is shared, the effective number of sites that are paid in full would be substantially lower than that.
The required mobile Capex ballpark estimate would be a fifth (including its share of related fixed support investment, e.g., IT, Core, Transport, Switching, Routing, Product development, etc.) of what it otherwise would be if we continue “The Mobile History” as it has been running up to today.
In this “Alternate Reality” ” instead of having a mobile Capex level of about 10% of the total fixed and mobile revenue (~15+% of mobile service revenues), we would be down to between 2% and 3% of the total telecom revenues (assuming it remains reasonably flat at a 2023 level. The fixed investment level would be relatively low, household coverage would be finished, and most households would be connected. If we use numbers of fixed broadband Capex without substantial fiber deployment, that level should not be much higher than 5% of the total revenue. Thus, instead of today’s persistent level of 18% – 20% of the total telecom revenues, in our “Alternate Reality,” it would not exceed 10%. And just imagine what such a change would do to the operational cost structure.
Obviously, this fictive (and speculative) reality would be “The End of Mobile History.”
It would be an “End to Big Capex” and a stop to spending mobile Capex like there is no (better fixed broadband) tomorrow.
This is an end-reflection of where the current mobile network development may be heading unless the industry gets better at optimizing and prioritizing between mobile and fixed broadband. Re-architecting the fundamental design paradigms of mobile network design, plan, and build is required, including an urgent reset of current 6G thinking.
ACKNOWLEDGEMENT.
I greatly acknowledge my wife, Eva Varadi, for her support, patience, and understanding during the creative process of writing this Blog. There should be no doubt that without the support of Russell Waller (New Street Research), this blog would not have been possible. Thank you so much for providing the financial telco data for Western Europe that lays the ground for much of the Capex analysis in this article. This blog has also been published in telecomanalysis.net with some minor changes and updates.
FURTHER READING.
New Street Research covers the following countries in their Quarterly report: Austria, Belgium, Denmark, Finland, France, Germany, Greece, Italy, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, and the United Kingdom. Across those 15 countries, ca. 56 telcos are covered.
Rupert Wood, “A crisis of overproduction in bandwidth means that telecoms capex will inevitably fall,” Analysys Mason (July 2024). A rather costly (for mortals & their budgets, at least) report called “The end of big capex: new strategic options for the telecoms industry”allegedly demonstrates the crisis.
Danish Investment Screening Act, “Particularly sensitive sectors and activities,” Danish Business Authority, (July 2021). Note that the “Danish Investment Screening Act” is closely aligned with broader European Union (EU) frameworks and initiatives to safeguard critical infrastructure from high-risk foreign suppliers. The Act reflects Denmark’s effort to implement national and EU-level policies to protect sensitive sectors from foreign investments that could pose security risks, particularly in critical infrastructure such as telecommunications, energy, and defense.
German press on high-risk suppliers in German telecommunications networks: “Zeit für den Abschied von Huawei, sagt Innenministerin Faeser” (Handelsblatt, August 18, 2023), “Deutsche Telekom und Huawei: Warum die Abhängigkeit bleibt” (Die Welt, September 7, 2023), “Telekom-Netz: Kritik an schleppendem Rückzug von Huawei-Komponenten” (Der Spiegel, September 20, 2023), “Faeser verschiebt Huawei-Bann und stößt auf heftige Kritik” (Handelsblatt, July 18, 2024), “Huawei-Verbot in 5G-Netzen: Deutschland verschärft, aber langsam” (Tagesschau, July 15, 2024), and “Langsame Fortschritte: Deutschland und das Huawei-Dilemma” (Der Spiegel, September 21, 2024) and many many others.
Kim Kyllesbech Larsen, “Capacity planning in mobile data networks experiencing exponential growth in demand” (April 2012). See slide 5, showing that 50% of all data traffic is generated in 1 cell, 80% of data traffic is carried in up to 3 cells, and only 20% of traffic can be regarded as truly mobile. The presentation has been viewed more than 19 thousand times.
Opanga, “The RAIN AI Platform”, provides a cognitive AI-based solution that addresses (1) Network Optimization lowering Capex demand and increasing the Customer Experience, (2) Energy Reduction above and beyond existing supplier solutions leading to further Opex efficiencies, and (3) Network Intelligence using AI to better manage your network data at a much higher resolution than is possible with classical dashboard applied to technology-driven data lakes.
“From an economic and customer experience standpoint, deploying stratospheric drones may be significantly more cost effective than establishing extra terrestrial infrastructures”.
As a mobile cellular industry expert and a techno-economist, the first time I was presented with the concept of stratospheric drones, I feel the butterflies in my belly. That tingling feeling that I was seeing something that could be a huge disruptor of how mobile cellular networks are being designed and built. Imagine getting rid of the profitability-challenged rural cellular networks (i.e., the towers, the energy consumption, the capital infrastructure investments), and, at the same time, offering much better quality to customers in rural areas than is possible with the existing cellular network we have deployed there. A technology that could fundamentally change the industry’s mobile cellular cost structure for the better at a quantum leap in quality and, in general, provide economical broadband services to the unconnected at a fraction of the cost of our traditional ways of building terrestrial cellular coverage.
Back in 2015, I got involved with Deutsche Telekom AG Group Technology, under the leadership of Bruno Jacobfeuerborn, in working out the detailed operational plans, deployment strategies, and, of course, the business case as well as general economics of building a stratospheric cellular coverage platform from scratch with the UK-based Stratospheric Platform Ltd [2] in which Deutsche Telekom is an investor. The investment thesis was really in the way we expected the stratospheric high-altitude platform to make a large part of mobile operators’ terrestrial rural cellular networks obsolete and how it might strengthen mobile operator footprints in countries where rural and remote coverage was either very weak or non-existing (e.g., The USA, an important market for Deutsche Telekom AG).
At the time, our thoughts were to have an operational stratospheric coverage platform operationally by 2025, 10 years after kicking off the program, with more than 100 high-altitude platforms covering a major Western European country serving rural areas. As it so often is, reality is unforgiving, as it often is with genuinely disruptive ideas. Getting to a stage of deployment and operation at scale of a high-altitude platform is still some years out due to the lack of maturity of the flight platform, including regulatory approvals for operating a HAP network at scale, increasing the operating window of the flight platform, fueling, technology challenges with the advanced antenna system, being allowed to deployed terrestrial-based cellular spectrum above terra firma, etc. Many of these challenges are progressing well, although slowly.
Globally, various companies are actively working on developing stratospheric drones to enhance cellular coverage. These include aerospace and defense giants like Airbus, advancing its Zephyr drone, and BAE Systems, collaborating with Prismatic for their PHASA-35 UAV. One of the most exciting HAPS companies focusing on developing world-leading high-altitude aircraft that I have come across during my planning work on how to operationalize a Stratospheric cellular coverage platform is the German company Leichtwerk AG, which has their hydrogen-fueled StratoStreamer as well as a solar-powered platform under development with the their StratoStreamer being close to production-ready. Telecom companies like Deutsche Telekom AG and BT Group are experimenting with hydrogen-powered drones in partnership with Stratospheric Platforms Limited. Through its subsidiary HAPSMobile, SoftBank is also a significant player with its Sunglider project. Additionally, entities like China Aerospace Science and Technology Corporation and Cambridge Consultants contribute to this field by co-developing enabling technologies (e.g., advanced phased-array antenna, fuel technologies, material science, …) critical for the success and deployability of high-altitude platforms at scale, aiming to improve connectivity in rural, remote, and underserved areas.
The work on integrating High Altitude Platform (HAP) networks with terrestrial cellular systems involves significant coordination with international regulatory bodies like the International Telecommunication Union Radiocommunication Sector (ITU-R) and the World Radiocommunication Conference (WRC). This process is crucial for securing permission to reuse terrestrial cellular spectrum in the stratosphere. Key focus areas include negotiating the allocation and management of frequency bands for HAP systems, ensuring they don’t interfere with terrestrial networks. These efforts are vital for successfully deploying and operating HAP systems, enabling them to provide enhanced connectivity globally, especially in rural areas where terrestrial cellular frequencies are already in use and remote and underserved regions. At the latest WRC-2023 conference, Softbank successfully gained approval within the Asia-Pacific region to use mobile spectrum bands for stratospheric drone-based mobile broadband cellular services.
Most mobile operators have at least 50% of their cellular network infrastructure assets in rural areas. While necessary for providing the coverage that mobile customers have come to expect everywhere, these sites carry only a fraction of the total mobile traffic. Individually, rural sites have poor financial returns due to their proportional operational and capital expenses.
In general, the Opex of the cellular network takes up between 50% and 60% of the Technology Opex, and at least 50% of that can be attributed to maintaining and operating the rural part of the radio access network. Capex is more cyclical than Opex due to, for example, the modernization of radio access technology. Nevertheless, over a typical modernization cycle (5 to 7 years), the rural network demands a little bit less but a similar share of Capex overall as for Opex. Typically, the Opex share of the rural cellular network may be around 10% of the corporate Opex, and its associated total cost is between 12% and 15% of the total expenses.
The global telecom towers market size in 2023 is estimated at ca. 26+ billion euros, ca. 2.5% of total telecom turnover, with a projected growth of CAGR 3.3% from now to 2030. The top 10 Tower management companies manage close to 1 million towers worldwide for mobile CSPs. Although many mobile operators have chosen to spin off their passive site infrastructure, there are still some remaining that may yet to spin off their cellular infrastructure to one of many Tower management companies, captive or independent, such as American Tower (224,019+ towers), Cellnex Telecom (112,737+ towers), Vantage Towers (46,100+ towers), GD Towers (+41,600 towers), etc…
IMAGINE.
Focusing on the low- or no-profitable rural cellular coverage.
Imagine an alternative coverage technology to the normal cellular one all mobile operators are using that would allow them to do without the costly and low-profitable rural cellular network they have today to satisfy their customers’ expectations of high-quality ubiquitous cellular coverage.
For the alternative technology to be attractive, it would need to deliver at least the same quality and capacity as the existing terrestrial-based cellular coverage for substantially better economics.
If a mobile operator with a 40% EBITDA margin did not need its rural cellular network, it could improve its margin by a sustainable 5% and increase its cash generation in relative terms by 50% (i.e., from 0.2×Revenue to 0.3×Revenue), assuming a capex-to-revenue ratio of 20% before implementing the technology being reduced to 15% after due to avoiding modernization and capacity investments in the rural areas.
Imagine that the alternative technology would provide a better cellular quality to the consumer for a quantum leap reduction of the associated cost structure compared to today’s cellular networks.
Such an alternative coverage technology might also impact the global tower companies’ absolute level of sustainable tower revenues, with a substantial proportion of revenue related to rural site infrastructure being at risk.
Figure 1 An example of an unmanned autonomous stratospheric coverage platform. Source: Cambridge Consultants presentation (see reference [2]) based on their work with Stratospheric Platforms Ltd (SPL) and SPL’s innovative high-altitude coverage platform.
TERRESTRIAL CELLULAR RURAL COVERAGE – A MATTER OF POOR ECONOMICS.
When considering the quality we experience in a terrestrial cellular network, a comprehensive understanding of various environmental and physical factors is crucial to predicting the signal quality accurately. All these factors generally work against cellular signal propagation regarding how far the signal can reach from the transmitting cellular tower and the achievable quality (e.g., signal strength) that a customer can experience from a cellular service.
Firstly, the terrain plays a significant role. Rural landscapes often include varied topographies such as hills, valleys, and flat plains, each affecting signal reach differently. For instance, hilly or mountainous areas may cause signal shadowing and reflection, while flat terrains might offer less obstruction, enabling signals to travel further.
At higher frequencies (i.e., above 1 GHz), vegetation becomes an increasingly critical factor to consider. Trees, forests, and other dense foliage can absorb and scatter radio waves, attenuating signals. The type and density of vegetation, along with seasonal changes like foliage density in summer versus winter, can significantly impact signal strength.
The height and placement of transmitting and receiving antennas are also vital considerations. In rural areas, where there are fewer tall buildings, the height of the antenna can have a pronounced effect on the line of sight and, consequently, on the signal coverage and quality. Elevated antennas mitigate the impact of terrain and vegetation to some extent.
Furthermore, the lower density of buildings in rural areas means fewer reflections and less multipath interference than in urban environments. However, larger structures, such as farm buildings or industrial facilities, must be factored in, as they can obstruct or reflect signals.
Finally, the distance between the transmitter and receiver is fundamental to signal propagation. With typically fewer cell towers spread over larger distances, understanding how signal strength diminishes with distance is critical to ensuring reliable coverage at a high quality, such as high cellular throughput, as the mobile customer expects.
The typical way for a cellular operator to mitigate the environmental and physical factors that inevitably result in loss of signal strength and reduced cellular quality (i.e., sub-standard cellular speed) is to build more sites and thus incur increasing Capex and Opex in areas that in general will have poor economical payback associated with any cellular assets. Thus, such investments make an already poor economic situation even worse as the rural cellular network generally would have very low utilization.
Figure 2 Cellular capacity or quality measured by the unit or total throughput is approximately driven by the amount of spectrum (in MHz) times the effective spectral efficiency (in Mbps/MHz/units) times the number of cells or capacity units deployed. When considering the effective spectral efficiency, one needs to consider the possible “boost” that a higher order MiMo or Advanced Antenna System will bring over and above the Single In Single Out (SISO) antenna would result in.
As our alternative technology also would need to provide at least the same quality and capacity it is worth exploring what can be expected in terms of rural terrestrial capacity. In general, we have that the cellular capacity (and quality) can be written as (also shown in Figure 2 above):
Throughput (in Mbps)= Spectral Bandwidth in MHz × Effective Spectral Efficiency in Mbps/MHz/Cell × Number of Cells
We need to keep in mind that an additional important factor when considering quality and capacity is that the higher the operational frequency, the lower the radius (all else being equal). Typically, we can improve the radius at higher frequencies by utilizing advanced antenna beam forming, that is, concentrate the radiated power per unit coverage area, which is why you will often hear that the 3.6 GHz downlink coverage radius is similar to that of 1800 MHz (or PCS). This 3.6 GHz vs. 1.8 GHz coverage radius comparison is made when not all else is equal. Comparing a situation where the 1800 MHz (or PCS) radiated power is spread out over the whole coverage area compared to a coverage situation where the 3.6 GHz (or C-band in general) solution makes use of beamforming, where the transmitted energy density is high, allowing to reach the customer at a range that would not be possible if the 3.6 GHz radiated power would have been spread out over the cell like the example of the 1800 MHz.
As an example, take an average Western European rural 5G site with all cellular bands between 700 and 2100 MHz activated. The site will have a total of 85 MHz DL and 75 MHz UL, with a 10 MHz difference between DL and UL due to band 38 Supplementary Downlink SDL) operational on the site. In our example, we will be optimistic and assume that the effective spectral efficiency is 2 Mbps per MHz per cell (average over all bands and antenna configurations), which would indicate a fair amount of 4×4 and 8×8 MiMo antenna systems deployed. Thus, the unit throughput we would expect to be supplied by the terrestrial rural cell would be 170 Mbps (i.e., 85 MHz × 2.0 Mbps/MHz/Cell). With a rural cell coverage radius between 2 and 3 km, we then have an average throughput per square kilometer of 9 Mbps/km2. Due to the low demand and high-frequency bandwidth per active customer, DL speeds exceeding 100+ Mbps should be relatively easy to sustain with 5G standalone, with uplink speeds being more compromised due to larger coverage areas. Obviously, the rural quality can be improved further by deploying advanced antenna systems and increasing the share of higher-order MiMo antennas in general, as well as increasing the rural site density. However, as already pointed out, this would not be an economically reasonable approach.
THE ADVANTAGE OF SEEING FROM ABOVE.
Figure 3 illustrates the difference between terrestrial cellular coverage from a cell tower and that of a stratospheric drone or high-altitude platform (“Antenna-in-the-Sky”). The benefit of seeing the world from above is that environmental and physical factors have substantially less impact on signal propagation and quality primarily being impacted by distance as it approximates free space propagation. This situation is very different for a terrestrial-based cellular tower with its radiated signal being substantially impacted by the environment as well as physical factors.
It may sound silly to talk about an alternative coverage technology that could replace the need for the cellular tower infrastructure that today is critical for providing mobile broadband coverage to, for example, rural areas. What alternative coverage technologies should we consider?
If, instead of relying on terrestrial-based tower infrastructure, we could move the cellular antenna and possibly the radio node itself to the sky, we would have a situation where most points of the ground would be in the line of sight to the “antenna-in-the-sky.” The antenna in the sky idea is a game changer in terms of coverage itself compared to conventional terrestrial cellular coverage, where environmental and physical factors dramatically reduce signal propagation and signal quality.
The key advantage of an antenna in the sky (AIS) is that the likelihood of a line-of-sight to a point on the ground is very high compared to establishing a line-of-sight for terrestrial cellular coverage that, in general, would be very low. In other words, the cellular signal propagation from an AIS closely approximates that of free space. Thus, all the various environmental signal loss factors we must consider for a standard terrestrial-based mobile network do not apply to our antenna in the sky.
Over the last ten years, we have gotten several technology candidates for our antenna-in-the-sky solution, aiming to provide terrestrial broadband services as a substitute, or enhancement, for terrestrial mobile and fixed broadband services. In the following, I will describe two distinct types of antenna-in-the-sky solutions: (a) Low Earth Orbit (LEO) satellites, operating between 500 to 2000 km above Earth, that provide terrestrial broadband services such as we know from Starlink (SpaceX), OneWeb (Eutelsat Group), and Kuiper (Amazon), and (b) So-called, High Altitude Platforms (HAPS), operating at altitudes between 15 to 30 km (i.e., in the stratosphere). Such platforms are still in the research and trial stages but are very promising technologies to substitute or enhance rural network broadband services. The HAP is supposed to be unmanned, highly autonomous, and ultimately operational in the stratosphere for an extended period (weeks to months), fueled by green hydrogen and possibly solar. The high-altitude platform is thus also an unmanned aerial vehicle (UAV), although I will use the term stratospheric drone and HAP interchangeably in the following.
Low Earth Orbit (LEO) satellites and High Altitude Platforms (HAPs) represent two distinct approaches to providing high-altitude communication and observation services. LEO satellites, operating between 500 km and 2,000 km above the Earth, orbit the planet, offering broad global coverage. The LEO satellite platform is ideal for applications like satellite broadband internet, Earth observation, and global positioning systems. However, deploying and maintaining these satellites involves complex, costly space missions and sophisticated ground control. Although, as SpaceX has demonstrated with the Starlink LEO satellite fixed broadband platform, the unitary economics of their satellites significantly improve by scale when the launch cost is also considered (i.e., number of satellites).
Figure 4 illustrates a non-terrestrial network architecture consisting of a Low Earth Orbit (LEO) satellite constellation providing fixed broadband services to terrestrial users. Each hexagon represents a satellite beam inside the larger satellite coverage area. Note that, in general, there will be some coverage overlap between individual satellites, ensuring a continuous service including interconnected satellites. The user terminal (UT) dynamically aligns itself, aiming at the best quality connection provided by the satellites within the UT field of vision.
Figure 4 Illustrating a Non-Terrestrial Network consisting of a Low Earth Orbit (LEO) satellite constellation providing fixed broadband services to terrestrial users (e.g., Starlink, Kuiper, OneWeb,…). Each hexagon represents a satellite beam inside the larger satellite coverage area. Note that, in general, there will be some coverage overlap between individual satellites, ensuring a continuous service. The operating altitude of a LEO satellite constellation is between 300 and 2,000 km. It is assumed that the satellites are interconnected, e.g., laser links. The User Terminal antenna (UT) is dynamically orienting itself after the best line-of-sight (in terms of signal quality) to a satellite within UT’s field-of-view (FoV). The FoV has not been shown in the picture above so as not to overcomplicate the illustration. It should be noted just like with the drone it is possible to integrate the complete gNB on the LEO satellite. There might even be applications (e.g., defense, natural & unnatural disaster situations, …) where a standalone 5G SA core is integrated.
On the other hand, HAPs, such as unmanned (autonomous) stratospheric drones, operate at altitudes of approximately 15 km to 30 km in the stratosphere. Unlike LEO satellites, the stratospheric drone can hover or move slowly over specific areas, often geostationary relative to the Earth’s surface. This characteristic makes them more suitable for localized coverage tasks like regional broadband, surveillance, and environmental monitoring. The deployment and maintenance of the stratospheric drones are managed from the Earth’s surface and do not require space launch capabilities. Furthermore, enhancing and upgrading the HAPs is straightforward, as they will regularly be on the ground for fueling and maintenance. Upgrades are not possible with an operational LEO satellite solution where any upgrade would have to wait on a subsequent generation and new launch.
Figure 5 illustrates the high-level network architecture of an unmanned autonomous stratospheric drone-based constellation providing terrestrial cellular broadband services to terrestrial mobile users delivered to their normal 5G terminal equipment. Each hexagon represents a beam arising from the phased-array antenna integrated into the drone’s wingspan. To deliver very high-availability services to a rural area, one could assign three HAPs to cover a given area. The drone-based non-terrestrial network is drawn consistent with the architectural radio access network (RAN) elements from Open RAN, e.g., Radio Unit (RU), Distributed Unit (DU), and Central Unit (CU). It should be noted that the whole 5G gNB (the 5G NodeB), including the CU, could be integrated into the stratospheric drone, and in fact, so could the 5G standalone (SA) packet core, enabling full private mobile 5G networks for defense and disaster scenarios or providing coverage in very remote areas with little possibility of ground-based infrastructure (e.g., the arctic region, or desert and mountainous areas).
Figure 5 illustrates a Non-Terrestrial Network consisting of a stratospheric High Altitude Platform (HAP) drone-based constellation providing terrestrial Cellular broadband services to terrestrial mobile users delivered to their normal 5G terminal equipment. Each hexagon represents a beam inside the larger coverage area of the stratospheric drone. To deliver very high-availability services to a rural area, one could assign three HAPs to cover a given area. The operating altitude of a HAP constellation is between 10 to 50 km with an optimum of around 20 km. It is assumed that there is inter-HAP connectivity, e.g., via laser links. Of course, it is also possible to contemplate having the gNB (full 5G radio node) in the stratospheric drone entirely, which would allow easier integration with LEO satellite backhauls, for example. There might even be applications (e.g., defense, natural & unnatural disaster situations, …) where a standalone 5G SA core is integrated.
The unique advantage of the HAP operating in the stratosphere is (1) The altitude is advantageous for providing wider-area cellular coverage with a near-ideal quality above and beyond what is possible with conventional terrestrial-based cellular coverage because of very high line-of-sight likelihood due to less environment and physical issues that substantially reduces the signal propagation and quality of a terrestrial coverage solution, and (2) More stable atmospheric conditions characterize the stratosphere compared to the troposphere below it. This stability allows the stratospheric drone to maintain a consistent position and altitude with less energy expenditure. The stratosphere offers more consistent and direct sunlight exposure for a solar-powered HAP with less atmospheric attenuation. Moreover, due to the thinner atmosphere at stratospheric altitudes, the stratospheric drone will experience a lower air resistance (drag), increasing the energy efficiency and, therefore, increasing the operational airtime.
Figure 6 illustrates Leichtwerk AG’s StratoStreamer HAP design that is near-production ready. Leichtwerk AG works closely together with AESA towards the type certificate that would make it possible to operationalize a drone constellation in Europe. The StratoStreamer has a wingspan of 65 meter and can carry a payload of 100+ kg. Courtesy: Leichtwerk AG.
Each of these solutions has its unique advantages and limitations. LEO satellites provide extensive coverage but come with higher operational complexities and costs. HAPs offer more focused coverage and are easier to manage, but they need the global reach of LEO satellites. The choice between these two depends on the specific requirements of the intended application, including coverage area, budget, and infrastructure capabilities.
In an era where digital connectivity is indispensable, stratospheric drones could emerge as a game-changing technology. These unmanned (autonomous) drones, operating in the stratosphere, offer unique operational and economic advantages over terrestrial networks and are even seen as competitive alternatives to low earth orbit (LEO) satellite networks like Starlink or OneWeb.
STRATOSPHERIC DRONES VS TERRESTRIAL NETWORKS.
Stratospheric drones positioned much closer to the Earth’s surface than satellites, provide distinct signal strength and latency benefits. The HAP’s vantage point in the stratosphere (around 20 km above the Earth) ensures a high probability of line-of-sight with terrestrial user devices, mitigating the adverse effects of terrain obstacles that frequently challenge ground-based networks. This capability is particularly beneficial in rural areas in general and mountainous or densely forested areas, where conventional cellular towers struggle to provide consistent coverage.
Why the stratosphere? The stratosphere is the layer of Earth’s atmosphere located above the troposphere, which is the layer where weather occurs. The stratosphere is generally characterized by stable, dry conditions with very little water vapor and minimal horizontal winds. It is also home to the ozone layer, which absorbs and filters out most of the Sun’s harmful ultraviolet radiation. It is also above the altitude of commercial air traffic, which typically flies at altitudes ranging from approximately 9 to 12 kilometers (30,000 to 40,000 feet). These conditions (in addition to those mentioned above) make operating a stratospheric platform very advantageous.
Figure 6 illustrates the coverage fundamentals of (a) a terrestrial cellular radio network with the signal strength and quality degrading increasingly as one moves away from the antenna and (b) the terrestrial coverage from a stratospheric drone (antenna in the sky) flying at an altitude of 15 to 30 km. The stratospheric drone, also called a High-Altitude Platform (HAP), provides near-ideal signal strength and quality due to direct line-of-sight (LoS) with the ground, compared to the signal and quality from a terrestrial cellular site that is influenced by its environment and physical factors and the fact that LoS is much less likely in a conventional terrestrial cellular network. It is worth keeping in mind that the coverage scenarios where a stratospheric drone and a low earth satellite may excel in particular are in rural areas and outdoor coverage in more dense urban areas. In urban areas, the clutter, or environmental features and objects, will make line-of-site more challenging, impacting the strength and quality of the radio signals.
Figure 6 The chart above illustrates the coverage fundamentals of (a) a terrestrial cellular radio network with the signal strength and quality degrading increasingly as one moves away from the antenna and (b) the terrestrial coverage from a stratospheric drone (antenna in the sky) flying at an altitude of 15 to 30 km. The stratospheric drone, also called a High Altitude Platform (HAP), provides near-ideal signal strength and quality due to direct line-of-sight (LoS) with the ground, compared to the signal & quality from a terrestrial cellular site that is influenced by its environment and physical factors and the fact that LoS is much less likely in a conventional terrestrial cellular network.
From an economic and customer experience standpoint, deploying stratospheric drones may be significantly more cost-effective than establishing extensive terrestrial infrastructure, especially in remote or rural areas. The setup and operational costs of cellular towers, including land acquisition, construction, and maintenance, are substantially higher compared to the deployment of stratospheric drones. These aerial platforms, once airborne, can cover vast geographical areas, potentially rendering numerous terrestrial towers redundant. At an operating height of 20 km, one would expect a coverage radius ranging from 20 km up to 500 km, depending on the antenna system, application, and business model (e.g., terrestrial broadband services, surveillance, environmental monitoring, …).
The stratospheric drone-based coverage platform, and by platform, I mean the complete infrastructure that will replace the terrestrial cellular network, will consist of unmanned autonomous drones with a considerable wingspan (e.g., 747-like of ca. 69 meters). For example, European (German) Leichtwerk’s StratoStreamer has a wingspan of 65 meters and a wing area of 197 square meters with a payload of 120+ kg (note: in comparison a Boing 747 has ca. 500+ m2 wing area but its payload is obviously much much higher and in the range of 50 to 60 metric tons). Leichtwerk AG work closely together with AESA in order to achieve the European Union Aviation Safety Agency (EASA) type certificate that would allow the HAPS to integrate into civil airspace (see refs. [34] for what that means).
An advanced antenna system is positioned under the wings (or the belly) of the drone. I will assume that the coverage radius provided by a single drone is 50 km, but it can dynamically be made smaller or larger depending on the coverage scenario and use case. The drone-based advanced antenna system breaks up the coverage area (ca. six thousand five hundred plus square kilometers) into 400 patches (i.e., a number that can be increased substantially), averaging approx. 16 km2 per patch and a radius of ca. 2.5 km. Due to its near-ideal cellular link budget, the effective spectral efficiency is expected to be initially around 6 Mbps per MHz per cell. Additionally, the drone does not have the same spectrum limitations as a rural terrestrial site and would be able to support frequency bands in the downlink from ~900 MHz up to 3.9 GHz (and possibly higher, although likely with different antenna designs). Due to the HAP altitude, the Earth-to-HAP uplink signal will be limited to a lower frequency spectrum to ensure good signal quality is being received at the stratospheric antenna. It is prudent to assume a limit of 2.1 GHz to possibly 2.6 GHz. All under the assumption that the stratospheric drone operator has achieved regulatory approval for operating the terrestrial cellular spectrum from their coverage platform. It should be noted that today, cellular frequency spectrum approved for terrestrial use cannot be used at an altitude unless regulatory permission has been given (more on this later).
Let’s look at an example. We would need ca. 46 drones to cover the whole of Germany with the above-assumed specifications. Furthermore, if we take the average spectrum portfolio of the 3 main German operators, this will imply that the stratospheric drone could be functioning with up to 145 MHz in downlink and at least 55 MHz uplink (i.e., limiting UL to include 2.1 GHz). Using the HAP DL spectral efficiency and coverage area we get a throughput density of 70+ Mbps/km2 and an effective rural cell throughput of 870 Mbps. In terrestrial-based cellular coverage, the contribution to quality at higher frequencies is rapidly degrading as a function of the distance to the antenna. This is not the case for HAP-based coverage due to its near-ideal signal propagation.
In comparison, the three incumbent German operators have on average ca. 30±4k sites per operator with an average terrestrial coverage area of 12 km2 and a coverage radius of ca. 2.0 km (i.e., smaller in cities, ~1.3 km, larger in rural areas, ~2.7 km). Assume that the average cost of ownership related only to the passive part of the site is 20+ thousand euros and that 50% of the 30k sites (expect a higher number) would be redundant as the rural coverage would be replaced by stratospheric drones. Such a site reduction quantum conservatively would lead to a minimum gross monetary reduction of 300 million euros annually (not considering the cost of the alternative technology coverage solution).
In our example, the question is whether we can operate a stratospheric drone-based platform covering rural Germany for less than 300 million euros yearly. Let’s examine this question. Say the stratospheric drone price is 1 million euros per piece (similar to the current Starlink satellite price, excluding the launch cost, which would add another 1.1 million euros to the satellite cost). For redundancy and availability purposes, we assume we need 100 stratospheric drones to cover rural Germany, allowing me to decommission in the radius of 15 thousand rural terrestrial sites. The decommissioning cost and economical right timing of tower contract termination need to be considered. Due to the standard long-term contracts may be 5 (optimistic) to 10+ years (realistic) year before the rural network termination could be completed. Many Telecom businesses that have spun out their passive site infrastructure have done so in mutual captivity with the Tower management company and may have committed to very “sticky” contracts that have very little flexibility in terms of site termination at scale (e.g., 2% annually allowed over total portfolio).
We have a capital expense of 100 million for the stratospheric drones. We also have to establish the support infrastructure (e.g., ground stations, airfield suitability rework, development, …), and consider operational expenses. The ballpark figure for this cost would be around 100 million euros for Capex for establishing the supporting infrastructure and another 30 million euros in annual operational expenses. In terms of steady-state Capex, it should be at most 20 million per year. In our example, the terrestrial rural network would have cost 3 billion euros, mainly Opex, over ten years compared to 700 million euros, a little less than half as Opex, for the stratospheric drone-based platform (not considering inflation).
The economical requirements of a stratospheric unmanned and autonomous drone-based coverage platform should be superior compared to the current cellular terrestrial coverage platform. As the stratospheric coverage platform scales and increasingly more stratospheric drones are deployed, the unit price is also likely to reduce accordingly.
Spectrum usage rights yet another critical piece.
It should be emphasized that the deployment of cellular frequency spectrum in stratospheric and LEO satellite contexts is governed by a combination of technical feasibility, regulatory frameworks, coordination to prevent interference, and operational needs. The ITU, along with national regulatory bodies, plays a central role in deciding the operational possibilities and balancing the needs and concerns of various stakeholders, including satellite operators, terrestrial network providers, and other spectrum users. Today, there are many restrictions and direct regulatory prohibitions in repurposing terrestrially assigned cellular frequencies for non-terrestrial purposes.
The role of the World Radiocommunications Conference (WRC) role is pivotal in managing the global radio-frequency spectrum and satellite orbits. Its decisions directly impact the development and deployment of various radiocommunication services worldwide, ensuring their efficient operation and preventing interference across borders. The WRC’s work is fundamental to the smooth functioning of global communication networks, from television and radio broadcasting to cellular networks and satellite-based services. The WRC is typically held every three to four years, with the latest one, WRC-23, held in Dubai at the end of 2023, reference [13] provides the provisional final acts of WRC-23 (December 2023). In landmark recommendation, WRC-23 relaxed the terrestrial-only conditions for the 698 to 960 MHz and 1,71 to 2.17 GHz, and 2.5 to 2.69 GHz frequency bands to also apply for high-altitude platform stations (HAPS) base stations (“Antennas-in -Sky”). It should be noted that there are slightly different frequency band ranges and conditions, depending on which of the three ITU-R regions (as well as exceptions for particular countries within a region) the system will be deployed in. Also the HAPS systems do not enjoy protection or priority over existing use of those frequency bands terrestrially. It is important to note that the WRC-23 recommendation only apply to coverage platforms (i.e., HAPS) in the range from 20 to 50 km altitude. These WRC-23 frequency-bands relaxation does not apply to satellite operation. With the recognized importance of non-terrestrial networks and the current standardization efforts (e.g., towards 6G), it is expected that the fairly restrictive regime on terrestrial cellular spectrum may be relaxed further to also allow mobile terrestrial spectrum to be used in “Antenna-in-the-Sky” coverage platforms. Nevertheless, HAPS and terrestrial use of cellular frequency spectrum will have to be coordinated to avoid interference and resulting capacity and quality degradation.
SoftBank announced recently (i.e., 28 December 2023 [11]), after deliberations at the WRC-23, that they had successfully gained approval within the Asia-Pacific region (i.e., ITU-R region 3) to use mobile spectrum bands, namely 700-900MHz, 1.7GHz, and 2.5GHz, for stratospheric drone-based mobile broadband cellular services (see also refs. [13]). As a result of this decision, operators in different countries and regions will be able to choose a spectrum with greater flexibility when they introduce HAPS-based mobile broadband communication services, thereby enabling seamless usage with existing smartphones and other devices.
Another example of re-using terrestrial licensed cellular spectrum above ground is SpaceX direct-to-cell capable 2nd generation Starlink satellites.
On January 2nd, 2024, SpaceX launched their new generation of Starlink satellites with direct-to-cell capabilities to close a connection to a regular mobile cellular phone (e.g., smartphone). The new direct-to-cell Starlink satellites use T-Mobile US terrestrial licensed cellular frequency band (i.e., 2×5 MHz Band 25, PCS G-block) and will work, according to T-Mobile US, with most of their existing mobile phones. The initial direct-to-cell commercial plans will only support low-bandwidth text messaging and no voice or more bandwidth-heavy applications (e.g., streaming). Expectations are that the direct-to-cell system would deliver up to 18.3 Mbps (3.66 Mbps/MHz/cell) downlink and up to 7.2 Mbps (1.44 Mbps/MHz/cell) uplink over a channel bandwidth of 5 MHz (maximum).
Given that terrestrial 4G LTE systems struggle with such performance, it will be super interesting to see what the actual performance of the direct-to-cell satellite constellation will be.
COMPARISON WITH LEO SATELLITE BROADBAND NETWORKS.
When juxtaposed with LEO satellite networks such as Starlink (SpaceX), OneWeb (Eutelsat Group), or Kuiper (Amazon), stratospheric drones offer several advantages. Firstly, the proximity to the Earth’s surface (i.e., 300 – 2,000 km) results in lower latency, a critical factor for real-time applications. While LEO satellites, like those used by Starlink, have reduced latency (ca. 3 ms round-trip-time) compared to traditional geostationary satellites (ca. 240 ms round-trip-time), stratospheric drones can provide even quicker response times (one-tenth of an ms in round-trip-time), making the stratospheric drone substantially more beneficial for applications such as emergency services, telemedicine, and high-speed internet services.
A stratospheric platform operating at 20 km altitude and targeting surveillance, all else being equal, would be 25 times better at distinguishing objects apart than an LEO satellite operating at 500 km altitude. The global aerial imaging market is expected to exceed 7 billion euros by 2030, with a CAGR of 14.2% from 2021. The flexibility of the stratospheric drone platform allows for combining cellular broadband services and a wide range of advanced aerial imaging services. Again, it is advantageous that the stratospheric drone regularly returns to Earth for fueling, maintenance, and technology upgrades and enhancements. This is not possible with an LEO satellite platform.
Moreover, the deployment and maintenance of stratospheric drones are, in theory, less complex and costly than launching and maintaining a constellation of satellites. While Starlink and similar projects require significant upfront investment for satellite manufacturing and rocket launches, stratospheric drones can be deployed at a fraction of the cost, making them a more economically viable option for many applications.
The Starlink LEO satellite constellation currently is the most comprehensive satellite (fixed) broadband coverage service. As of November 2023, Starlink had more than 5,000 satellites in low orbit (i.e., ca. 550 km altitude), and an additional 7,000+ are planned to be deployed, with a total target of 12+ thousand satellites. The current generation of Starlink satellites has three downlink phased-array antennas and one uplink phase-array antenna. This specification translates into 48 beams downlink (satellite to ground) and 16 beams uplink (ground to satellite). Each Starlink beam covers approx. 2,800 km2 with a coverage range of ca. 30 km, over which a 250 MHz downlink channel (in the Ku band) has been assigned. According to Portillo et al. [14], the spectral efficiency is estimated to be 2.7 Mbps per MHz, providing a total throughput of a maximum of 675 Mbps in the coverage area or a throughput density of ca. 0.24 Mbps per km2.
According to the latest Q2-2023 Ookla speed test it is found that “among the 27 European countries that were surveyed, Starlink had median download speeds greater than 100 Mbps in 14 countries, greater than 90 Mbps in 20 countries, and greater than 80 in 24 countries, with only three countries failing to reach 70 Mbps” (see reference [18]). Of course, the actual customer experience will depend on the number of concurrent users demanding resources from the LEO satellite as well as weather conditions, proximity of other users, etc. Starlink themselves seem to have set an upper limit of 220 Mbps download speed for their so-called priority service plan or otherwise 100 Mbps (see [19] below). Quite impressive performance if there are no other broadband alternatives available.
According to Elon Musk, SpaceX aims to reduce each Starlink satellite’s cost to less than one million euros. However, according to Elon Musk, the unit price will depend on the design, capabilities, and production volume. The launch cost using the SpaceX Falcon 9 launch vehicle starts at around 57 million euros, and thus, the 50 satellites would add a launch cost of ca. 1.1 million euros per satellite. SpaceX operates, as of September 2023, 150 ground stations (“Starlink Gateways”) globally that continue to connect the satellite network with the internet and ground operations. At Starlink’s operational altitude, the estimated satellite lifetime is between 5 and 7 years due to orbital decay, fuel and propulsion system exhaustion, and component durability. Thus, a LEO satellite business must plan for satellite replacement cycles. This situation differs greatly from the stratospheric drone-based operation, where the vehicles can be continuously maintained and upgraded. Thus, they are significantly more durable, with an expected useful lifetime exceeding ten years and possibly even 20 years of operational use.
Let’s consider our example of Germany and what it would take to provide LEO satellite coverage service targeting rural areas. It is important to understand that a LEO satellite travels at very high speeds (e.g., upwards of 30 thousand km per hour) and thus completes an orbit around Earth in between 90 to 120 minutes (depending on the satellite’s altitude). It is even more important to remember that Earth rotates on its axis (i.e., 24 hours for a full rotation), and the targeted coverage area will have moved compared to a given satellite orbit (this can easily be several hundreds to thousands of kilometers). Thus, to ensure continuous satellite broadband coverage of the same area on Earth, we need a certain number of satellites in a particular orbit and several orbits to ensure continuous coverage at a target area on Earth. We would need at least 210 satellites to provide continuous coverage of Germany. Most of the time, most satellites would not cover Germany, and the operational satellite utilization will be very low unless other areas outside Germany are also being serviced.
Economically, using the Starlink numbers above as a guide, we incur a capital expense of upwards of 450 million euros to realize a satellite constellation that could cover Germany. Let’s also assume that the LEO satellite broadband operator (e.g., Starlink) must build and launch 20 satellites annually to maintain its constellation and thus incur an additional Capex of ca. 40+ million euros annually. This amount does not account for the Capex required to build the ground network and the operations center. Let’s say all the rest requires an additional 10 million euros Capex to realize and for miscellaneous going forward. The technology-related operational expenses should be low, at most 30 million euros annually (this is a guesstimate!) and likely less. So, covering Germany with an LEO broadband satellite platform over ten years would cost ca. 1.3 billion euros. Although substantially more costly than our stratospheric drone platform, it is still less costly than running a rural terrestrial mobile broadband network.
Despite being favorable compared in economic to the terrestrial cellular network, it is highly unlikely to make any operational and economic sense for a single operator to finance such a network, and it would probably only make sense if shared between telecom operators in a country and even more so over multiple countries or states (e.g., European Union, United States, PRC, …).
Despite the implied silliness of a single mobile operator deploying a satellite constellation for a single Western European country (irrespective of it being fairly large), the above example serves two purposes; (1) To illustrates how economically in-efficient rural mobile networks are that a fairly expansive satellite constellation could be more favorable. Keep in mind that most countries have 3 or 4 of them, and (2) It also shows that the for operators to share the economics of a LEO satellite constellation over larger areal footprint may make such a strategy very attractive economically,
Due to the path loss at 550 km (LEO) being substantially higher than at 20 km (stratosphere), all else being equal, the signal quality of the stratospheric broadband drone would be significantly better than that of the LEO satellite. However, designing the LEO satellite with more powerful transmitters and sensitive receivers can compensate for the factor of almost 30 in altitude difference to a certain extent. Clearly, the latency performance of the LEO satellite constellation would be inferior to that of the stratospheric drone-based platform due to the significantly higher operating altitude.
It is, however, the capacity rather than shared cost could be the stumbling block for LEOs: For a rural cellular network or stratospheric drone platform, we see the MNOs effectively having “control” over the capex costs of the network, whether it be the RAN element for a terrestrial network, or the cost of whole drone network (even if it in the future, this might be able to become a shared cost).
However, for the LEO constellation, we think the economics of a single MNO building a LEO constellation even for their own market is almost entirely out of the question (ie multiple €bn capex outlay). Hence, in this situation, the MNOs will rely on a global LEO provider (ie Starlink, or AST Space Mobile) and will “lend” their spectrum to their in their respective geography in order to provide service. Like the HAPs, this will also require further regulatory approvals in order to free up terrestrial spectrum for satellites in rural areas.
We do not yet have the visibility of the payments the LEOs will require, so there is the potential that this could be a lower cost alternative again to rural networks, but as we show below, we think the real limitation for LEOs might not be the shared capacity rental cost, but that there simply won’t be enough capacity available to replicate what a terrestrial network can offer today.
However, the stratospheric drone-based platform provides a near-ideal cellular performance to the consumer, close to the theoretical peak performance of a terrestrial cellular network. It should be emphasized that the theoretical peak cellular performance is typically only experienced, if at all, by consumers if they are very near the terrestrial cellular antenna and in a near free-space propagation environment. This situation is a very rare occurrence for the vast majority of mobile consumers.
Figure 7 summarizes the above comparison between a rural terrestrial cellular network with the non-terrestrial cellular networks such as LEO satellites and Stratospheric drones.
Figure 7 Illustrating a comparison between terrestrial cellular coverage with stratospheric drone-based (“Antenna-in-the-sky”) cellular coverage and Low Earth Orbit (LEO) satellite coverage options.
While the majority of the 5,500+ Starlink constellation is 13 GHz (Ku-band), at the beginning of 2024, Space X launched a few 2nd generation Starlink satellites that support direct connections from the satellite to a normal cellular device (e.g., smartphone), using 5 MHz of T-Mobile USA’s PCS band (1900 MHz). The targeted consumer service, as expressed by T-Mobile USA, is providing texting capabilities over areas with no or poor existing cellular coverage across the USA. This is fairly similar to services at similar cellular coverage areas presently offered by, for example, AST SpaceMobile, OmniSpace, and Lynk Global LEO satellite services with reported maximum speed approaching 20 Mbps. The so-called Direct-2-Device, where the device is a normal smartphone without satellite connectivity functionality, is expected to develop rapidly over the next 10 years and continue to increase the supported user speeds (i.e., utilized terrestrial cellular spectrum) and system capacity in terms of smaller coverage areas and higher number of satellite beams.
Table 1 below provides an overview of the top 10 LEO satellite constellations targeting (fixed) internet services (e.g., Ku band), IoT and M2M services, and Direct-to-Device (or direct-to-cell) services. The data has been compiled from the NewSpace Index website, which should be with data as of 31st of December 2023. The Top-10 satellite constellation rank has been based on the number of launched satellites until the end of 2023. Two additional Direct-2-Cell (D2C or Direct-to-Device, D2D) LEO satellite constellations are planned for 2024-2025. One is SpaceX Starlink 2nd generation, which launched at the beginning of 2024, using T-Mobile USA’s PCS Band to connect (D2D) to normal terrestrial cellular handsets. The other D2D (D2C) service is Inmarsat’s Orchestra satellite constellation based on L-band (for mobile terrestrial services) and Ka for fixed broadband services. One new constellation (Mangata Networks) targeting 5G services. With two 5G constellations already launched, i.e., Galaxy Space (Yinhe) launched 8 LEO satellites, 1,000 planned using Q- and V-bands (i.e., not a D2D cellular 5G service), and OmniSpace launched two satellites and have planned 200 in total. Moreover, currently, there is one planned constellation targeting 6G by the South Korean Hanwha Group (a bit premature, but interesting nevertheless) with 2,000 6G LEO Satellites planned. Most currently launched and planned satellite constellations offering (or plan to provide) Direct-2-Cell services, including IoT and M2M, are designed for low-frequency bandwidth services that are unlikely to compete with terrestrial cellular networks’ quality of service where reasonable good coverage (or better) exists.
In Table 1 below, we then show 5 different services with the key input variables as cell radius, spectral efficiency and downlink spectrum. From this we can derive what the “average” capacity could be per square kilometer of rural coverage.
We focus on this metric as the best measure of capacity available once multiple users are on the service the spectrum available is shared. This is different from “peak” speeds which are only relevant in the case of very few users per cell.
We start with terrestrial cellular today for bands up to 2.1GHz and show that assuming a 2.5km cell radius, the average capacity is equivalent to 11Mbps per sq.km.
For a LEO service using Ku-band, i.e., with 250MHz to an FWA dish, the capacity could be ca. 2Mbps per sq.km.
For a LEO-based D2D device, what is unknown is what the ultimate spectrum allowance could be for satellite services with cellular spectrum bands, and spectral efficiency. Giving the benefit of the doubt on both, but assuming the beam radius is always going to be larger, we can get to an “optimistic” future target of 2Mbps per sq. km, i.e., 1/5th of a rural terrestrial network.
Finally, we show for a stratospheric drone, that given similar cell radius to a rural cell today, but with higher downlink available and greater spectral efficiency, we can reach ca. 55Mbps per sq. km, i.e. 5x what a current rural network can offer.
INTEGRATING WITH 5G AND BEYOND.
The advent of 5G, and eventually 6G, technology brings another dimension to the utility of stratospheric drones delivering mobile broadband services. The high-altitude platform’s ability to seamlessly integrate with existing 5G networks makes them an attractive option for expanding coverage and enhancing network capacity at superior economics, particularly in rural areas where the economics for terrestrial-based cellular coverage tend to be poor. Unlike terrestrial networks that require extensive groundwork for 5G rollout, the non-terrestrial network operator (NTNO) can rapidly deploy stratospheric drones to provide immediate 5G coverage over large areas. The high-altitude platform is also incredibly flexible compared to both LEO satellite constellations and conventional rural cellular network flexibility. The platform can easily be upgraded during its ground maintenance window and can be enhanced as the technology evolves. For example, upgrading to and operationalizing 6G would be far more economical with a stratospheric platform than having to visit thousands or more rural sites to modernize or upgrade the installed active infrastructure.
SUMMARY.
Stratospheric drones represent a significant advancement in the realm of wireless communication. Their strategic positioning in the stratosphere offers superior coverage and connectivity compared to terrestrial networks and low-earth satellite solutions. At the same time, their economic efficiency makes them an attractive alternative to ground-based infrastructures and LEO satellite systems. As technology continues to evolve, these high-altitude platforms (HAPs) are poised to play a crucial role in shaping the future of global broadband connectivity and ultra-high availability connectivity solutions, complementing the burgeoning 5G networks and paving the way for next-generation three-dimensional communication solutions. Moving away from today’s flat-earth terrestrial-locked communication platforms.
The strategic as well as the disruptive potential of the unmanned autonomous stratospheric terrestrial coverage platform is enormous, as shown in this article. It has the potential to make most of the rural (at least) cellular infrastructure redundant, resulting in substantial operational and economic benefits to existing mobile operators. At the same time, the HAPs could, in rural areas, provide much better service overall in terms of availability, improved coverage, and near-ideal speeds compared to what is the case in today’s cellular networks. It might also, at scale, become a serious competitive and economical threat to LEO satellite constellations, such as, for example, Starlink and Kuipers, that would struggle to compete on service quality and capacity compared to a stratospheric coverage platform.
Although the strategic, economic, as well as disruptive potential of the unmanned autonomous stratospheric terrestrial coverage platform is enormous, as shown in this article, the flight platform and advanced antenna technology are still in a relatively early development phase. Substantial regulatory work remains in terms of permitting the terrestrial cellular spectrum to be re-used above terra firma at the “Antenna-in-the-Sky. The latest developments out of WRC-23 for Asia Pacific appear very promising, showing that we are moving in the right direction of re-using terrestrial cellular spectrum in high-altitude coverage platforms. Last but not least, operating an unmanned (autonomous) stratospheric platform involves obtaining certifications as well as permissions and complying with various flight regulations at both national and international levels.
Terrestrial Mobile Broadband Network – takeaway:
It is the de facto practice for mobile cellular networks to cover nearly 100% geographically. The mobile consumer expects a high-quality, high-availability service everywhere.
A terrestrial mobile network has a relatively low area coverage per unit antenna with relatively high capacity and quality.
Mobile operators incur high and sustainable infrastructure costs, especially in rural areas with low or no return on that cost.
Physical obstructions and terrain limit performance (i.e., non-free space characteristics).
Well-established technology with high reliability.
The potential for high bandwidth and low latency in urban areas with high demand may become a limiting factor for LEO satellite constellations and stratospheric drone-based platforms. Thus, it is less likely to provide operational and economic benefits covering high-demand, dense urban, and urban areas.
LEO Satellite Network – takeaway:
The technology is operational and improving. There is currently some competition (e.g., Starlink, Kuiper, OneWeb, etc.) in this space, primarily targeting fixed broadband and satellite backhaul services. Increasingly, new LEO satellite-based business models are launched providing lower-bandwidth cellular-spectrum based direct-to-device (D2D) text, 4G and 5G services to regular consumer and IoT devices (i.e., Starlink, Lynk Global, AST SpaceMobile, OmniSpace, …).
Broader coverage, suitable for global reach. It may only make sense when the business model is viewed from a worldwide reach perspective (e.g., Starlink, OneWeb,…), resulting in much-increased satellite network utilization.
An LEO satellite broadband network can cover a vast area per satellite due to its high altitude. However, such systems are in nature capacity-limited, although beam-forming antenna technologies (e.g., phased array antennas) allow better capacity utilization.
The LEO satellite solutions are best suited for low-population areas with limited demand, such as rural and largely unpopulated areas (e.g., sea areas, deserts, coastlines, Greenland, polar areas, etc.).
Much higher latency compared to terrestrial and drone-based networks.
Less flexible once in orbit. Upgrades and modernization only via replacement.
The LEO satellite has a limited useful operational lifetime due to its lower orbital altitude (e.g., 5 to 7 years).
Lower infrastructure cost for rural coverage compared to terrestrial networks, but substantially higher than drones when targeting regional areas (e.g., Germany or individual countries in general).
Complementary to the existing mobile business model of communications service providers (CSPs) with a substantial business risk to CSPs in low-population areas where little to no capacity limitations may occur.
Requires regulatory permission (authorization) to operate terrestrial frequencies on the satellite platform over any given country. This process is overseen by national regulatory bodies in coordination with the International Telecommunication Union (ITU) as well as national regulators (e.g., FCC in the USA). Satellite operators must apply for frequency bands for uplink and downlink communications and coordinate with the ITU to avoid interference with other satellites and terrestrial systems. In recent years, however, there has been a trend towards more flexible spectrum regulations, allowing for innovative uses of the spectrum like integrating terrestrial and satellite services. This flexibility is crucial in accommodating new technologies and service models.
Operating a LEO satellite constellation requires a comprehensive set of permissions and certifications that encompass international and national space regulations, frequency allocation, launch authorization, adherence to space debris mitigation guidelines, and various liability and insurance requirements.
Both LEO and MEO satellites is likely going to be complementary or supplementary to stratospheric drone-based broadband cellular networks offering high-performing transport solutions and possible even acts as standalone or integrated (with terrestrial networks) 5G core networks or “clouds-in-the-sky”.
Stratospheric Drone-Based Network – takeaway:
It is an emerging technology with ongoing research, trials, and proof of concept.
A stratospheric drone-based broadband network will have lower deployment costs than terrestrial and LEO satellite broadband networks.
In rural areas, the stratospheric drone-based broadband network offers better economics and near-ideal quality than terrestrial mobile networks. In terms of cell size and capacity, it can easily match that of a rural mobile network.
The solution offers flexibility and versatility and can be geographically repositioned as needed. The versatility provides a much broader business model than “just” an alternative rural coverage solution (e.g., aerial imaging, surveillance, defense scenarios, disaster area support, etc.).
Reduced latency compared to LEO satellites.
Also ideal for targeted or temporary coverage needs.
Complementary to the existing mobile business model of communications service providers (CSPs) with additional B2B and public services business potential from its application versatility.
Potential substantial negative impact on the telecom tower business as the stratospheric drone-based broadband network would make (at least) rural terrestrial towers redundant.
May disrupt a substantial part of the LEO satellite business model due to better service quality and capacity leaving the LEO satellite constellations revenue pool to remote areas and specialized use cases.
Requires regulatory permission to operate terrestrial frequencies (i.e., frequency authorization) on the stratospheric drone platform (similar to LEO satellites). Big steps have are already been made at the latest WRC-23, where the frequency bands 698 to 960 MHz, 1710 to 2170 MHz, and 2500 to 2690 MHz has been relaxed to allow for use in HAPS operating at 20 to 50 km altitude (i.e., the stratosphere).
Operating a stratospheric platform in European airspace involves obtaining certifications as well as permissions and (of course) complying with various regulations at both national and international levels. This includes the European Union Aviation Safety Agency (EASA) type certification and the national civil aviation authorities in Europe.
Leichtwerk AG, “High Altitude Platform Stations (HAPS) – A Future Key Element of Broadband Infrastructure” (2023). I recommend to closely follow Leichtwerk AG which is a world champion in making advanced gliding planes. The hydrogen powered StratoStreamer HAP is near-production ready, and they are currently working on a solar-powered platform. Germany is renowned for producing some of the best gliding planes in the world (after WWII Germany was banned from developing and producing aircrafts, military as well as civil. These restrictions was only relaxed in the 60s). Germany has a long and distinguished history in glider development, dating back to the early 20th century. German manufacturers like Schleicher, Schempp-Hirth, and DG Flugzeugbau are among the world’s leading producers of high-quality gliders. These companies are known for their innovative designs, advanced materials, and precision engineering, contributing to Germany’s reputation in this field.
ITU Publication, World Radiocommunications Conference 2023 (WRC-23), Provisional Final Acts, (December 2023). Note1: The International Telecommunication Union (ITU) divides the world into three regions for the management of radio frequency spectrum and satellite orbits: Region 1: includes Europe, Africa, the Middle East west of the Persian Gulf including Iraq, the former Soviet Union, and Mongolia, Region 2: covers the Americas, Greenland, and some of the eastern Pacific Islands, and Region 3: encompasses Asia (excl. the former Soviet Union), Australia, the southwest Pacific, and the Indian Ocean’s islands.
Geoff Huston, “Starlink Protocol Performance” (November 2023). Note 2: The recommendations, such as those designated with “ADD” (additional), are typically firm in the sense that they have been agreed upon by the conference participants. However, they are subject to ratification processes in individual countries. The national regulatory authorities in each member state need to implement these recommendations in accordance with their own legal and regulatory frameworks.
Lynk Global website: https://lynk.world/ (see also FCC Order and Authorization). It should be noted that Lynk can operate within 617 to 960 MHz (Space-to-Earth) and 663 to 915 MHz (Earth-to-Space). However, only outside the USA. Constellation Area: IoT / M2M, Satellite-to-Cellphone, Internet, Direct-to-Cell. 8 LEO satellites out of 10 planned.
Omnispace website: https://omnispace.com/Constellation Area: IoT / M2M, 5G. World’s first global 5G non terrestrial network. Initial support 3GPP-defined Narrow-Band IoT radio interface. Planned 200 LEO and <15 MEO satellites. So far only 2 satellites launched.
NewSpace Index: https://www.newspace.im/ I find this resource having excellent and up-to date information of commercial satellite constellations.
LEOLABS Space visualization – SpaceX Starlink mapping. (deselect “Debris”, “Beams”, and “Instruments”, and select “Follow Earth”). An alternative visualization service for Starlink & OneWeb satellites is the website Satellitemap.space (you might go to settings and turn on signal Intensity which will give you the satellite coverage hexagons).
European Union Aviation Safety Agency (EASA). Note that an EASA-type Type Certificate is a critical document in the world of aviation. This certificate is a seal of approval, indicating that a particular type of aircraft, engine, or aviation component meets all the established safety and environmental standards per EASA’s stringent regulations. When an aircraft, engine, or component is awarded an EASA Type Certificate, it signifies a thorough and rigorous evaluation process that it has undergone. This process assesses everything from design and manufacturing to performance and safety aspects. The issuance of the certificate confirms that the product is safe for use in civil aviation and complies with the necessary airworthiness requirements. These requirements are essential to ensure aircraft operating in civil airspace safety and reliability. Beyond the borders of the European Union, an EASA Type Certificate is also highly regarded globally. Many countries recognize or accept these certificates, which facilitate international trade in aviation products and contribute to the global standardization of aviation safety.
ACKNOWLEDGEMENT.
I greatly acknowledge my wife, Eva Varadi, for her support, patience, and understanding during the creative process of writing this article.
I also greatly appreciate my past collaboration and the many discussions on the topic of Stratospheric Drones in particular and advanced antenna designs and properties in general that I have had with Dr. Jaroslav Holis, Senior R&D Manager (Group Technology, Deutsche Telekom AG) over the last couple of years. When it comes to my early involvement in Stratospheric Drones activities with Group Technology Deutsche Telekom AG, I have to recognize my friend, mentor, and former boss, Dr. Bruno Jacobfeuerborn, former CTO Deutsche Telekom AG and Telekom Deutschland, for his passion and strong support for this activity since 2015. My friend and former colleague Rachid El Hattachi deserves the credit for “discovering” and believing in the opportunities that a cellular broadband-based stratospheric drone brings to the telecom industry.
Many thanks to CEO Dr. Reiner Kickert of Leichtwerk AG for providing some high resolution pictures of his beautiful StratoStreamer.
Thanks to my friend Amit Kerenfor suggesting a great quote that starts this article.
Any errors or unclarities are solely due to myself and not the collaborators and colleagues that have done their best to support this piece.
To my friend Rudolf van der Berg this story is not about how volumetric demand (bytes or bits) results in increased energy consumption (W·h). That notion is silly, as we both “violently” agree on ;-). I recommend that readers also check out Rudolf’s wonderful presentation, “Energy Consumption of the Internet (May 2023),” which he delivered at the RIPE86 student event this year in 2023.
Recently, I had the privilege to watch a presentation by a seasoned executive talk about what his telco company is doing for the environment regarding sustainability and CO2 reduction in general. I think the company is doing something innovative beyond compensating shortfalls with buying certificates and (mis)use of green energy resources.
They replace (reasonably) aggressively their copper infrastructure (country stat for 2022: ~90% of HH/~16% subscriptions) with green sustainable fiber (country stat for 2022: ~78%/~60%). This is an obvious strategy that results in a quantum leap in customer experience potential and helps reduce overall energy consumption resulting from operating the ancient copper network.
Missing a bit imo, was the consideration of and the opportunity to phase out the HFC network (country stat for 2022: ~70%/~60%) and reduce the current HFC+Fibre overbuild of 1.45 and, of course, reduce the energy consumption and operational costs (and complexity) of operating two fixed broadband technologies (3 if we include the copper). However, maybe understandably enough, substantial investments have been made in upgrading to Docsis 3.1. An investment that possibly still is somewhat removed from having been written off.
The “wtf-moment” (in an otherwise very pleasantly and agreeable session) came when the speaker alluded that as part of their sustainability and CO2 reduction strategy, the telco was busy migrating from 4G LTE to 5G with the reasoning that 5G is 90% more energy efficient compared to 4G.
Firstly, it is correct that 5G is (in apples-for-apples comparisons!) ca. 90% more efficient in delivering a single bit compared to 4G. The metric we use is Joules-per-bit or Watts-seconds-per-bit. It is also not uncommon at all to experience Telco executives hinting at the relative greenness of 5G (it is, in my opinion, decidedly not a green broadband communications technology … ).
Secondly, so what! Should we really care about relative energy consumption? After all, we pay for absolute energy consumption, not for whatever relativized measure of consumed energy.
I think I know the answer from the CFO and the in-the-know investors.
If the absolute energy consumption of 5G is higher than that of 4G, I will (most likely) have higher operational costs attributed to that increased power consumption with 5G. If I am not in an apples-for-apples situation, which rarely is the case, and I am anyway really not in, the 5G technology requires substantially more power to provide for new requirements and specifications. I will be worse off regarding the associated cost in absolute terms of money. Unless I also have a higher revenue associated with 5G, I am economically worse off than I was with the older technology.
Having higher information-related energy efficiency in cellular communications systems is a feature of the essential requirement of increasingly better spectral efficiency all else being equal. It does not guarantee that, in absolute monetary terms, a Telco will be better off … far from it!
THE ENERGY OF DELIVERING A BIT.
Energy, which I choose to represent in Joules, is equal to the Power (in Watt or W) that I need to consume per time-unit for a given output unit (e.g., a bit) times the unit of time (e.g., a second) it took to provide the unit.
Take a 4G LTE base station that consumes ca. 5.0kW to deliver a maximum throughput of 160 Mbps per sector (@ 80 MHz per sector). The information energy efficiency of the specific 4G LTE base station (e.g., W·s per bit) would be ca. 10 µJ/bit. The 4G LTE base station requires 10 micro (one millionth) Joules to deliver 1 bit (in 1 second).
In the 5G world, we would have a 5G SA base station, using the same frequency bands as 4G and with an additional 10 MHz @ 700MHz and 100 MHz @ 3.5 GHz included. The 3.5 GHz band is supported by an advanced antenna system (AAS) rather than a classical passive antenna system used for the other frequency bands. This configuration consumes 10 kW with ~40% attributed to the 3.5 GHz AAS, supporting ~1 Gbps per sector (@ 190 MHz per sector). This example’s 5G information energy efficiency would be ca. 0.3 µJ/bit.
In this non-apples-for-apples comparison, 5G is about 30 times more efficient in delivering a bit than 4G LTE (in the example above). Regarding what an operator actually pays for, 5G is twice as costly in energy consumption compared to 4G.
It should be noted that the power consumption is not driven by the volumetric demand but by the time that demand exists and the load per unit of time. Also, base stations will have a power consumption even when idle with the degree depending on the intelligence of the energy management system applied.
So, more formalistic, we have
E per bit = P (in W) · time (in sec) per bit, or in the basic units
J / bit = W·s / bit = W / (bit/s) = W / bps = W / [ MHz · Mbps/MHz/unit · unit-quantity ]
E per bit = P (in W) / [ Bandwidth (in MHz) · Spectral Efficiency (in Mbps/MHz/unit) · unit-quantity ]
It is important to remember that this is about the system spec information efficiency and that there is no direct relationship between the Power that you need and the outputted information your system will ultimately support bit-wise.
and
Thus, the relative efficiency between 4G and 5G is
Currently (i.e., 2023), the various components of the above are approximately within the following ranges.
The power consumption of a 5G RAT is higher than that of a 4G RAT. As we add higher frequency spectrum (e.g., C-band, 6GHz, 23GHz,…) to the 5G RAT, increasingly more spectral bandwidth (B) will be available compared to what was deployed for 4G. This will increase the bit-wise energy efficiency of 5G compared to 4G, although the power consumption is also expected to increase as higher frequencies are supported.
If the bandwidth and system power consumption is the same for both radio access technologies (RATs), then we have the relative information energy efficiency is
Depending on the relative difference in spectral efficiency. 5G is specified and designed to have at least ten times (10x) the spectral efficiency of 4G. If you do the math (assuming apples-to-apples applies), it is no surprise that 5G is specified to be 90% more efficient in delivering a bit (in a given unit of time) compared to 4G LTE.
And just to emphasize the obvious,
RAT refers to the radio access technology, BB is the baseband, freq the cellular frequencies, and idle to the situation where the system is not being utilized.
Volume in Bytes (or bits) does not directly relate to energy consumption. As frequency bands are added to a sector (of a base station), the overall power consumption will increase. Moreover, the more computing is required in the antenna, such as for advanced antenna systems, including massive MiMo antennas, the more power will be consumed in the base station. The more the frequency bands are being utilized in terms of time, the higher will the power consumption be.
Indirectly, as the cellular system is being used, customers consume bits and bytes (=8·bit) that will depend on the effective spectral efficiency (in bps/Hz), the amount of effective bandwidth (in Hz) experienced by the customers, e.g., many customers will be in a coverage situation where they may not benefit for example from higher frequency bands), and the effective time they make use of the cellular network resources. The observant reader will see that I like the term “effective.” The reason is that customers rarely enjoy the maximum possible spectral efficiency. Likely, not all the frequency spectrum covering customers is necessarily being applied to individual customers, depending on their coverage situation.
In the report “A Comparison of the Energy Consumption of Broadband Data Transfer Technologies (November 2021),” the authors show the energy and volumetric consumption of mobile networks in Finland over the period from 2010 to 2020. To be clear, I do not support the author’s assertion of causation between volumetric demand and energy consumption. As I have shown above, volumetric usage does not directly cause a given power consumption level. Over the 10-year period shown in the report, they observe a 70% increase in absolute power consumption (from 404 to 686 GWh, CAGR ~5.5%) and a factor of ~70 in traffic volume (~60 TB to ~4,000 TB, CAGR ~52%). Caution should be made in resisting the temptation to attribute the increase in energy over the period to be directly related to the data volume increase, however weak it is (i.e., note that the authors did not resist that temptation). Rudolf van der Berg has raised several issues with the approach of the above paper (as well as with many other related works) and indicated that the data and approach of the authors may not be reliable. Unfortunately, in this respect, it appears that systematic, reliable, and consistent data in the Telco industry is hard to come by (even if that data should be available to the individual telcos).
Technology change from 2G/3G to 4G, site densification, and more frequency bands can more than easily explain the increase in energy consumption (and all are far better explanations than data volume). It should be noted that there will also be reasons that decrease power consumption over time, such as more efficient electronics (e.g., via modernization), intelligent power management applications, and, last but not least, switching off of older radio access technologies.
The factors that drive a cell site’s absolute energy consumption is
Radio access technology with new technologies generally consumes more energy than older ones (even if the newer technologies have become increasingly more spectrally efficient).
The antenna type and configuration, including computing requirements for advanced signal processing and beamforming algorithms (that will improve the spectral efficiency at the expense of increased absolute energy consumption).
Equipment efficiency. In general, new generations of electronics and systems designs tend to be more energy-efficient for the same level of performance.
Intelligent energy management systems that allow for effective power management strategies will reduce energy consumption compared to what it would have been without such systems.
The network optimization goal policy. Is the cellular network planned and optimized for meeting the demands and needs of the customers (i.e., the economic design framework) or for providing the peak performance to as many customers as possible (i.e., the Umlaut/Ookla performance-driven framework)? The Umlaut/Ookla-optimized network, maxing out on base station configuration, will observe substantially higher energy consumption and associated costs.
The absolute cellular energy consumption has continued to rise as new radio access technologies (RAT) have been introduced irrespective of the leapfrog in those RATS spectral (bits per Hz) and information-related (Joules per bit) efficiencies.
WHY 5G IS NOT A GREEN TECHNOLOGY?
Let’s first re-acquaint ourselves with the 2015 vision of the 5G NGMN whitepaper;
“5G should support a 1,000 times traffic increase in the next ten years timeframe, with energy consumption by the whole network of only half that typically consumed by today’s networks. This leads to the requirement of an energy efficiency increase of x2000 in the next ten years timeframe.” (Section 4.2.2 Energy Efficiency, 5G White Paper by NGMN Alliance, February 2015).
The bold emphasis is my own and not in the paper itself. There is no doubt that the authors of the 5G vision paper had the ambition of making 5G a sustainable and greener cellular alternative than historically had been the case.
So, from the above statement, we have two performance figures that illustrate the ambition of 5G relative to 4G. Firstly, we have a requirement that the 5G energy efficiency should be 2000x higher than 4G (as it was back in the beginning of 2015).
or
if
Getting more spectrum bandwidth is relatively trivial as you go up in frequency and into, for example, the millimeter wave range (and beyond). However, getting 20+ GHz (e.g., 200+x100 MHz @ 4G) of additional practically usable spectrum bandwidth would be rather (=understatement) ambitious.
And that the absolute energy consumption of the whole 5G network should be half of what it was with 4G
If you think about this for a moment. Halfing the absolute energy consumption is an enormous challenge, even if it would have been with the same RAT. It requires innovation leapfrogs across the RAT electronic architecture, design, and material science underlying all of it. In other words, fundamental changes are required in the RF frontend (e.g., Power amplifiers, transceivers), baseband processing, DSP, DAC, ADC, cooling, control and management systems, algorithms, compute, etc…
But reality eats vision for breakfast … There really is no sign that the super-ambitious goal set by the NGMN Alliance in early 2015 is even remotely achievable even if we would give it another ten years (i.e., 2035). We are more than two orders of magnitude away from the visionary target of NGMN, and we are almost at the 10-year anniversary of the vision paper. We more or less get the benefit of the relative difference in spectral efficiency (x10), but no innovation beyond that has contributed very much to quantum leap cellular energy efficiency bit-wise.
I know many operators who will say that from a sustainability perspective, at least before the energy prices went through the roof, it really does not matter that 5G, in absolute terms, leads to substantial increases in energy consumption. They use green energy to supply the energy demand from 5G and pay off $CO_2$ deficits with certificates.
First of all, unless the increased cost can be recovered with the customers (e.g., price plan increase), it is a doubtful economic venue to pursue (and has a bit of a Titanic feel to it … going down together while the orchestra is playing).
Second, we should ask ourselves whether it is really okay for any industry to greedily consume sustainable and still relatively scarce green resources without being incentivized (or encouraged) to pursue alternatives and optimize across mobile and fixed broadband technologies. Particularly when fixed broadband technologies, such as fiber, are available, that would lead to a very sizable and substantial reduction in energy consumption … as customers increasingly adapt to fiber broadband.
Fiber is the greenest and most sustainable access technology we can deploy compared to cellular broadband technologies.
SO WHAT?
5G is a reality. Telcos are and will continue to invest substantially into 5G as they migrate their customers from 4G LTE to what ultimately will be 5G Standalone. The increase in customer experience and new capabilities or enablers are significant. By now, most Telcos will (i.e., 2023) have a very good idea of the operational expense associated with 5G (if not … you better do the math). Some will have been exploring investing in their own green power plants (e.g., solar, wind, hydrogen, etc.) to mitigate part of the energy surge arising from transitioning to 5G.
I suspect that as Telcos start reflecting on Open RAN as they pivot towards 6G (-> 2030+), above and beyond what 6G, as a RAT, may bring of additional operational expense pain, there will be new energy consumption and sustainability surprises to the cellular part of Telcos P&L. In general, breaking up an electronic system into individual (non-integrated) parts, as opposed to being integrated into a single unit, is likely to result in an increased power consumption. Some of the operational in-efficiencies that occur in breaking up a tightly integrated design can be mitigated by power management strategies. Though in order to get such power management strategies to work at the optimum may force a higher degree of supplier uniformity than the original intent of breaking up the tightly integrated system.
However, only Telcos that consider both their mobile and fixed broadband assets together, rather than two silos apart, will gain in value for customers and shareholders. Fixed-mobile (network) conversion should be taken seriously and may lead to very different considerations and strategies than 10+ years ago.
With increasing coverage of fiber and with Telcos stimulating aggressive uptake, it will allow those to redesign the mobile networks for what they were initially supposed to do … provide convenience and service where there is no fixed network present, such as when being mobile and in areas where the economics of a fixed broadband network makes it least likely to be available (e.g., rural areas) although LEO satellites (i.e., here today), maybe stratospheric drones (i.e., 2030+), may offer solid economic alternatives for those places. Interestingly, further simplifying the cellular networks supporting those areas today.
TAKE AWAY.
Volume in Bytes (or bits) does not directly relate to the energy consumption of the underlying communications networks that enable the usage.
The duration, the time scale, of the customer’s usage (i.e., the use of the network resources) does cause power consumption.
The bit-wise energy efficiency of 5G is superior to that of 4G LTE. It is designed that way via its spectral efficiency. Despite this, a 5G site configuration is likely to consume more energy than a 4G LTE site in the field and, thus, not a like-for-like in terms of number of bands and type of antennas deployed.
The absolute power consumption of a 5G configuration is a function of the number of bands deployed, the type of antennas deployed, intelligent energy management features, and the effective time 5G resources that customers have demanded.
Due to its optical foundation, Fiber is far more energy efficient in both bit-wise relative terms and absolute terms than any other legacy fixed (e.g., xDSL, HFC) or cellular broadband technology (e.g., 4G, 5G).
Looking forward and with the increasing challenges of remaining sustainable and contributing to CO2 reduction, it is paramount to consider an energy-optimized fixed and mobile converged network architecture as opposed to today’s approach of optimizing the fixed network separately from the cellular network. As a society, we should expect that the industry works hard to achieve an overall reduction in energy consumption, relaxing the demand on existing green energy infrastructures.
With 5G as of today, we are orders of magnitude from the original NGMN vision of energy consumption of only half of what was consumed by cellular networks ten years ago (i.e., 2014), requiring an overall energy efficiency increase of x2000.
Be aware that many Telcos and Infrastructure providers will use bit-wise energy efficiency when they report on energy consumption. They will generally report impressive gains over time in the energy that networks consume to deliver bits to their customers. This is the least one should expect.
Last but not least, the telco world is not static and is RAT-wise not very clean, as mobile networks will have several RATs deployed simultaneously (e.g., 2G, 4G, and 5G). As such, we rarely (if ever) have apples-to-apples comparisons on cellular energy consumption.
ACKNOWLEDGEMENT.
I greatly acknowledge my wife, Eva Varadi, for her support, patience, and understanding during the creative process of writing this article. I also greatly appreciate the discussion on this topic that I have had with Rudolf van der Berg over the last couple of years. I thank him for pointing out and reminding me (when I forget) of the shortfalls and poor quality of most of the academic work and lobbying activities done in this area.
PS
If you are aiming at a leapfrog in absolute energy reduction of your cellular network, above and beyond what you get with your infrastructure suppliers (e.g., Nokia, Ericsson, Huawei…), I really recommend you take a look at Opanga‘s machine learning-based Joule ML solution. The Joules ML has been proven to reduce RAN energy costs by 20% – 40% on top of what the RAT supplier’s (e.g., Ericsson, Nokia, Huawei, etc.) own energy management solutions may bring.
Disclosure: I am associated with Opanga and on their Industry Advisory Board.
Back in 2014, working at Deutsche Telekom AG and responsible for Technology Economics, we looked at alternatives to fiber deployment in Germany (and other markets). It was clear that deploying fiber in Germany would be massively costly and take a very long time… As an incumbent solely relying on xDSL, there was unease in general and in particular with observing that HFC (hybrid-fiber-coaxial) providers were gaining a lot of traction in key markets around Germany. There was an understanding that fiber would be necessary to secure the longer-term survivability of the business. Even as far back as 2011, this was clear to some visionaries within Deutsche Telekom. My interest at the time was whether fixed wireless access (FWA) solutions could be deployed faster (yes, it could and can, at least in Germany) and bridge the time until fiber was sufficiently deployed and with an economically attractive uptake that allowed an operator to retire the FWA solution or re-purpose it for normal mobile access. It economically did not make sense to deploy FWA everywhere … by far not. Though we found that in certain suburban and rural areas, it could make sense to deploy FWA solutions. … So why did it not happen? At the time, the responsible executives for fixed broadband deployment (no, no converged organization at the time) were nervous that “their” fiber Capex would be re-prioritized to FWA and thus taken away from their fiber deployment. Resulting in even further delays in fiber coverage in Germany. Also … they argued the write-off of fiber investments (e.g., 15 – 20+ years) is so much much longer compared to FWA (e.g., 5 – 7 years), and when factoring in the useful lifetime of fiber versus FWA, it made no sense to deploy it (of course ignoring that we could deploy FWA within 6 months while the fiber in that area might not be present in the next 10+ years;-).
I learned three main lessons (a lot more, actually … but that’s for my memoirs if I remember;-)
FWA can be made economically favorable but not universally so everywhere.
FWA can be a great instrument to bridge the time until fiber deployment has arrived and a given demand (uptake) in an area exists (you just need to make sure your FWA design accounts for the temporary nature of the purpose of your solutions).
FWA at high frequencies (e.g., >20 GHz) is not “just” an overlay of an MNOs existing mobile network. The design should be considered a standalone network, with maximum re-use of any existing infrastructure, with line-of-sight (LoS) to customers and LoS redundancy build-in (i.e., multiple redundant paths to a customer).
We are now 10+ years further (and Germany is still Europe’s laggard in terms of fiber deployment and will remain so for many years to come), and the technology landscape that supports both fiber and fixed wireless access is much further as well…
In the following, it is always good to keep in mind that
“Even if your something appears less economically attractive than something else, if that something else is not available or present, your solution may be an interesting opportunity to capture growth to your business. At least within a given window of opportunity.”
and, so it begins …
FIXED WIRELESS ACCESS (FWA).
In this blog, I will define Fixed Wireless Access (FWA) as a service that provides a fixed-like wireless-based internet broadband connection to a household. FWA bypasses the need for a last-mile fixed wired connection from a nearby access point (e.g., street cabinet) to a customer’s household. Thus substituting the need for a fixed copper, coax, or fiber last-mile connection. I will, in general, position FWA in a modern context of 5G, which may enable existing MNOs to bridge the time until they will have fiber coverage, for example, rural and sub-urban areas. Or, as the thinking goes (for some), completely avoid the need for costly and (allegedly) less profitable deployment of fiber in less household-dense areas where more kilometer of fiber needs to be deployed in order to reach the same amount of households compared to an urban or dense urban area. Of course, companies may also be tempted to build FWA-dedicated ISP networks operating in the mmWave range (i.e., >20 GHz) or in the so-called mid-bands range (e.g., ≥ 2.5 GH, C-band, …) to provide higher quality internet services to sub-urban and rural customers where the economics for fiber coverage and connectivity may be comparably challenged in terms of economics and time to fiber availability.
Figure 1 below provides an overview and comparison of the various ways we connect our customers’ homes, with the exception of LEO satellite and stratospheric drone-based connectivity solutions (it’s another very interesting story). So, illustrating terrestrial network-based connectivity to the household with either a fixed-line (buried or aerial) or wireless.
Figure 1 illustrates 3 different ways to connect to a household. The first (Household A) is the “normal” fixed connection, where the last mile from the street cabinet is a physical connection entering the customer’s household either via a buried connection or via a street pole (aerial connection). In the second situation (Household B), the service provider has no fixed assets readily available in a given area but has mobile radio access network infrastructure in the proximity of the household. The provider may choose to offer Fixed Mobile Substitution (FMS) using their existing mobile infrastructure and spectrum capacity to offer households fixed-like service via an in-door modem capable of receiving the radio frequencies upon which the FMS service is offered. Alternatively, and better for the mobile capacity in general (as well as providing a better customer experience), would be to offer the service with an outdoor customer premise antenna (CPA) connecting to an in-door CPE. If the FMS service is provided via a CPA, it may be called or identified as a fixed wireless access (FWA) service. In this connection scenario, cellular spectrum resources are being shared between the household FMS customers and the mobile customer base. The third connectivity scenario (Household C), is where a dedicated high-speed wireless link is established between a service provider’s remote advanced antenna system (and its associated radio access network equipment) and the household’s (typically outdoor) customer premise antenna. Both infrastructure and spectral resources will be dedicated to providing competitive (to broadband fixed alternatives) fixed-like services to customers. This is fixed-wireless access or FWA. In a modern setting service providers would offer fiber-like speeds (e.g., >100 Mbps) with dedicated mmWave 5G (SA) infrastructure. However, it is also possible to provide better-than-average mobile broadband services over a CPA and an operator’s mobile network (as it is often done with 4G or/and cellular 5G NSA).
For the wireless connection between the service provider’s access network and the household, we have several options;
(1) The Fixed Wireless Access (FWA) network provides a dedicated wireless link between the service provider’s network and the customer’s home. In order to maximize the customer experience, typically, an outdoor customer premise antenna (CPA) would have to be installed on the exterior of a household, offering line-of-sight with the provider’s own advanced antenna residing on its access network infrastructure. The provider will likely dedicate a sufficient amount of wireless spectrum bandwidth (in MHz) to provide a competitive (to fixed) broadband service. In a 5G SA (standalone) setting, this could be a cellular spectrum in the mid-band range (≥ 2.5 – 10 GHz) or (or and) mmWave spectrum above 20 GHz. An access network providing fixed-wireless services in the mid-band spectrum typically would overlay an existing mobile network (if the provider is also an MNO) with possibly site additions allowing for higher-availability services to households as well as increase the scale and potential of connecting households due to increased LoS likelihood. In case the services rely on mmWave frequency bands, I would in general, expect a dedicated network infrastructure would have to be built to provide sufficient household scale, reliability, and availability to households in the covered broadband service area. This may (also) rely on existing mobile network infrastructure if the provider is an established MNO, or it may be completely standalone. My rule of thumb is that for every household that is subscribing to the FWA service, I need at least 2, preferably 3, individual line-of-sight solutions to the household CPA. Most conventional cellular network designs (99+% of all there are out in the wild) cannot offer that kind of coverage solution.
The customer premise antenna (CPA) connects to the household’s customer premise equipment (CPE). The CPE provides WiFi coverage within the household either as a single unit or as part of a meshed WiFi household network.
(2) A service that is based on Fixed Mobile Substitution (FMS) utilizes existing cellular resources, such as infrastructure and spectrum bandwidth, to provide a service to a cellular-based (e.g., 4G/5G) customer premise equipment (CPE) residing inside a customer’s household. The CPE connects to the mobile network (via 4G and/or 5G ) and enjoys the quality of the provider’s mobile network. Inside the household, the CPE offers WiFi coverage that is utilized by the household’s occupants. As existing mobile resources are shared with regular mobile customers that may also be in the same household as the FMS solution itself, the service provider needs to carefully balance capacity and quality between the two customer segments, with the household one typically being the greedy one (with respect to network resources and service plans) and impacting network resources substantially more than the regular mobile user (e.g., usually 20+ to 1).
Figure 2 summarizes various connection possibilities there are to connect a household to the internet as well as media content such as linear and streaming TV.
FWA has been around the telco and ISP toolbox for many years in one form or another. The older (or let’s put it nicer, the experienced) reader will remember that a decade ago, many of us believed that WiMax (Worldwide Interoperability for Microwave Access) was the big thing to solve all the ailing (& failings) of 3G, maybe even becoming our industry’s de facto 4G standard. WiMax promised up to 1 Gbps for a fixed (wireless) access base station and up to around 100 Mbps at low mobility (i.e., <50 km per hour). As we know today, it should not be.
FAST FORWARD TO TODAY & TOMORROW WITH 5G AND FIBER SERVICES.
GSMA (GSM Association, the mobile interest group) has been fairly bullish on the advantages and opportunities of 5G-based Fixed Wireless Access (5G-FWA). Alleging a significant momentum behind FWA with (1) 74+ broadband service providers launching FWA services globally, (2) Expecting 40 million 5G FWA subscribers by 2025. Note globally, as of October 2022, there were 5.5 billion unique mobile subscribers. So 5G FWA amounts to <1% of unique subscribers, and last but not least (3) They expect up to 80% cost saving versus fiber to the home (FTTH) @ 100 Mbps downlink. GSMA lists more advantages according with GSMA but the 3 here are maybe the most important.
According to GSMA, in Western Europe, they expect roughly around 275+ million people will subscribe to 5G by 2025. This number represents ca. 140 million unique 5G households. Applying household scaling between western Europe and Global on the global total of 40 million 5G FWA HH, one should expect to capture between 4 to 5 million 5G FWA households or ca. 2.5% FWA HH penetration in Western Europe by 2025 (see below for details of this estimate). This FWA number also corresponds to a ca. 4% of all unique 5G households, or ca. 2% of all unique 5G subscribers, or ca. 1% of all unique mobile subscribers (in 2025). While 40 million (5 million) globally (in Western Europe) sounds like a large number, it is, to all effects rather minuscule and underwhelming compared to the total mobile and fixed broadband market.
The GSMA report, “The 5G FWA opportunity: series highlights” (from July 2022) also provides a 2025 projection for 5G FWA connections as a percentage of households across various countries. In Figure 3 below, find the GSMA projections with, as a comparison, the estimated fiber-to-the-home connections (FTTH) in 2025 and, for reference, the actual FTTH connections in 2021. It seems compelling to assume that 5G FWA would be an alternative to fiber at home or an HFC D3.1 (D = Docsis) connection. Of course, it is only possible to get a service if the technology of choice covers the household. A fiber connection to your household requires that there is a fiber passing in the proximity of your household. Thus the degree of fiber coverage is important in order to assess the fiber subscription uptake possible. Likewise, a 5G FWA connection requires that the household is within a very good and high-quality 5G coverage of the FWA provider (or the underlying network operator). Figure 4 below provides an overview of 2021 actual and 2026 (projected) fiber-based household coverage (i.e., homes passed) percentages in Western Europe.
Figure 3 above shows GSMA 2025 projections of 5G FWA household (HH) connections vs. actual FTTH connections in 2021 and the author’s forecast of FTTH connections by 2025. In countries where the is no 5G-FWA data means, according to GSMA that the expectations are below 1% of HH connected. The total Western Europe 5G FWA connection figure is in excess of 10+ million HH versus 4 – 5 million that was assessed based on the global number of 5G FWA and unique mobile households. In most Western European markets, 5G FWA as defined in the GSMA study, will be a niche service. Note: the FTTH connected percentages are based on total households in the country instead of homes passed figures. Markets that have reached 80% of HHs are capped at that level. In all cases, it would be possible to go beyond. Sources: GSMA for 5G FWA and OECD statistics database.Figure 4 fiber coverage measured as a percentage of households passed across Western Europe. 2016 and 2021 are actual data based on European Commission’s “Broadband Coverage in Europe 2021” (authored by Omdia et al.). The 2026 & 2031 figures are the author’s own forecast based on the last 5 years maximum FTTP/B deployment speed. I have imposed a 95% Household coverage ceiling in my deployment model. The pie charts illustrate the degree the fiber deployment can make use of aerial infrastructure vis-a-vis buried requirements.
If we take a look at 5G coverage, which may be an enabler for FWA services that can compete with fiber quality, it would be fairly okay to assume that most mobile operators in Western Europe would have close to a full 5G population (and households) coverage. However, accessing the 5G quality of that coverage would be problematic. 5G coverage may be based on 700 MHz piggybacking on LTE (i.e., non-standalone, NSA 5G), providing nearly 100% household coverage, it may involve considerable mid-band (i.e., > 2.1 GHz frequency bands) 5G coverage in urban and suburban areas with varying degree of rural coverage, it may also involve the deployment of mmWave (i.e., >20 GHz frequency bands) as an overlay to the normal macro cellular network or as dedicated standalone fixed-wireless access network or a combination of both.
Actually, one might also think that in geographical areas where fiber coverage, or D3.1-based HFC, is relatively limited or completely lacking, 5G FWA opportunities would be more compelling due to the lack of competing broadband alternatives. If the premise is that the 5G FWA service should be fiber-like, it would require good quality 5G coverage with speeds exceeding 100 Mbps at high availability and consistency. However, if the fixed broadband service that FWA would compete with is legacy xDSL, then some of the requirements for fiber-like quality may be relaxed (e.g., 100+ Mbps, very high availability, …).
What are the opportunities, and where? Focusing on fiber deployment in Western Europe, Figure 5 illustrates homes covered by fiber and those with no fiber coverage in urban and rural areas as of 2021 (actual). The figure below also provides a forecast of home coverage and homes missing by 2026.
Figure 5 illustrates the percentage of homes fiber covered (i.e., passed) as well as the homes where fiber coverage remains. The 2021 numbers are actual and based on data in the latest European Commission’s “Broadband Coverage in Europe 2021” (authored by Omdia et al.). The 2026 data is the author’s forecast model based on the last 5 years’ fastest fiber rollout speed. 2021 Households numbers (in a million households) are added to the 2021 charts. In general, it is expected that the number of rural households will continue to decline over the period.
As Figure 5 above shows, the urban fiber deployment in Europe is happening at a fast pace in most markets, and the opportunities for alternatives (at scale) may at the same time be seen as diminishing apart from a few laggard markets (e.g., Austria, Belgium, Germany, UK, ..). Rural opportunities for broadband alternatives (to fiber) may be viewed more optimistically with many more households only having access to aging copper lines or relative poor HFC.
A 5G FWA provider may need to think about the window of opportunity to return on the required investment. To address this question, Figure 6 below provides a projection for when at least 80% of households will be connected in urban and rural areas. Showing that in some markets, rural areas may remain more attractive for longer than the corresponding urban areas. Further, if one views the 5G FWA as a bridge to fiber availability, there may be many more opportunities for FWA than what Figures 5 and 6 allude to.
Figure 6 shows projected years until 80% of households have been covered using the maximum deployment pace of the last 5 years. The left side (a) illustrates the urban deployment and (b) the rural fiber deployment. The 80% limit is somewhat arbitrary and, particularly in urban areas, is likely to be exceeded once reached (assuming further deployment is economical). Most commercial (unsubsidized) deployment focus has been in urban areas, while rural areas are often deployed if subsidies are made available by European Union or local government.
Looking at the opportunity for fiber alternatives going forward, Figure 7 below provides the quantum of households that remain to be covered by fiber. This lack of fiber also creates opportunities for broadband alternatives, such as 5G FWA, and maybe non-terrestrial broadband solutions (e.g., Starlink, oneWeb,…). Cellular operators, with a good depth of site coverage, should be able to provide competitive alternatives to existing legacy fixed copper services, as long as LoS is not required, at least. Particularly in some rural areas, depending on the site density and spectrum commitment, around rural villages and towns. Cellular networks may not have much capacity and quality to spare in urban areas for fixed mobile substitution (FMS), at least if designed economically. This said, and depending on the cellular, and fixed broadband competitive environment, FMS-based services (4G and 5G) may be able to bridge the short time until fiber becomes available in an area. This can be used by an incumbent telco that is in the process of migrating its aging copper infrastructure to fiber or as a measure by competing cellular operators to tease copper customers away from that incumbent. Hopefully, those cellular Telcos have also thought about FMS migration off their cellular networks to a permanent fixed broadband solution, such as fiber (or a dedicated mmWave-based FWA service).
Figure 7 estimates the remaining households in (a) urban and (b) rural areas in 2023 and 2026. It may be regarded as a measure of the remaining potential for alternative (to fiber) broadband services. Note: Please note that the scale of Urban and Rural households remaining is different.
As pointed out previously, GSMA projects by 2025 ca. 5 million 5G FWA households in Western Europe. This is less than 3 out of every 100 regular households. Compared with fiber coverage of households estimated to be around 60 out of 100 by 2025. Given that some countries in Western Europe are lagging behind fiber deployment (e.g., Germany, UK, Italy, … see charts above), leaving a large part of their population without modern fixed broadband, one could expect the number might have been bigger than just a few percent. However, 5G FWA at 3.x GHz, and at mmWave frequencies require line-of-sight connections to a customer’s household to provide fiber-like quality and stability. Cellular networks were (obviously) never designed to have LoS to its customers as the cellular frequencies (≤ 3 GHz) were sufficiently low not to be “bothered” (too much) by penetration losses. At and above 3 GHz LoS is increasingly required if a fiber-like service is required.
Another aspect that is often under-appreciated or flat-out ignored (particularly by cellular-minded marketing & sales professionals), is the need for an exterior household customer premise antenna (CPA) that will allow a household to pick up the FWA signal at a higher quality (compared to a gateway antenna indoor due to penetration loss) and with minimum network interference, which may reduce overall quality and capacity in the cellular network (that coincidentally will hurt the normal cellular user as well as other FWA customers). The reason for this neglect is, in my opinion, that it is (allegedly) more difficult to sell such as product to cellular-minded customers and to cellular-minded salespeople as well. It also may increase the cost of technical support due to more complex installation procedures (compared to having a normal mobile phone or indoor gateway) than just turning on a cellular-WiFi modem box inside the home, and it may also result in higher ongoing customer service cost due to more components compared to either a cellular phone or a cellular modem.
THE ECONOMICS.
GSMA Intelligence group compared the total cost of ownership (TCO) of a dedicated 5G FWA mmWave-based connection with that of fiber-to-the-home (FTTH) for an MNO with an existing 5G network in Europe. It appears that the GSMA’s TCO model(s) are rich in detail regarding the underlying traffic models and cost drivers. Moreover, it would also appear that their TCO analysis is (at least at some level) based on an assumed kilometer-based TCO benchmark. It is unclear to me whether Opex has been considered. Though given the analysis is TCO, I assume that it is the case it was considered.
GSMA (for Europe) found that compared to fiber-based household connectivity, 5G FWA is 80% cheaper in rural areas, 60% cheaper in suburban, and 35% cheaper in urban areas compared to an FTTH deployment.
My initial thoughts, without doing any math on the GSMA results, was that I could (easily) imagine that 5G FWA would require less absolute Capex compared to deploying fiber to the home. At least for buried fiber deployment. I would be less confident wrt this result when it comes to aerial fiber deployment, but maybe it is also still a valid result. However, when considering Opex, what 5G FWA incrementally contributes, I would be much less sure that 5G FWA would be outperforming FTTH. At the least in rural and suburban areas where the household customer density per 5G FWA site would be very low (even before considering the opportunity based on LoS likelihood). Thus, the 5G FWA Opex scaled with the number of household subscribers may be a lot less favorable than FTTH, considering the access energy consumption and technical support costs alone. This is even before considering whether a normal rural and a suburban cellular network is at all suitable (designed for) for providing high availability and high-quality+ fixed-like broadband services delivered by 3.x GHz or mmWave frequencies (which in rural and suburban areas may be even more problematic on existing cellular networks).
I would generally not expect that the existing rural/suburban cellular network would be remotely adequate to permanently replace the need for fiber-connected homes. We would most likely need to densify (add new sites) to ensure high quality and high availability connectivity to customers’ premises. This typically would translate into line-of-site (LoS) requirements between the 5G FWA antenna and the customers’ households. Also, to ensure high availability, similar to a fiber connection, we should expect the need for redundant LoS connectivity to the customers’ households (note: experience has shown that having only one LoS connection compromises availability and consistency/reliability substantially). Such redundant connectivity solutions would be even more difficult to find in existing cellular networks. These considerations would, if considered, both add substantial Capex and additional Opex to the 5G FWA TCO reducing the economical (and maybe commercial) attractiveness compared to FTTH.
HOW TO MAKE APPLES AND ORANGES MORE LIKE BANANAS.
As mentioned above, GSMA appears to base (some of) its economic conclusions on a per kilometer (km) unit driver. That is Euro-per-km. While I don’t have anything particular against this driver, apart from being rather 1-dimensional, I believe it provides fewer insights than maybe others’ more direct drivers of income, capital, and operational cost as well as, in the end, a given solution’s commercial success.
I prefer to use the number of households (HH) per square kilometer, thus HH per km2. For fiber deployment and household coverage, I would use fiber per HH passed (HHP). Fiber connecting the household, providing the actual connection (“the last mile”) to customers’ home, I use fiber HH connected (HHC). The intention behind fiber coverage, what is called household passed, is to be able to connect households by extending the fiber to the “last mile” (or the last-1.61-kilometer) and start generating revenues that return on the capital investment done in the first place. Fiber coverage can be thought of as a real option to connect a home. Fiber coverage is obviously a necesity for connecting a home. Similarly, building dedicated fixed-wireless access infrastructure, incrementally on existing cellular infra or from scratch, is to provide a fixed-like high-quality wireless connection to a household.
Figure 8 The above is an illustration of fiber deployment (i.e., coverage and connection) in comparison with fixed wireless access (FWA) coverage and fixed-like wireless services rendered to households (as opposed to individual mobile devices). It also provides a bit of rationale why a km-metric may capture less of the “action” than what happens within a km2 and with the households within. The most important metric in my analysis is the number of connected homes within a km2 as they tend to pay for the party.
Thus household density is a very important driver for the commercial potential, as well as how much of the deployment capital and operational cost can be assigned to a given household in a given geographical area. Urban areas, in general, have more households than suburban and rural areas. The deployment of Capex and Opex in urban areas will be lower per household than in suburban and more rural urbanized areas.
Every household that is fiber covered, implying that the dwelling is within a short reach of the main fiber passing through and ultimately connected, requires an investment with an operational cost associated and revenue for the service is supported by the connection. Fiber total cost of ownership (TCO) will depend on the amount of households covered and the number of households directly connected to a paying customer. For the fiber deployment economics, I am using data from my “Nature of Telecom Capex” (see Figure 16, and please note that the data is for buried fiber) that provides the capital cost of fiber coverage (households passed) and for homes fiber connected, both as a function of household density. For fiber homes passed (HHP) economics, I am renormalizing to fiber homes connected (HHC). Thus if 90% of homes are covered (i.e., passed) in an area and 60% of the homes passed are connected, those connected homes pay for the remaining unconnected homes (30%) under the fiber coverage. This somewhat inflates the cost of connecting a home but is similar to the economic logic of cellular coverage, where the cost is paid by customers having access to the cellular site, even if the cellular site usually covers a lot more people than customers.
In general, fiber deployment becomes increasingly costly as the deployment moves from denser urbanized areas out to suburban and finally rural areas as the household density decreases and more area (and kilometers) need to be covered to capture the same amount of households as in urban areas. Also, it is worth keeping in mind that in countries with the possibility of substantial aerial fiber deployment (e.g., Spain, France, Portugal, Poland, etc..), this leads to a significant unit cost reduction in comparison to buried fiber deployment as we know it from Germany, Netherlands and Denmark. Figure 4 above provides an overview of Western European countries with aerial fiber deployment possibilities and those where buried fiber is required.
For an incremental FWA solution, an existing cellular site will be used. The site location will offer a coverage area where normal broadband cellular services can be provided. Households can of course be connected either via a normal mobile device or a dedicate inhourse gateway connecting to the cellular network (possibly via an exterior CPA) and offering indoor WiFi coverage. For scalable fiber-like wireless quality (e.g., stability and speed) of effective speeds exceeding 100+ Mbps per household connection to be offered from a normal cellular site we typically need line-of-site (LoS) to a customer home as well as a substantial amount of dedicated spectrum bandwidth (100+ MHz) provisioned on an advanced antenna system (AAS e.g., massive MiMo 64×64). The 5G FWA solution, I am assuming, is one that requires the receiving customer to have an outdoor antenna installed on the customer’s home with LoS to the cellular site hosting the FWA solution. The solution is assumed to cover 1 km2 (range of ca. 560 meters) with an effective speed of 300 Mbps per connection. That throughput should hold up to a given connection load limit, after which the speed is expected to decrease as additional household connections are added to the cellular site.
One of, in my opinion, the biggest assumptions (or neglects) of the fiber-like 5G FWA service to households at scale (honestly, a couple of % of HH is not worth discussing;-) is the ability to achieve a line-of-sight between the provider’s cellular site antenna and that of a household with its own customer premise antenna (CPA). For 3.x GHz services, one may assume that everything will still work nicely without LoS and with an inhouse gateway without supporting exterior CPA. I agree … with that premise … if what is required is to beat a xDSL or poor HFC service. There are certainly still many places in Western Europe where that may even make good business sense to attempt to do (that is, competing inferior fixed legacy “broadband” services). The way that cellular networks have been designed (which obviously also have to do with the relative low cellular frequency ranges of the past) is not supporting LoS at scale in urbanized environments. Some great work by professor Dr Akram Al-Hourani, summarised in Figure 9 below, clearly illustrates the difficulty in achieving LoS in urban areas. While I am of the opinion that the basic logic of urban LoS is straightforward, it seems that cellular folks tend to be so used to having (good) cellular coverage pretty much anywhere that it is forgotten when considering higher frequencies that work much better at (or only with) line-of-sight.
The lack of LoS in areas targeted for 5G FWA services needs to be considered in the economic analysis. At least if you are up against fiber-like quality and your intention is to compete at scale (some household opportunity as is the case for fiber). For your FWA cellular-based network, this would often require some degree of densification compared to the as-is cellular network that may be in place. In my work below, I have assumed that my default 5G FWA configuration and targeted service requires 6 sectors covering a 1 km2 of a given urbanized household density. The consequence of that may be that a new (greenfield) site will be required in order to provide 5G FWA at scale (>10+% of HH).
Figure 9 above illustrates the probability in an urban environment for achieving line-of-sight (LoS) between two points, separated by a horizon distance d12 and at height h1 and h2. It is worth keeping in mind that typical urban (and rural) antenna height will be in the range of 30 meter. To give context to the above LoS probability curves, a typical one and two storey will have a height less than 10 meters and 30 meters would represent probably represent 80+% of urbanized areas. The above illustration is inspired by the wonderful work of Dr Akram Al-Hourani Associate Professor and the Telecommunication Program Manager at the School of Engineering, Royal Melbourne Institute of Technology (RMIT) (see his paper “On the Probability of Line-of-Sight in Urban Environments”). There is some relatively simple Monte Carlo simulation work that can be done to verify the above LoS probability trends that I recommend doing.
The economics of this solution is straightforward. I have an upfront investment in enabling the FWA solution with a targeted quality level (e.g., ). In a first approximation and up to a predefined (and pre-agreed as sellable with Marketing), this investment is independent of the number of household customers I get. Of course, at some given load & quality conditions, the FWA capacity may have to be expanded by, for example, adding more capable antennas, more FWA (relevant) spectrum, additional sectors, or building a new site. It should be noted that I have not considered the capacity expansion part in the presented analysis in this article. Thus, as the amount of connected FWA households increases, the quality, in general, and speed, in particular, would decrease (typically by a non-linear process).
Most cellular networks have a substantial part of their infrastructure that does not generate any substantial amount of traffic. In other words, its resources are substantially under-utilized in most cellular networks. Part of building a cellular network is to ensure coverage is guaranteed to almost all of the population (98%+) and geography (>90%), irrespective of the expected demand. Some Telcos’ obsession with public speed & performance tests & benchmarks (e.g., Umlaut, Ookla, etc…) has resulted in many networks having an “insane” (un-demanded and highly un-economical) amount of capacity and quality in coverage areas without any particular customer demand. This typically leads to industry consultants proposing to use all that excess quality for what they may call FWA. I would call it FMS (but what’s in a name). Though, even if there may be a lot of excess cellular capacity and quality in rural and subs-urban areas, it’s hardly fiber-like. And it is also highly unlikely to offer the same scale opportunity in terms of households as a fiber deployment would do (hint: LoS likelihood). The opportunity that is exploitable is to compete with xDSL and poor-quality HFC (if available at all). If an area doesn’t have fiber and no good quality coax, that excess cellular capacity can be used as an alternative to xDSL.
To provide competitive fiber-like FWA services with wireless on top of an existing cellular network, we need to design it “right”. Our aim should be a speed well above 100 Mbps (e.g., 300 Mbps) with stability and availability that requires a different design principle than current legacy cellular networks. To provide a 300 Mbps wireless household connection we could start out with a bandwidth of 100 MHz at 3.5 GHz (i.e., 5G mid-band as an example). Later it is possible to upgrade to or add a mmWave solution with even more bandwidth (e.g., 20 to 300 GHz frequency range with bandwidths of multiples of GHz). In order to get both stability and availability, I will assume that I need a minimum of two but preferably three different LoS solutions for an individual household. If no fiber or other high-quality fixed broadband competitors are around, this requirement may be relaxed (although I think a minimum of two LoS connections are required to provide a real fixed broadband alternative at frequencies above 3 GHz).
SOME COMPARATIVE RESULTS.
In my economic analysis of fiber deployment and 5G-based fixed wireless access, the total cost of ownership (TCO) is presented relative to the number of households connected. This way of presenting the economics has the advantage of relating costs directly to the customer that will pay for the service.
The Capex for fiber deployment can be broken up into two parts. The first part is the fiber coverage, also called fiber household passed (HHP). The second part is household connected (HHC), connecting customer households to the main fiber pass, which is also what we like to call Fiber to the Home (FTTH).
The capital expense of fiber coverage is mainly driven by the civil work (ca. 70%, with the remainder being ca. 20% to passive and ca. 10% for the active part) and relates to the distance fiber is being laid out over (yes, there is a km driver there;-). The cost can be directly related to household density. We have an economic relationship between deployment cost and the actual household density reflecting the difference in unit deployment cost between urban (i.e., high household density, least unit Capex), suburban, and rural (i.e., low household density and highest unit Capex ) urbanized areas. You need fewer kilometers to cover a given amount of households in dense urban areas than is required in a rural village with spread-out dwellings and substantially lower household density. In my economic analysis, I re-scale the fiber coverage cost to the number of households connected (i.e., the customers). Similar to household coverage cost, the household connection cost can likewise be related to the household density, which is already a measure of the connection cost. The details have been described in details in my earlier article, “The Nature of Telecom Capex.”.
The capital expenses related to fixed wireless access will, by its very nature, have a fairly large variation in its various components making up the total investment to provide fixed-like services to customer households. It will depend critically on the design criteria of the service we would like to offer (e.g., max & min speed, availability, … ) as well as the cellular network’s starting point (e.g., greenfield vs brownfield, site density, the likelihood of customer household LoS, etc..). Furthermore, supplier choice, including existing supplier lock-in and corporate purchasing power can influence the unit Capex substantially as well. Civil works and passive infrastructure is reasonably stable across western Europe, with a minor dependency on a given country’s income levels for the civil work-related cost. In my experience, the largest capital expense variation will be on the active telecom equipment depending heavily on procurement scale and supplier leverage. As I have worked in the past for a Telco which is imo&e is one of the strongest (in the industry) in terms of purchasing power and supplier leverage, there is a danger that my unitary Capex assessment may be biased towards the lower end of a reasonable estimate for an industry average for the active equipment required. Another Capex expense factor associated with substantial variation is the spectrum expense I am using in my estimate. My 5G FWA design example requires me to deploy 100 MHz at 3.x GHz (e.g., 3.4 – 3.7 GHz). I have chosen the spectrum cost to be the median of 3.x GHz European spectrum auctions from 2017 to 2023 (a total of 22 in my dataset). The auction median cost is found to be ca. 0.08 € per MHz-pop, and the interquartile range (as a measure for variation) is 0.08 € per MHz-pop. Using an average number of people per Western European household of 2.2, assuming a telco market share of 30%, and a 100 MHz bandwidth, the spectrum cost per connected household would be ca. 60 Euro (per HHC).
In general, the cost of connecting households to fiber scales directly (strongly) with the household density. The cost of connecting a household with fixed wireless access only scales very weakly with the household density (e.g., via CPA, CPE, technical support). Although, if the criteria are that FWA will have to continue to deliver a very high target speed and availability, as the household density increases, there will be substantial step function increases in the required Capex and subsequent resulting Opex. FWA TCO per connected house becomes prohibitively costly as the household density decreases, as is the case for normal cellular services as well.
The total cost of ownership (TCO) includes both the capital as well as the operational expenses relating to the technical implementation of the fixed (FTTH) and fiber-like broadband (5G FWA) service. The various components included in the TCO analysis are summarised in Figure 10.
Figure 10 illustrates the critical parameters used in this analysis and their drivers. As explained, all drivers are re-scaled to be consistent with the household connection. Rather than, for example, the number of households passed for fiber deployment or population coverage for cellular infrastructure deployment. Note 1: for a new 5G FWA site, “Active Equipment” should include a fiber connection & the associated backhaul and possible fronthaul transport equipment. This transport solution is assumed present for an existing site and not included in its economics.
In my analysis, I have compared the cost of implementing different FWA designs with that of connecting a household with fiber. I define a competitive 5G FWA service as a service that can provide a similar quality in terms of speed and stability as that of a GPON-based fiber connection would be able to. The fiber-to-the-home service is designed to bring up to 1 Gbps line speed to a household and could, with the right design, be extended to 10 Gbps with XGPON at a relatively low upgrade capital cost. The FWA service targets an effective speed of 300 Mbps. As household connections are added to the 5G FWA site, at some point, it would become unlikely that the targeted service level can be maintained unless substantial expansions are made to the 5G site, e.g., adding a mmWave solution with a jump in additional frequency spectrum (>100MHz). This would likely lead to additional unit Capex and increases in operational expenses (e.g., energy cost, possible technical support costs, etc..).
Figure 11 compares the TCO, Capex, and Opex of buried fiber to the home (FTTH) to that of fixed wireless access (FWA). For FTTH it is assumed that homes connected amount to 60% of homes passed, which is 90% of the actual household density. The designed FTTH network supports up to 1 Gbps. The FWA is based on LoS to connected homes assuming that I need a total of 6 sectors, one from an existing mobile site and a new 5G site only configured with 5G FWA. The LoS is closed by beamforming from a 64×64 massive MiMo antenna configuration (per sector), with provisioned 100 MHz bandwidth at 3.x GHz, to the customer premise antenna (CPA) installed optimally on the customer household. It is assumed that 30% of covered households will subscribe to the service, and the network cover 98% of all households (with 3-LoS sectors per connected home). The FWA service targets an effective speed of up to 300 Mbps per household. As the number of connected homes increases, there will be a point where the actual serviced speed to the home will be less than 300 Mbps due to the load. The € 30(±8) per month is the Western Europe average cost of a minimum 250 Mbps fixed broadband package. The cities indicate the equivalent household densities. Note: the FWA Opex and, consequently its TCO is different from what has been presented in one of my LinkedIn posts recently. The reason for this is that I spend more time improving my FWA energy consumption model and added some more energy management and steering to my economical model. This is one of the most important cost drivers (also for 5G in general) and I suspect that much more will have to be done in this area to get the overall power consumption substantially down compared to the existing solutions we have today.
Assuming 6 cellular sectors for my chosen 5G FWA solution with 3 of those sectors being greenfield (e.g., abbreviated 3Si + 3Sn), Figure 11 shows that for 5G FWA at scale and targeting competitive services (in terms of quality and stability), is rarely a more economical solution (based on TCO) compared to fiber. Only at high household densities does 5G FWA become economically as attractive as fiber-to-the-home. Although the problem with 5G FWA at large household densities is, that the connection load may be too high to maintain the service design specifications, e.g., speed and availability, without substantial additional upgrades (e.g., mmWave, additional spectrum & sector densification). Even if 5G FWA on a per connected home is (much) more Capex efficient, the economics of Fiber deployment and household fiber connections are more scalable to the connected home than a fixed-like wireless service will be at low and medium household densely urbanized areas.
Relaxing the 5G FWA configuration will not help much as Figure 12 below illustrates. Only in cases where a single existing site (with 3 sectors) can offer a reasonable LoS scale to customer’s households may the TCO be brought down to a comparable range as that of fiber to the home (for a given household density, that is). Using Professor Al-Hourani results one can show that if no receiving household point (e.g., height of building + antenna) is heigher than 15 meter (max. three story buildings) the maximum amount of households with LoS should be no more than 20%. Given that in more rural and suburban environment buildings may be more likely to be a lot lower in exterior height than 15 meter (e.g., 5 – 10 meters) the number of households with LoS (from a single point) could be substantially lower than 20%. In addition, to having a LoS to a household, it, of course, also needs to be your customers premise. Say you have a market share of 30%, one should not expect within a given coverage area to have a potential of more than maybe a maximum of 6% (and likely a lot lower than that). This of course makes any dedicated 5G FWA investment prohibitedly costly due to the lack of scale.
Figure 12 above illustrates a coverage area of 500 connected households and, thus, a relatively dense urban coverage area. FTTH has an uptake of 60% of homes passed, and 5G FWA has a market share of 30% within the covered area. The fiber is relatively straightforward and can be either based on buried or aerial fiber. The depicted figure is based on buried fiber homes connected (FTTH). For FWA we have several options to cover households; (3Si) is based on having 3 sectors with LOS to all household customers. All three sectors are upgraded to support 5G FWA. Based on existing mobile networks and FWA at scale, this would unlikely be the situation. (1Si) is based on one sector covering all connected households (in principle with LoS). One existing sector is upgraded to support 5G FWA. Unless the operator only picks HH with good coverage (e.g., LoS to a given sector) then this scenario appears even more unlikely than the (3Si) scenario at this scale of connected homes, (3Si+3Sn) is based on having an existing site with 3 sectors as well requiring a new 3-sectors site to provide LoS household coverage within the service area. This is also the basis for the FWA cost curves in Figure 10, (3Si+6Sn) based on having an existing site with 3 sectors and requiring two new 3-sectors sites (i.e., additional 6 sectors) to provide LoS household coverage within the service area. Finally, the TCO is compared with (M) a normal mobile 3-sectored 4G & 5G site TCO. The mobile TCO has been normalized to mobile customers assuming a market share of 30%. Note (*): The TCO for the FTTH and all FWA comparisons are based on TCO relative to households connected (HHC).
All in all, using dedicated 5G FWA (or 4G FWA, for that matter) is unlikely to be as economical as a household fiber connection. In rural and suburban areas, where the load may be less of an issue, the existing cellular network’s intercellular distances tend to be too large to be directly usable for fiber-like services. Thus, requiring site densification. In denser urban areas, the connection load may require additional investment to support the demand and maintain quality (e.g., mmWave solutions). However, these places may also be the areas most likely already to be covered by fiber or high quality HFC.
Irrespective of FWA’s maybe poorer economics, in comparison with fiber deployment, there are many countries in Western Europe (and a lot of other places) that lack comprehensive fiber coverage in both urban, suburban and rural areas. Areas that may only be covered by mediocor xDSL services and whatever broadband mobile coverage support. Geograophical areas where fiber may only be deployed years from now if ever at all (unless encourage by EU or other non-commercial subsidies). Such under-served fiber areas may still be commercially interesting for cellular infrastructure telcos, levering existing infra, or dedicated FWA ISPs that may have gotten their hands on lower cost mmWave spectrum.
I should also point out that there is plenty of opportunity for operational expense improvements by deploying for example more intelligent power management systems and/or simply switching off-and-on antenna elements (in the deployed AAS/massive-MiMo antennas) in off-peak traffic hours. The service level that is offered to FWA customers may also be optimized by modern care solutions, e.g., AI chatbots, Apps, IVR, WiFi optimizer solutions, … reducing the need for human-human technical support interactions. However, assuming an FWA customer require a customer premise antenna, requires connectivity to indoor gateway and high quality WiFi coverage in the household, is likely to result in Opex increase in customer care.
IN THE NOW THOUGHTS
I don’t see, FWA, 5G or not, as a credible alternative for fiber to the home. It is doubtful on a household-connection basis that it economically is a better choice. The argument that there is an incredible amount of underutilized resources in our cellular networks, so why not use all that for providing fixed-like, and maybe even fiber-like, services to rural and suburban households, is trying to avoid being held responsible for having possible wasted shareholders money and value but focusing more on being the best irrespective of whether value-generating demand was present or not.
FWA and FMS are technology options that may bridge a time where fiber becomes available in a given geographical footprint. It may act as a precursor for broadband demand that can trigger an accelerate uptake of fiber broadband services once the households have been fiber covered. But its nature as a fiber-like service is likely temporary albeit it may be around for several technology refreshment cycles.
Though, the cellular industry will have to address the relative high operational costs associated with a cellular solution targeting fixed- and fiber-like broadband (and to be honest mobile broadband as well) in comparison with fiber-to-the-home Opex. The projected energy cost of 5G (and 6G for that matter) ecosystem is simply not sustainable nor should it be acceptable to the industry. While suppliers are quick to address the massive improvement in energy consumption per bit-rate per new technology generation, what really is relevant for the network economics is the absolute consumption.
Finally, In time and day, where sustainability and reduction of wasteful demand on critical resources is of incredible importance to our industry, not only for our children’s children but also for achieving favorable financing, shareholders & investors money, consumer trust (and their money month upon month), and possibly the executives self-image, its is difficult to understand why any telco would not prioritize their fiber deployment or fiber service uptake over an incredible resource demanding 5G FWA to either compete or substitute much greener or substantially more sustainable fiber-based services.
ACKNOWLEDGEMENT.
I greatly acknowledge my wife Eva Varadi, for her support, patience, and understanding during the creative process of writing this Blog. Of course, a lot of thanks go out to my former Technology and Network Economics colleagues, who have been a source of inspiration and knowledge. Special thank you to Maurice Ketel (who for many years let my Technology Economics Unit in Deutsche Telekom, I respect him above and beyond), Paul Borker, Remek Prokopik, Michael Dueser, Gudrun Bobzin, as well as many many other industry colleagues who have contributed with valuable discussions and important insights. Of course, I can also not get away with (not that I ever would) not thanking Petr Ledl (leading DTAG’s Research & Trials) and Jaroslav Holis (R&T DTAG) for their willingness and a great deal of patience with my many questions into the nature of advanced antenna systems, massive MiMo, what the performance is today and what to expect in terms of performance in the near future. Any mistakes or misrepresentations of these technologies in this article is solely due to me.
Tom Copeland & Vladimir Antikarov, “Real Options – A practitioner’s Guide”, Texere (2001). My book version is first edition. It is an excellent book, and maybe one of the best written on Real Options. However, the 1st edition is full of mistakes & errors corrected in later editions. Make sure, if you acquire this book, to choose a later than 1st edition.
Full disclosure … when I was first introduced to the concept of Network Slicing, from one of the 5G fathers that I respect immensely (Rachid, it must have been back at the end of 2014), I thought that it was one of the most useless concepts that I had heard of. I did simply not see (or get) the point of introducing this level of complexity. It did not feel right. My thoughts were that taking the slicing concept to the limit might actually not make any difference to not having it, except for a tremendous amount of orchestration and management overhead (and, of course, besides the technological fun of developing it and getting it to work).
It felt a bit (a lot, actually) as a “let’s do it because we can” thinking. With the “We can” rationale based on the maturity of cloudification and softwarization frameworks, such as cloud-native, public-cloud scale, cloud computing (e.g., edge), software-defined networks (SDN), network-function virtualization (NFV), and the-one-that-is-always-named Artificial Intelligence (AI). I believed there could be other ways to offer the same variety of service experiences without this additional (what I perceived as an unnecessary) complexity. At the time, I had reservations about its impact on network planning, operations, and network efficiency. Not at all sure, it would be a development in the right economic direction.
Since then, I have softened to the concept of Network Slicing. Not (of course) that I have much choice, as slicing is an integral part of 5G standalone (5G) implementation that will be implemented and launched over the next couple of years across our industry. Who knows, I may very likely be proven very wrong, and then I learn something.
What is a network slice? We can see a network slice as an on-user-demand logical separated network partitioning, software-defined on-top of our common physical network infrastructure (wam … what a mouthful … test me out on this one next time you see me), slicing through our network technology stack and its layers. Thinking of a virtual private network (VPN) tunnel through a transport network is a reasonably good analogy. The network slice’s logical partitioning is isolated from other traffic streams (and slices) flowing through the 5G network. Apart from the slice logical isolation, it can have many different customizations, e.g., throughput, latency, scale, Quality of Service, availability, redundancy, security, etc… The user equipment initiates the slice request from a list of pre-defined slice categories. Assuming the network is capable of supporting its requirements, the chosen slice category is then created, orchestrated, and managed through the underlying physical infrastructure that makes up the network stack. The pre-defined slice categories are designed to match what our industry believe is the most essential use-cases, e.g., (a) enhanced mobile broadband use cases (eMBB), (b) ultra-reliable low-latency communications (uRLLC) use cases, (c) massive machine-type communication (MMTC) use cases, (d) Vehicular-to-anything (V2X) use-cases, etc… While the initial (early day) applications of network slicing are expected to be fairly static and configurationally relatively simple, infrastructure suppliers (e.g., Ericsson, Huawei, Nokia, …)expect network slices to become increasingly dynamic and rich in their configuration possibilities. While slicing is typically evoked for B2B and B2B2X, there is not really a reason why consumers could not benefit from network slicing as well (e.g., gaming/VR/AR, consumer smart homes, consumer vehicular applications, etc..).
Show me the money!
Ericsson and Arthur D. Little (ADL) have recently investigated the network slicing opportunities for communications service providers (CSP). Ericsson and ADL have analyzed more than 70 external market reports on the global digitalization of industries and critically reviewed more than 400 5G / digital use cases (see references in Further Readings below). They conclude that the demand from digitalization cannot be served by CSPs without Network Slicing, e.g., “Current network resources cannot match the increasing diversity of demands over time” and “Use cases will not function” (in a conventional mobile network). Thus, according to Ericsson and ADL, the industry can not “live” without Network Slicing (I guess it is good that it comes with 5G SA then). In fact, from their study, they conclude that 30% of the 5G use cases explored would require network slicing (oh joy and good luck that it will be in our networks soon).
Ericsson and ADL find globally a network slicing business potential of 200 Billion US dollars by 2030 for CSPs. With a robust CAGR (i.e., the potential will keep growing) between 23% to 36% by 2030 (i.e., CAGR estimate for period 2025 to 2030). They find that 6 Industries segments take 90+% of the slicing potential; (1) Healthcare (23%), (2) Government (17%), (3) Transportation (15%), (4) Energy & Utilities (14%), (5) Manufacturing (12%) and (6) Media & Entertainment (11%). For the keen observer, we see that the verticals are making up for most of the slicing opportunities, with only a relatively small part being related to the consumers. It should, of course, be noted that not all CSPs are necessarily also mobile network operators (MNOs), and there are also outside the strict domain of MNOs revenue potential for non-MNO CSPs (I assume).
Let us compare this slicing opportunity to global mobile industry revenue projections from 2020 to 2030. GSMA has issued a forecast for mobile revenues until 2025, expecting a total turnover of 1,140 Billion US$ in 2025 at a CAGR (2020 – 2025) of 1.26%. Assuming this compounded annual growth rate would continue to apply, we would expect a global mobile industry revenue of 1,213 Bn US$ by 2030. Our 5G deployments will contribute in the order of 621 Bn US$ (or 51% of the total). The incremental total mobile revenue between 2020 and 2030 would be ca. 140 Bn US$ (i.e., 13% over period). If we say that roughly 20% is attributed to mobile B2B business globally, we have that by 2030 we would expect a B2B turnover of 240+ Bn US$ (an increase of ca. 30 Bn US$ over 2020). So, Ericsson & ADL’s 200 Bn US$ network slicing potential is then ca. 16% of the total 2030 global mobile industry turnover or 30+% of the 5G 2030 turnover. Of course, this assumes that somehow the slicing business potential is simply embedded in the existing mobile turnover or attributed to non-MNO CSPs (monetizing the capabilities of the MNO 5G SA slicing enablers).
Of course, the Ericsson-ADL potential could also be an actual new revenue stream untapped by today’s network infrastructures due to the lack of slicing capabilities that 5G SA will bring in the following years. If so, we can look forward to a boost of the total turnover of 16% over the GSMA-based 2030 projection. Given ca. 90% of the slicing potential is related to B2B business, it may imply that B2B mobile business would almost double due to network slicing opportunities (hmmm).
Another recent study assessed that the global 5G network slicing market will reach approximately 18 Bn US$ by 2030 with a CAGR of ca. 41% over 2020-2030.
Irrespective of the slicing turnover quantum, it is unlikely that the new capabilities of 5G SA (including network slicing and much richer granular quality of service framework) will lead to new business opportunities and enable unexplored use cases. That, in turn, may indeed lead to enhanced monetization opportunities and new revenue streams between now (2022) and 2030 for our industry.
Most Western European markets will see 5G SA being launched over the next 2 to 3 years; as 5G penetration rapidly approaches 50% penetration, I expect network slicing use cases being to be tried out with CSP/MNOs, industry partners, and governmental institutions soon after 5G SA has been launched. It should be pointed out that already for some years, slicing concepts have been trialed out in various settings. Both in 4G as well as 5G NSA networks.
Prologue to Network Slicing.
5G comes with a lot of fundamental capabilities as shown in the picture below,
5G allows for (1) enhanced mobile broadband, (2) very low latency, (3) massive increase in device density handling, i.e., massive device scale-up, (4) ultra-higher network reliability and service availability, and (5) enhanced security (not shown in the above diagram) compared to previous Gs.
The service (and thus network) requirement combinations are very high. The illustration below shows two examples of mapped-out sub-set of service (and therefore also eventually slice) requirements mapped onto the major 5G capabilities. In addition, it is quite likely that businesses would have additional requirements related to slicing performance monitoring, for example, in real-time across the network stack.
and with all the various industrial or vertical use cases (see below) one could imagine (noting that there may be many many more outside our imagination), the “fathers” of 5G became (very) concerned with how such business-critical services could be orchestrated and managed within a traditional mobile network architecture as well as across various public land mobile networks (PLMN). Much of this also comes out of the wish that 5G should “conquer” (take a slice of) next-generation industries (i.e., Industry 4.0), providing additional value above and beyond “the dumb bit pipe.” Moreover, I do believe that in parallel with the wish of becoming much more relevant to Industry 4.0 (and the next generation of verticals requirements), what also played a role in the conception of network slicing is the deeply rooted engineering concept of “control being better than trust” and that “centralized control is better than decentralized” (I lost count on this debate of centralized control vs. distributed management a long time ago).
So, yes … The 5G world is about to get a lot more complex in terms of Industrial use cases that 5G should support. And yes, our consumers will expect much higher download speeds, real-time (whatever that will mean) gaming capabilities, and “autonomous” driving …
“… it’s clear that the one shared public network cannot meet the needs of emerging and advanced mobile connectivity use cases, which have a diverse array of technical operations and security requirements.” (quote from Ericsson and Arthur D. Little study, 2021).
“The diversity of requirements will only grow more disparate between use cases — the one-size-fits-all approach to wireless connectivity will no longer suffice.” (quote from Ericsson and Arthur D. Little study, 2021).
Being a naturalist (yes, I like “naked” networks), it does seem somewhat odd (to me) to say that next generation (e.g., 5G) networks cannot support all the industrious use cases that we may throw at it in its native form. Particular after having invested billions in such networks. By partitioning a network up in limiting (logically isolated), slice instances can all be supported (allegedly). I am still in the thinking phase on that one (but I don’t think the math adds up).
Now, whether one agrees (entirely) with the economic sentiment expressed by Ericsson and ADL or not. We need a richer granular way of orchestrating and managing all those diverse use-cases we expect our 5G network to support.
Network Slicing.
So, we have (or will get) network slicing with our 5G SA Core deployment. As a reminder, when we talk about a network slice, we mean;
“An on-user-demandlogicalseparated network partitioning, software-defined, on-top of a common physical network infrastructure.”
So, the customer requested the network slice, typically via a predefined menu of slicing categories that may also have been pre-validated by the relevant network. Requested slices can also be Customized,by the requester, within the underlying 5G infrastructure capabilities and functionalities. If the network can provide the requested slicing requirements, the slice is (in theory) granted. The core network then orchestrates a logically separated network partitioning throughout the relevant infrastructure resources to comply with the requested requirements (e.g., speed, latency, device scale, coverage, security, etc…). The requested partitioning (i.e., the slice) is isolated from other slices to enable (at least on a logical level) independence of other live slices. Slice Isolation is an essential concept to network slicing. Slice Elasticity ensures that resources can be scaled up and down to ensure individual slice efficiency and an overall efficient operation of all operating slices. It is possible to have a single individual network slice or partition a slice into sub-slices with their individual requirements (that does not breach the overarching slice requirements). GSMA has issued roaming and inter-PLMN guidelines to ensure 5G network slicing inter-operability when a customer’s application finds itself outside its home -PLMN.
Today, and thanks to GSMA and ITU, there are some standard network slice services pre-defined, such as (a) eMBB – Enhanced Mobile Broadband, (b) mMTC – Massive machine-type communications, (c) URLLC – Ultra-reliable low-latency communications, (d) V2X – Vehicular-to-anything communications. These identified standard network slices are called Slice Service Types (SST). SSTs are not only limited to above mentioned 4 pre-defined slice service types. The SSTs are matched to what is called a Generic Slice Template (GST) that currently, we have 37 slicing attributes, allowing for quite a big span of combinations of requirements to be specified and validated against network capabilities and functionalities (maybe there is room for some AI/ML guidance here).
The user-requested network slice that has been set up end-2-end across the network stack, between the 5G Core and the user equipment, is called the network slice instance. The whole slice setup procedure is very well described in Chapter 12 of “5G NR and enhancements, from R15 to R16. The below illustration provides a high-level illustration of various network slices,
The 5G control function Access and Mobility management Function (AMF) is the focal point for the network slicing instances. This particular architectural choice does allow for other slicing control possibilities with a higher or lower degree of core network functionality sharing between slice instances. Again the technical details are explained well in some of the reading resources provided below. The takeaway from the above illustration is that the slice instance specifications are defined for each layer and respective physical infrastructure (e.g., routers, switches, gateways, transport device in general, etc…) of the network stack (e.g., Telco Core Cloud, Backbone, Edge Cloud, Fronthaul, New Radio, and its respective air-interface). Each telco stack layer that is part of a given network slice instance is supposed to adhere strictly to the slice requirements, enabling an End-2-End, from Core to New Radio through to the user equipment, slice of a given quality (e.g., speed, latency, jitter, security, availability, etc..).
And it may be good to keep in mind that although complex industrial use cases get a lot of attention, voice and mobile broadband could easily be set up with their own slice instances and respective quality-of-services.
Network slicing examples.
All the technical network slicing “stuff” is pretty much-taken care of by standardization and provided by the 5G infrastructure solution providers (e.g., Mavenir, Huawei, Ericsson, Nokia, etc..). Figuring the technical details of how these works require an engineering or technical background and a lot of reading.
As I see it, the challenge will be in figuring out, given a use-case, the slicing requirements and whether a single slice instance suffice or multiple are required to provide the appropriate operations and fulfillment. This, I expect, will be a challenge for both the mobile network operator as well as the business partner with the use case. This assumes that the economics will come out right for more complex (e.g., dynamic) and granular slice-instance use cases. For the operator as well as for businesses and public institutions.
The illustration below provides examples of a few (out of the 37) slicing attributes for different use cases, (a) Factories with time-critical, non-time-critical, and connected goods sub-use cases (e.g., sub-slice instances, QoS differentiated), (b) Automotive with autonomous, assisted and shared view sub-use cases, (c) Health use cases, and (d) Energy use cases.
One case that I have been studying is Networked Robotics use cases for the industrial segment. Think here about ad-hoc robotic swarms (for agricultural or security use cases) or industrial production or logistics sorting lines; below are some reflections around that.
End thoughts.
With the emergence of the 5G Core, we will also get the possibility to apply Network slicing to many diverse use cases. That there are interesting business opportunities with network slicing, I think, is clear. Whether it will add 16% to the global mobile topline by 2030, I don’t know and maybe also somewhat skeptical about (but hey, if it does … fantastic).
Today, the type of business opportunities that network slicing brings in the vertical segments is not a very big part of a mobile operator’s core competence. Mobile operators with 5G network slicing capabilities ultimately will need to build up such competence or (and!) team up with companies that have it.
That is, if the future use cases of network slicing, as envisioned by many suppliers, ultimately will get off the ground economically as well as operationally. I remain concerned that network slicing will not make operators’ operations less complex and thus will add cost (and possible failures) to their balance sheets. The “funny” thing (IMO) is that when our 5G networks are relatively unloaded, we would not have a problem delivering the use cases (obviously). Once our 5G networks are loaded, network slicing may not be the right remedy to manage traffic pressure situations or would make the quality we are providing to consumers progressively worse (and I am not sure that business and value-wise, this is a great thing to do). Of course, 6G may solve all those concerns 😉
Acknowledgement.
I greatly acknowledge my wife, Eva Varadi, for her support, patience, and understanding during the creative process of writing this Blog. Also, many of my Deutsche Telekom AG and Industry colleagues, in general, have in countless ways contributed to my thinking and ideas leading to this little Blog. Thank you!
Jia Shen, Zhongda Du, & Zhi Zhang, “5G NR and enhancements, from R15 to R16”, Elsevier Science, (2021). Provides a really good overview of what to expect from 5G standalone. Chapter 12 provides a good explanation of (and in detail account for) how 5G Network Slicing works in detail. Definitely one of my favorite books on 5G, it is not “just” an ANRA.
Claudia Campolo, Antonella Molinaro, Antonio Lera, and Francesco Menichella, “5G Network Slicing for Vehicle-to-Everything Services”, IEEE Wireless Communications 24, (December 2017). Great account of how network slicing should work for V2X services.
GSMA, “Securing the 5G Era” (2021). A good overview of security principles in 5G and how previous vulnerabilities in previous cellular generations are being addressed in 5G. This includes some explanation on why slicing further enhances security.
By the end of 2020, according with Ericsson, it was estimated that there where ca. 7.6 million 5G subscriptions in Western Europe (~ 1%). Compare this to North America’s ca. 14 million (~4%) and 190 million (~11%) North East Asia (e.g, China, South Korea, Japan, …).
Maybe Western Europe is not doing that great, when it comes to 5G penetration, in comparison with other big regional markets around the world. To some extend the reason may be that 4G network’s across most of Western Europe are performing very well and to an extend more than servicing consumers demand. For example, in The Netherlands, consumers on T-Mobile’s 4G gets, on average, a download speed of 100+ Mbps. About 5× the speed you on average would get in USA with 4G.
From the October 2021 statistics of the Global mobile Suppliers Association (GSA), 180 operators worldwide (across 72 countries) have already launched 5G. With 37% of those operators actively marketing 5G-based Fixed Wireless Access (FWA) to consumers and businesses. There are two main 5G deployment flavors; (a) non-standalone (NSA) deployment piggybacking on top of 4G. This is currently the most common deployment model, and (b) as standalone (SA) deployment, independently from legacy 4G. The 5G SA deployment model is to be expected to become the most common over the next couple of years. As of October 2021, 15 operators have launched 5G SA. It should be noted that, operators with 5G SA launched are also likely to support 5G in NSA mode as well, to provide 5G to all customers with a 5G capable handset (e.g., at the moment only 58% of commercial 5G devices supports 5G SA). Only reason for not supporting both NSA and SA would be for a greenfield operator or that the operator don’t have any 4G network (none of that type comes to my mind tbh). Another 25 operators globally are expected to be near launching standalone 5G.
It should be evident, also from the illustration below, that mobile customers globally got or will get a lot of additional download speed with the introduction of 5G. As operators introduce 5G, in their mobile networks, they will leapfrog their available capacity, speed and quality for their customers. For Europe in 2021 you would, with 5G, get an average downlink (DL) speed of 154 ± 90 Mbps compared to 2019 4G DL speed of 26 ± 8 Mbps. Thus, with 5G, in Europe, we have gained a whooping 6× in DL speed transitioning from 4G to 5G. In Asia Pacific, the quality gain is even more impressive with a 10× in DL speed and somewhat less in North America with 4× in DL speed. In general, for 5G speeds exceeding 200 Mbps on average may imply that operators have deployed 5G in the C-band band (e.g., with the C-band covering 3.3 to 5.0 GHz).
The above DL speed benchmark (by Opensignal) gives a good teaser for what to come and to expect from 5G download speed, once a 5G network is near you. There is of course much more to 5G than downlink (and uplink) speed. Some caution should be taken in the above comparison between 4G (2019) and 5G (2021) speed measurements. There are still a fair amount of networks around the world without 5G or only started upgrading their networks to 5G. I would expect the 5G average speed to reduce a bit and the speed variance to narrow as well (i.e., performance becoming more consistent).
In a previous blog I describe what to realistically expect from 5G and criticized some of the visionary aspects of the the original 5G white paper paper published back in February 2015. Of course, the tech-world doesn’t stand still and since the original 5G visionary paper by El Hattachi and Erfanian. 5G has become a lot more tangible as operators deploy it or is near deployment. More and more operators have launched 5G on-top of their 4G networks and in the configuration we define as non-standalone (i.e., 5G NSA). Within the next couple of years, coinciding with the access to higher frequencies (>2.1 GHz) with substantial (unused or underutilized) spectrum bandwidths of 50+ MHz, 5G standalone (SA) will be launched. Already today many high-end handsets support 5G SA ensuring a leapfrog in customer experience above and beyond shear mobile broadband speeds.
The below chart illustrates what to expect from 5G SA, what we already have in the “pocket” with 5G NSA, and how that may compare to existing 4G network capabilities.
There cannot be much doubt that with the introduction of the 5G Core (5GC) enabling 5G SA, we will enrich our capability and service-enabler landscape. Whether all of this cool new-ish “stuff” we get with 5G SA will make much top-line sense for operators and convenience for consumers at large is a different story for a near-future blog (so stay tuned). Also, there should not be too much doubt that 5G NSA already provide most of what the majority of our consumers are looking for (more speed).
Overall, 5G SA brings benefits, above and beyond NSA, on (a) round-trip delay (latency) which will be substantially lower in SA, as 5G does not piggyback on the slower 4G, enabling the low latency in ultra-reliable low latency communications (uRLLC), (b) a factor of 250× improvement device density (1 Million devices per km2) that can be handled supporting massive machine type communication scenarios (mMTC), (c) supports communications services at higher vehicular speeds, (d) in theory should result in low device power consumption than 5G NSA, and (e) enables new and possible less costly ways to achieve higher network (and connection) availability (e.g., with uRLLC).
Compared to 4G, 5G SA brings with it a more flexible, scalable and richer set of quality of service enablers. A 5G user equipment (UE) can have up to 1,024 so called QoS flows versus a 4G UE that can support up to 8 QoS classes (tied into the evolved packet core bearer). The advantage of moving to 5G SA is a significant reduction of QoS driven signaling load and management processing overhead, in comparison to what is the case in a 4G network. In 4G, it has been clear that the QoS enablers did not really match the requirements of many present day applications (i.e., brutal truth maybe is that the 4G QoS was outdated before it went live). This changes with the introduction of 5G SA.
So, when is it a good idea to implement 5G Standalone for mobile operators?
There are maybe three main events that should trigger operators to prepare for and launch 5G SA;
Economical demand for what 5G SA offers.
Critical mass of 5G consumers.
Want to claim being the first to offer 5G SA.
with the 3rd point being the least serious but certainly not an unlikely factor in deploying 5G SA. Apart from potentially enriching consumers experience, there are several operational advantages of transitioning to a 5GC, such as more mature IT-like cloudification of our telecommunications networks (i.e., going telco-cloud native) leading to (if designed properly) a higher degree of automation and autonomous network operations. Further, it may also allow the braver parts of telco-land to move a larger part of its network infrastructure capabilities into the public-cloud domain operated by hyperscalers or network-cloud consortia’s (if such entities will appear). Another element of the 5G SA cloud nativification (a new word?) that is frequently not well considered, is that it will allow operators to start out (very) small and scale up as business and consumer demand increases. I would expect that particular with hyperscalers and of course the-not-so-unusual-telco-supplier-suspects (e.g., Ericsson, Nokia, Huawei, Samsung, etc…), operators could launch fairly economical minimum viable products based on a minimum set of 5G SA capabilities sufficient to provide new and cost-efficient services. This will allow early entry for business-to-business new types of QoS and (or) slice-based services based on our new 5G SA capabilities.
Western Europe mobile market expectations – 5G technology share.
By end of 2021, it is expected that Western Europe would have in the order of 36 Million 5G connections, around a 5% 5G penetration. Increasing to 80 Million (11%) by end of 2022. By 2024 to 2025, it is expected that 50% of all mobile connections would be 5G based. As of October 2021 ca. 58% of commercial available mobile devices supports already 5G SA. This SA share is anticipated to grow rapidly over the next couple of years making 5G NSA increasingly unimportant.
Approaching 50% of all connections being 5G appears a very good time to aim having 5G standalone implemented and launched for operators. Also as this may coincide with substantial efforts to re-farming existing frequency spectrum from 4G to 5G as 5G data traffic exceeds that of 4G.
For Western Europe 2021, ca. 18% of the total mobile connections are business related. This number is expected to steadily increase to about 22% by 2030. With the introduction of new 5G SA capabilities, as briefly summarized above, it is to be expected that the 5G business connection share quickly will increase to the current level and that business would be able to directly monetize uRLLC, mMTC and the underlying QoS and network slicing enablers. For consumers 5G SA will bring some additional benefits but maybe less obvious new monetization possibilities, beyond the proportion of consumers caring about latency (e.g., gamers). Though, it appears likely that the new capabilities could bring operators efficiency opportunities leading to improved margin earned on consumers (for another article).
Recommendation:
Learn as much as possible from recent IT cloudification journeys (e.g., from monolithic to cloud, understand pros and cons with lift-and-shift strategies and the intricacies of operating cloud-native environments in public cloud domains).
Aim to have 5GC available for 5G SA launch latest by 2024.
Run 5GC minimum viable product poc’s with friendly (business) users prior to bigger launch.
As 5G is launched on C-band / 3.x GHz it may likewise be a good point in time to have 5G SA available. At least for B2B customers that may benefit from uRLLC, lower latency in general, mMTC, a much richer set of QoS, network slicing, etc…
Having a solid 4G to 5G spectrum re-farming strategy ready between now and 2024 (too late imo). This should map out 4G+NSA and SA supply dynamics as increasingly customers get 5G SA capabilities in their devices.
Western Europe mobile market expectations – traffic growth.
With the growth of 5G connections and the expectation that 5G would further boost the mobile data consumption, it is expected that by 2023 – 2024, 50% of all mobile data traffic in Western Europe would be attributed to 5G. This is particular driven by increased rollout of 3.x GHz across the Western European footprint and associated massive MiMo (mMiMo) antenna deployments with 32×32 seems to be the telco-lands choice. In blended mobile data consumption a CAGR of around 34% is expected between 2020 and 2030, with 2030 having about 26× more mobile data traffic than that of 2020. Though, I suspect that in Western Europe, aggressive fiberization of telecommunications consumer and business markets, over the same period, may ultimately slow the growth (and demand) on mobile networks.
A typical Western European operator would have between 80 – 100+ MHz of bandwidth available for 4G its downlink services. The bandwidth variation being determined by how much is required of residual 3G and 2G services and whether the operator have acquired 1500MHz SDL (supplementary downlink) spectrum. With an average 4G antenna configuration of 4×4 MiMo and effective spectral efficiency of 2.25 Mbps/MHz/sector one would expect an average 4G downlink speed of 300+ Mbps per sector (@ 90 MHz committed to 4G). For 5G SA scenario with 100 MHz of 3.x GHz and 2×10 MHz @ 700 MHz, we should expect an average downlink speed of 500+ Mbps per sector for a 32×32 massive MiMo deployment at same effective spectral efficiency as 4G. In this example, although naïve, quality of coverage is ignored. With 5G, we more than double the available throughput and capacity available to the operator. So the question is whether we remain naïve and don’t care too much about the coverage aspects of 3.x GHz, as beam-forming will save the day and all will remain cheesy for our customers (if something sounds too good to be true, it rarely is true).
In an urban environment it is anticipated that with beam-forming available in our mMiMo antenna solutions downlink coverage will be reasonably fine (i.e., on average) with 3.x GHz antennas over-layed on operators existing macro-cellular footprint with minor densification required (initially). In the situation that 3.x GHz uplink cannot reach the on-macro-site antenna, the uplink can be closed by 5G @ 700 MHz, or other lower cellular frequencies available to the operator and assigned to 5G (if in standalone mode). Some concerns have been expressed in literature that present advanced higher order antenna’s (e.g., 16×16 and above ) will on average provide a poorer average coverage quality over a macro cellular area than what consumers would be used to with lower order antennas (e.g., 4×4 or lower) and that the only practical (at least with today’s state of antennas) solution would be sectorization to make up for beam forming shortfalls. In rural and sub-urban areas advanced antennas would be more suitable although the demand would be a lot less than in a busy urban environment. Of course closing the 3.x GHz with existing rural macro-cellular footprint may be a bigger challenge than in an urban clutter. Thus, massive MiMo deployments in rural areas may be much less economical and business case friendly to deploy. As more and more operators deploy 3.x GHz higher-order mMiMo more field experience will become available. So stay tuned to this topic. Although I would reserve a lot more CapEx in my near-future budget plans for substantial more sectorization in urban clutter than what I am sure is currently in most operators plans. Maybe in rural and suburban areas the need for sectorizations would be much smaller but then densification may be needed in order to provide a decent 3.x GHz coverage in general.
Western Europe mobile market expectations – 5G RAN Capex.
That brings us to another important aspect of 5G deployment, the Radio Access Network (RAN) capital expenditures (CapEx). Using my own high-level (EU-based) forecast model based on technology deployment scenario per Western European country that in general considers 1 – 3% growth in new sites per anno until 2024, then from 2025 onwards, I assuming 2 – 5% growth due to densifications needs of 5G, driven by traffic growth and before mentioned coverage limitations of 3.x GHz. Exact timing and growth percentages depends on initial 5G commercial launch, timing of 3.x GHz deployment, traffic density (per site), and site density considering a country’s surface area.
According with Statista, Western Europe had in 2018 a cellular site base of 421 thousands. Further, Statista expected this base will grow with 2% per anno in the years after 2018. This gives an estimated number of cellular sites of 438k in 2020 that has been assumed as a starting point for 2020. The model estimates that by 2030, over the next 10 years, an additional 185k (+42%) sites will have been built in Western Europe to support 5G demand. 65% (120+k) of the site growth, over the next 10 years, will be in Germany, France, Italy, Spain and UK. All countries with relative larger geographical areas that are underserved with mobile broadband services today. Countries with incumbent mobile networks, originally based on 900 MHz GSM grids (of course densified since the good old GSM days), and thus having coarser cellular grids with higher degree of mismatching the higher 5G cellular frequencies (i.e., ≥ 2.5 GHz). In the model, I have not accounted for an increased demand of sectorizations to keep coverage quality upon higher order mMiMO deployments. This, may introduce some uncertainty in the Capex assessment. However, I anticipate that sectorization uncertainty may be covered in the accelerated site demand the last 5 years of the period.
In the illustration above, the RAN capital investment assumes all sites will eventually be fiberized by 2025. That may however be an optimistic assumption and for some countries, even in Western Europe, unrealistic and possibly highly uneconomical. New sites, in my model, are always fiberized (again possibly too optimistic). Miscellaneous (Misc.) accounts for any investments needed to support the RAN and Fiber investments (e.g., Core, Transport, Cap. Labor, etc..).
In the economical estimation price erosion has been taken into account. This erosion is a blended figure accounting for annual price reduction on equipment and increases in labor cost. I am assuming a 5-year replacement cycle with an associated 10% average price increase every 5 years (on the previous year’s eroded unit price). This accounts for higher capability equipment being deployed to support the increased traffic and service demand. The economical justification for the increase unit price being that otherwise even more new sites would be required than assumed in this model. In my RAN CapEx projection model, I am assuming rational deployment, that is demand driven deployment. Thus, operators investments are primarily demand driven, e.g., only deploying infrastructure required within a given financial recovery period (e.g., depreciation period). Thus, if an operator’s demand model indicate that it will need a given antenna configuration within the financial recovery period, it deploys that. Not a smaller configuration. Not a bigger configuration. Only the one required by demand within the financial recovery period. Of course, there may be operators with other deployment incentives than pure demand driven. Though on average I suspect this would have a neglectable effect on the scale of Western Europe (i.e., on average Western Europe Telco-land is assumed to be reasonable economically rational).
All in all, demand over the next 8 years leads to an 80+ Billion Euro RAN capital expenditure, required between 2022 and 2030. This, equivalent to a annual RAN investment level of a bit under 10 Billion Euro. The average RAN CapEx to Mobile Revenue over this period would be ca. 6.3%, which is not a shockingly high level (tbh), over a period that will see an intense rollout of 5G at increasingly higher frequencies and increasingly capable antenna configurations as demand picks up. Biggest threat to capital expenditures is poor demand models (or no demand models) and planning processes investing too much too early, ultimately resulting in buyers regret and cycled in-efficient investment levels over the next 10 years. And for the reader still awake and sharp, please do note that I have not mentioned the huge elephant in the room … The associated incremental operational expense (OpEx) that such investments will incur.
As mobile revenues are not expected to increase over the period 2022 to 2030, this leaves 5G investments main purpose to maintaining current business level dominated by consumer demand. I hope this scenario will not materialize. Given how much extra quality and service potential 5G will deliver over the next 10 years, it seems rather pessimistic to assume that our customers would not be willing to pay more for that service enhancement that 5G will brings with it. Alas, time will show.
Acknowledgement.
I greatly acknowledge my wife Eva Varadi for her support, patience and understanding during the creative process of writing this Blog. Petr Ledl, head of DTAG’s Research & Trials, and his team’s work has been a continuous inspiration to me (thank you so much for always picking up on that phone call Petr!). Also many of my Deutsche Telekom AG, T-Mobile NL & Industry colleagues in general have in countless of ways contributed to my thinking and ideas leading to this little Blog. Thank you!
Rachid El Hattachi & Javan Erfanian , “5G White Paper”, NGMN Alliance, (February 2015). See also “5G White Paper 2” by Nick Sampson (Orange), Javan Erfanian (Bell Canada) and Nan Hu (China Mobile).
Global Mobile Frequencies Database. (last update, 25 May 2021). I recommend very much to subscribe to this database (€595,. single user license). Provides a wealth of information on spectrum portfolios across the world.
Jia Shen, Zhongda Du, & Zhi Zhang, “5G NR and enhancements, from R15 to R16”, Elsevier Science, (2021). Provides a really good overview of what to expect from 5G standalone. Particular, very good comparison with what is provided with 4G and the differences with 5G (SA and NSA).
Ali Zaidi, Fredrik Athley, Jonas Medbo, Ulf Gustavsson, Giuseppe Durisi, & Xiaoming Chen, “5G Physical Layer Principles, Models and Technology Components”, Elsevier Science, (2018). The physical layer will always pose a performance limitation on a wireless network. Fundamentally, the amount of information that can be transferred between two locations will be limited by the availability of spectrum, the laws of electromagnetic propagation, and the principles of information theory. This book provides a good description of the 5G NR physical layer including its benefits and limitations. It provides a good foundation for modelling and simulation of 5G NR.
Thomas L. Marzetta, Erik G. Larsson, Hong Yang, Hien Quoc Ngo, “Fundamentals of Massive MIMO”, Cambridge University Press, (2016). Excellent account of the workings of advanced antenna systems such as massive MiMo.
Western Europe: Western Europe has a bit of a fluid definition (I have found), here Western Europe includes the following countries comprising a population of ca. 425 Million people (in 2021); Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland United Kingdom, Andorra, Cyprus, Faeroe Islands, Greenland, Guernsey, Jersey, Malta, Luxembourg, Monaco, Liechtenstein, San Marino, Gibraltar.
As I am preparing for my keynote speech for the Annual Dinner event of the Telecom Society Netherlands (TSOC) end of January 2020, I thought the best way was to write down some of my thoughts on the key question “Is the ‘Uber’ moment for the telecom sector coming?”. In the end it turned out to be a lot more than some of my thoughts … apologies for that. Though it might still be worth reading, as many of those considerations in this piece will be hitting a telcos near you soon (if it hasn’t already).
Knowing Uber Technologies Inc’s (Uber) business model well (and knowing at least the Danish taxi industry fairly well as my family has a 70+ years old Taxi company, Radio-Taxi Nykoebing Sjaelland Denmark, started by my granddad in 1949), it instinctively appear to be an odd question … and begs the question “why would the telecom sector want an Uber moment?” … Obviously, we would prefer not to be massively loss making (as is the Uber moment at this and past moments, e.g., several billions of US$ loss over the last couple of years) and also not the regulatory & political headaches (although we have our own). Not to mention some of the negative reputation issues around “their” customer experience (quiet different from telco topics and thank you for that). Also not forgetting that Uber has access to only a fraction of the value chain in the markets the operate … Althans of course Uber is also ‘infinitely’ lighter in terms of assets than a classical Telco … Its also a bit easier to replicate an Uber (or platform businesses in general) than an asset-heavy Telco (as it requires a “bit” less cash to get started;-). But but … of course the question is more related to the type of business model Uber represent rather than the taxi / ride hailing business model itself. Thinking of Uber makes such a question more practical and tangible …
And not to forget … The super cool technology aspects of being a platform business such as Uber … maybe Telco-land can and should learn from platform businesses? … Lets roll!
uber Uber
Uber main business (ca. 81%) is facilitating peer-2-peer ride sharing and ride hailing services via their mobile application and its websites. Uber tabs into the sharing economy. Making use of under-utilized private cars and their owners (producers) willingness to give up hours of their time to drive others (consumers) around in their private vehicle. Uber had 95 million active users (consumers) in 2018 and is expected to reach 110 million in 2019 (22% CAGR between 2016 & 2019). Uber has around 3+ million drivers (producers) spread out over 85+ countries and 900+ cities around the world (although 1/3 is in the USA). In the third quarter of 2019, Uber did 1.77 billion trips. That is roughly 200 trips per Uber driver per month of which the median income is 155 US$ per month (1.27 US$ per trip) before gasoline and insurances. In December 2017, the median monthly salary for Americans was $3,714.
In addition Uber also provides food delivery services (i.e., Uber Eats, ca. 11%), Uber Freight services (ca. 7%) and what they call Other Bets (ca. 1%). The first 9 month of 2019, Uber spend more than 40% of the turnover on R&D. Uber has an average revenue per trip (ARPT) of ca. 2 US$ (out of 9.5 US$ per trip based on gross bookings). Not a lot of ARPT growth the last 9 quarters. Although active users (+30% YoY), trips (+31% YoY), Gross Bookings (+32%) and Adjusted Net Revenue (+35%) all shows double digit growth.
Uber allegedly takes a 25% fee of each fare (note: if you compare gross bookings, the total revenue generated by their services, to net revenue which Uber receives the average is around 20%).
Uber’s market cap, roughly 10 years after being founded, after its IPO was 76 Bn US$ (@ May 10th, 2019) only exceeded by Facebook (104.2 Bn @ IPO) and Alibaba Group (167.6 Bn US$ @ IPO). 7 month after Uber’s market cap is ca. 51 Bn US$ (-33% down on IPO). The leading European telco Deutsche Telekom AG (25 years old, 1995) in comparison has a market capitalization around 70 Bn US$ and is very far from loss making. Deutsche Telekom is one of the world’s leading integrated telecommunications companies, with some 170+ million mobile customers, 28 million fixed-network lines, and 20 million broadband lines.
Peal the Onion
“Telcos are pipe businesses, Ubers are platform businesses”
In other words, Telco’s are adhering to a classical business model with fairly linear causal value chain (see Michael Porter’s classic from 1985). It’s the type of input/output businesses that has been around since the dawn of the industrial revolution. Such a business model can (and should) have a very high degree of end-2-end customer experience control.
Ubers (e.g., Uber, Airbnb, Booking.com, ebay, Tinder, Minecraft, …) are non-linear business models that benefit from direct and indirect network effects allowing for exponential growth dynamics. Such businesses are often piggybacking on under-utilized or un-used assets owned by individuals (e.g., homes & rooms, cars, people time, etc…). Moreover, these businesses facilitate networked connectivity between consumers and producers via a digital platform. As such, platform businesses rarely have complete end-2-end customer experience control but would focus on the quality and experience of networked connectivity. While platform business have little control over their customers (i.e., consumers and producers) experiences or overall customer journey they may have indirectly via near real-time customer satisfaction feedback (although this is after the fact).
Clearly the internet has enabled many new ways of doing business. In particular it allows for digital businesses (infrastructure lite) to create value by facilitating networked-scaled business models where demand (i.e., customers demand XYZ) and supply (i.e., businesses supplying XYZ).
Think ofAirbnb‘s internet-based platform that connects (or networks) consumers (guests), who are looking for temporary accommodation (e.g., hotel room), with producers (hosts, private or corporate) of temporary accommodations to each other. Airbnb thus allow for value creation by tying into the sharing economy of private citizens. Under-utilized private property is being monetized, benefiting hosts (producers), guests (consumers) and the platform business (by charging a transactional fee). Airbnb charges hosts a 3% fee that mainly covers the payment processing cost. Moreover, Airbnb’s typical guest fee is under 13% of the booking cost. “Airbnb is a platform business built upon software and other peoples under-utilized homes & rooms”. While Airbnb facilitated private (temporary) accommodations to consumers, today there are other online platform businesses (e.g., Booking.com, Experia.com, agoda.com, … ) that facilitates connections between hotels and consumers.
Think of Uber‘sonline ride hailing platformconnects travelers (consumer) with drivers (producers, private or corporate) as an alternative to normal cab / taxi services. Uber benefits from the under-utilization of most private cars, the private owners willingness to spend spare time and desire to monetize this under-utilization by becoming a private cab driver. Again the platform business exploring the sharing economy. Uber charges their drivers 25% of the faring fee. “Uber is a platform business built upon software and other peoples under-utilized cars and spare time”.The word platform was used 747 times in Uber’s IPO document. After Uber launched its digital online ride hailing platform, many national and regional taxi applications have likewise been launched. Facilitating an easier and more convenient way of hauling a taxi, piggybacking on the penetration of smartphones in any given market. In those models official taxi businesses and licensed taxi drivers collaborate around an classical industry digital platform facilitating and managing dispatches on consumer demand.
“A platform business relies on the sharing economy, monetizing networking (i.e., connecting) consumers and producers by taking a transaction fee on the value of involved transaction flow.”
E.g., consumer pays producer, or consumer get service for free and producer pays the platform business. It is a highly scaleble business model with exponential potential for growth assuming consumers and producers alike adapt your platform. The platform business model tends to be (physical) infrastructure and asset lite and software heavy. It typically (in start-up phase at least) relies on commercially available cloud offering (e.g., Lyft relies on AWS, Uber on AWS & Google) or if the platform business is massively scaled (e.g., Facebook), the choice may be to own data center infrastructure to have better platform control over operations. Typically you will see that successful Platform businesses at scale implements hybrid cloud model levering commercially available cloud solutions and own data centers. Platform businesses tend to be heavily automated (which is relative easy in a modern cloud environment) and rely very significantly on monetizing their data with underlying state-of-the-art real-time big data systems and of course intelligent algorithmic (i.e., machine learning based) business support systems.
Consider this
A platform-business’s technology stack, residing in a cloud, will typically run on a virtual machine or within a so-called container engine. The stack really resides on the upper protocol layers and is transparent to lower level protocols (e..g, physical, link, network, transport, …). In general the platform stack can be understood to function on the 3 platform layers presented in the chart to the left; (top-platform-layer) Networked Marketplace that connects producers and consumers with each other. This layer describes how a platform business customers connect (e.g., mobile app on smartphone), (middle-platform-layer) Enabling Layer in which microservices, software tools, business logic, rules and so forth will reside, (bottom-platform-layer) the Big Data Layer or Data Layer with data-driven decision making are occurring often supported by advanced real-time machine learning applications. The remaining technology stuff (e.g., physical infrastructure, servers, storage, LAN/WAN, switching, fixed and mobile telco networking, etc..) is typically taken care of by cloud or data center providers and telco providers. Which is explains why platform businesses tends to be infrastructure or asset lite (and software heavy) compared to telco and data-center providers.
“Many classical linear businesses are increasingly copying the platform businesses digital strategies (achieving an improved operational excellence) without given up on their fundamental value-chain control. Thus allowing to continue to provide consumers a known and often improved customer experience compared to a pure platform business.”
So what about the Telco model?
Well, the Telco business model is adhering to a linear value chain and business logic. And unless you are thinking of a service telco provider or virtual telco operator, Telcos are incredible infrastructure and asset heavy with massive capital investments required to provide competitive services to their customers. Apart from the required capital intensive underlying telco technology infrastructure, the telco business model requires; (1) public licenses to operate (often auctioned, or purchased and rarely “free”), (2) requires (public) telephony numbers, (3) spectrum frequencies (i.e.,for mobile operation) and so forth …
Furthermore, overall customer experience and end-2-end customer journey is very important to Telcos (as it is to most linear businesses and most would and should subscribe to being very passionate about it). In comparison to Platform Businesses, it would not be an understatement (at this moment in time at least) to say that most Telco businesses are lagging on cloudification/softwarisation, intelligent automation (whether domain-based or End-2-End) and advanced algorithmic (i.e., machine learning enabled) decision making as it relates to overarching business decisions as well as customer-related micro-decisions. However, from an economical perspective we are not talking about more than 10% – 20% of a Telco’s asset base (or capital expenses).
Mobile telco operators tend to be fairly advanced in their approaches to customer experience management, although mainly reactive rather than pro-active (due to lower intelligent algorithmic maturity again in comparison to most platform businesses). In general, fixed telco businesses are relative immature in their approaches to customer experience management (compared to mobile operators) possibly due lack of historical competitive pressure (“why care when consumers have not other choice” mindset). Alas this too is changing as more competition in fixed telco-land emerges.
“Telcos have some technology catching up to do in comparison & where relevant with platform businesses. However, that catching up does not force them to change the fundamentals of their business model (unless it make sense of course).”
Characteristic of a Platform Business
Often relies on the sharing economy (i.e., monetizing under-utilized resources).
It’s (exponential) growth relies on successful networking of consumers & producers (i.e., piggybacking on network effects).
Software-centric: platform business is software and focus / relies on the digital domain & channels.
Mobile-centric: mobile apps for consumers & producers.
Cloud-centric: platform-solution built on Public or Hybrid cloud models.
Cloud-native maturity level (i.e., the highest cloud maturity level).
Heavily end-2-end automated across cloud-native platform, processes & decision making.
Highly sophisticated data-driven decision making.
Infrastructure / asset lite (at scale may involve own data center assets).
Business driven & optimized by state-of-art big data real-time solutions supported by a very high level of data science & engineering maturity.
Little or no end-2-end customer experience control (i.e., in the sense of complete customer journey).
Very strong focus on connection experience including payment process.
Revenue source may be in form of transactional fee imposed on the value involved in networking producers and consumers (e.g., payment transaction, cost-per-click, impressions, etc..).
In my opinion it is not a given that a platform business always have to disrupt an existing market (or classical business model). However, a successful platform business often will be transformative, resulting in classical business attempting to copy aspects of the platform business model (e..g, digitalization, automation, cloud transformation, etc..). It is too early in most platform businesses life-cycle to conclude whether, where they disrupt, it is a temporary disruption (until the disrupted have transformed) or a permanently destruction of an existing classical market model (i.e., leaving little or no time for transformation).
So with the above in mind (and I am sure for many other defining factors), it is hard to see a classical telco transforming itself into a carbon copy of a platform business and maybe more importantly why this would make a lot of sense to do in the first instance. But but … it is also clear that Telco-land should proudly copy what make sense (e.g., particular around tech and level of digitization).
Teaser thought Though if you think in terms of sharing economical principles, the freedom that an eSIM (or software-based SIM equivalents) provides with 5 or more network profiles may bring to a platform business going beyond traditional MVNOs or Service Providers … well well … you think! (hint: you may still need an agreement with the classical telco though … if you are not in the club already;-). Maybe a platform model could also tab into under-utilized consumer resources that the consumer has already paid for? or what about a transactional model on Facebook (or other social media) where the consumer actual monetizes (and controls) personal information directly with third party advertisers? (actually in this model the social media company could also share part of its existing spoil earned on their consumer product, i.e., the consumer) etc…
However, it does not mean that telcos cannot (and should not) learn from some of the most successful platform business around. There certainly is enough classical beliefs in the industry that may be ripe for a bit of disruption … so untelconizing (or as my T-Mobile US friends like to call it uncarrier) ourselves may not be such a bad idea.
Telco-land
“There is more to telco technologies than its core network and backend platforms.”
Having a great (=successful) e-commerce business platform with cloud-native maturity level including automation that most telcos can only dream of, and mouth watering real-time big data platforms with the smartest data scientist and data engineers in the world … does not make for an easy straightforward transformation to a national (or world for that matter) leading (or non-leading) telco business in the classical sense of owning the value chain end-to-end.
Japan’s Rakuten is one platform business that has the ambition and expressed intention to move from being traditional platform-based business (ala Amazon.com) to become a mobile operator leveraging all the benefits and know-how of their existing platform technologies. Extending those principles, such as softwarization, cloudification and cloud-native automation principles, all the way out to the edge of the mobile antenna.
Many of us in telco-land thought that starting out with a classical telco, with mobile and maybe fixed assets as well, would make for an easy inclusion of platform-like technologies (as describe above), have had to revise our thinking somewhat. Certainly time-lines have been revised a couple of times, as have the assumed pre-conditions or context for such a transformation. Even economical and operational benefits that seems compelling, at least from a Greenfield perspective, turns out to be a lot more muddy when considering the legacy spaghetti we have in telcos with years and years in bag. And for the ones who keep saying that 5G will change all that … no I really doubt that it will any time soon.
While above platform-like telco topology looks so much simpler than the incumbent one … we should not forget it is what lays underneath the surface that matters. And what matters is software. Lots of software. The danger will always be present that we are ending up replacing hardware & legacy spaghetti complexity with software spaghetti complexity. Resulting unintended consequences in terms of longer-term operational stability (e.g,, when you go beyond being a greenfield business).
“Software have made a lot in the physical world redundant but it may also have leapfrogged the underlying operational complexity to an extend that may pose an existential threat down the line.”
While many platform businesses have perfected cloud-native e-commerce stacks reaching all the way out to the end-consumers mobile apps, residing on the smartphone’s OS, they do operate on the higher level of whatever relevant telco protocol stack. Platform businesses today relies on classical telcos to provide a robust connection data pipe to their end-users at high availability and stability.
What’s coming for us in Telco-land?
“Software will eat more and more of telco-land’s hardware as well as the world.”
(side note: for the ones who want to say that artificial intelligence (AI) will be eating the software, do remember that AI is software too and imo we talk then about autosarcophagy … no further comment;-).
Telcos, of the kinds with a past, will increasingly implement software solutions replacing legacy hardware functionality. Such software will be residing in a cloud environment either in form of public and/or private cloud models. We will be replacing legacy hardware-centric telco components or boxes with a software copy, residing on a boring but highly standardized hardware platform (i.e., a common off the shelf server). Yes … I talk about software definable networks (SDN) and network functional virtualization (NFV) features and functionalities (though I suspect SDN/NFV will be renamed to something else as we have talked about this for too many years for it to keep being exciting;-). The ultimate dream (or nightmare pending on taste) is to have all telco functions defined in software and operating on a very low number of standardized servers (let’s call it the pizza-box model). This is very close to the innovative and quiet frankly disruptive ideas of for example Drivenets in Israel (definitely worth a study if you haven’t already peeked at some of their solutions). We are of course seeing quiet some progress in developing software equivalents to telco core (i.e., Telco Cloud in above picture) functionalities, e.g., evolved packet core (EPC) functions, policy and charging rules function (PCRF), …. These solutions are available from the usual supplier suspects (e.g., Cisco, Ericsson, Huawei, and Nokia) as well as from (relative) new bets, such as for example Affirmed Networks and Mavenir (side note: if you are not the usual supplier suspect and have developed cloud-based telco functionalities drop me a note … particular if such work in a public or hybrid cloud model with for example Azure or AWS).
We will have software eating its way out to the edge of our telco networks. That is assuming it proves to make economical and operational sense (and maybe even anyway;-). As computing requirements, driven by softwarization of telco-land, goes “through the roof” across all network layers, edge computing centers will be deployed (or classical 2G BSC or 3G RNC sites will be re-purposed for the “lucky” operators with a more dis-aggregated network typologies).
Telcos (should) have very strong desires for platform-like automation as we know it from platform businesses cloud-native implementations. For a telco though, the question is whether they can achieve cloud-native automation principles throughout all their network layers and thus possibly allow for end-2-end (E2E) automation principles as known in a cloud-native world (which scope wise is more limited than the full telco stack). This assumes that an E2E automation goal makes economical and operational sense compared to domain-oriented automation (with domains not per see matching one to one the traditional telco network layers). While it is tempting to get all enthusiastic & winded-up about the role of artificial intelligence (AI) in telco (or any other) automation framework, it always make sense to take an ice cold shower and read up on non-AI based automation schemes as we have them in a cloud-native cloud environment before jumping into the rabbit hole. I also think that we should be very careful architecturally to spread intelligent agents all over our telco architecture and telco stack. AI will have an important mission in pro-active customer experience solutions and anomaly detection. The devil may be in how we close the loop of an intelligent agent’s output and a input to our automation framework.
To summarize what’s coming for the Telco sector;
Increased softwarization (or virtualization) moving from traditional platform layers out towards the edge.
Increased leveraging of cloud models (e.g., private, public, hybrid) following the path of softwarization.
Strive towards cloud-native operations including the obvious benefits from (non-AI based) automation that the cloud-native framework brings.
We will see a lot of focus on developing automation principles across the telco stack to the extend such will be different from cloud-native principles (note: expect there will be some at least for non-Greenfield implementations but also in general as the telco stack is not idem ditto a traditional platform stack). This may be hampered by lack of architectural standardization alignment across our industry. There is a risk that we will push for AI-based automation without exploring fully what non-AI based schemes may bring.
Inevitable the industry will spend much more efforts on developing cognitive-based pro-active customer experience solutions as well as expanding anomaly detection across the full telco stack. This will help in dealing with design complexities although might also be hampered by mis-alignment on standardization. Not to mention that AI should never become an excuse to not simplify designs and architectures.
Plus anything clever that I have not thought about or forgot to mention 🙂
So yes … softwarization, cloudification and aggressive (non-AI based) automation, known from platform-centric businesses, will be coming (in fact has arrived to an extend) for Telcos … over time and earlier for the few new brave Telco Greenfields …
Artificial intelligence based solutions will have a mission in pro-active customer experience (e.g., cellwize, uhana, …), zero-touch predictive maintenance, self-restoration & healing, and for advanced anomaly detection solutions (e.g., see Anodot as a leading example here). All are critical requirements in the new (and obviously in the old as well) telco world is being eaten by software. Self-learning “conscious” (defined in a relative narrow technical sense) anomaly detection solutions across the telco stack is in my opinion a must to deal with today’s and the future’s highly complex software architectures and systems.
I am also speculating whether intelligent agents (e.g., microagents reacting to an events) may make the telco layers less reliant on top-down control and orchestration (… I am also getting goosebumps by that idea … so maybe this is not good … hmmm … or I am cold … but then again orchestration is for non-trusting control “freaks”). Such a reactive microagent (or microservice) could take away the typical challenges with stack orchestration (e.g., blocking, waiting, …), decentralize control across the telco stack.
And no … we will not become Ubers … although there might be Ubers that will try to become us … The future will show …
Acknowledgement.
I also greatly acknowledge my wife Eva Varadi for her support, patience and understanding during the creative process of writing this Blog. Also many of my Deutsche Telekom AG, T-Mobile NL & Industry colleagues in general have in countless of ways contributed to my thinking and ideas leading to this little Blog. Thank you!
Further reading
Mike Isaac, “Super Pumped – The Battle for Uber”, 2019, W.W. Norton & Company. A good read and what starts to look like a rule of a Silicon Valley startup behavior (the very worst and of course some of the best). Irrespective of the impression this book leaves with me, I am also deeply impressed (maybe even more after reading the book;-) what Uber’s engineers have been pulling off over the last couple of years.
Chris Anderson, “Free – The Future of a Radical Price”, (2009), Hyperion eBook. This is one of the coolest books I have read on the topic of freemium, sharing economy and platform-based business models. A real revelations and indeed a confirmation that if you get something for free, you are likely not a customer but a product. A must read to understand the work around us. In this setting it is also worth reading “What is a Free Customer Worth?” by Sunil Gupta & Carl F. Mela (HBR, 2008).
Sangeet Paul Choudary, “Platform Scale”, (2015), Platform Thinking Labs Pte. Ltd. A must read for anyone thinking of developing a platform based business. Contains very good detailed end-2-end platform design recommendations. If you are interested in knowing the most important aspects of Platform business models and don’t have time for more academic deep dive, this is most likely the best book to read.
Laure Claire Reillier & Benout Reillier, “Platform Strategy”, (2017), Routledge Taylor & Francis Group. Very systematic treatment of platform economics and all strategic aspects of a platform business. It contains a fairly comprehensive overview of academic works related to platform business models and economics (that is if you want to go deeper than for example Choudary’s excellent “Platform Scale” above).
Jean-Charles Rochet & Jean Tirole, “Platform Competition in Two-sided Markets” (2003), Journal of the European Economic Association, 1, 990. Rochet & Tirole formalizes the economics of two-sided markets. The math is fairly benign but requires a mathematical background. Beside the math their paper contains some good descriptions of platform economics.
Todd W. Schneider, “Taxi and Ridehailing Usage in New York City”, a cool site that provides historical and up-to-date taxi and ride hailing usage data for New York and Chicago. This gives very interesting insights into the competitive dynamics of Uber / Ride hailing platform businesses vs the classical taxi business. It also shows that while ride hailing businesses have disrupted the taxi business in totality, being a driver for a ride hailing platform is not that great either (and as Uber continues to operate at impressive losses maybe also not for Uber either at least in their current structure).
Uber Engineeringis in general a great resource for platform / stack architecture, system design, machine learning, big data & forecasting solutions for a business model relying on real-time transactions. While I personally find the Uber architecture or system design too complex it is nevertheless an impressive solution that Uber has developed. There are many noteworthy blog posts to be found on the Uber Engineering site. Here is a couple of foundational ones (both from 2016 so please be aware that lots may have changed since then) “The Uber Engineering Tech Stack, Part I: The Foundation” (Lucie Lozinski, 2016) and “The Uber Engineering Tech Stack, Part II: The Edge and Beyond” (Lucie Lozinski, 2016) . I also found “Uber’s Big Data Platform: 100+ Petabytes with Minute Latency” post (by Reza Shiftehfar, 2018) very interesting in describing the historical development and considerations Uber went through in their big data platform as their business grew and scale became a challenge in their designs. This is really a learning resource.
Wireless One, “Rakuten: Japan’s new #4 is going all cloud”, 2019. Having had the privilege to visit Rakuten in Japan and listen to their chief-visionary Tareq Amin (CTO) they clearly start from being a platform-centric business (i.e., Asia’s Amazon.com) with the ambition to become a new breed of telco levering their platform technologies (and platform business model thinking) all the way out to the edge of the mobile base station antenna. While I love that Tareq Amin actually has gone and taken his vision from powerpoint to reality, I also think that Rakuten benefits (particular many of the advertised economical benefits) from being more a Greenfield telco than an established telco with a long history and legacy. In this respect it is humbling that their biggest stumbling block or challenge for launching their services is site rollout (yes touchy-feel infrastructure & real estate is a b*tch!). See also “Rakuten taking limited orders for services on its delayed Japan mobile network” (October, 2019).
Justin Garrison & Chris Nova, “Cloud Native Infrastructure”, 2018, O’Reilly and Kief Morris, “Infrastructure as Code”, 2016, O’Reilly. I am usually using both these books as my reference books when it comes to cloud native topics and refreshing my knowledge (and hopefully a bit of understanding).
Murat Uenlue, “The Complete Guide to the Revolutionary Platform Business Model”, 2017. Good read. Provides a great overview of platform business models and attempts systematically categorize platform businesses (e.g., Communications Platform, Social Platform, Search Platform, Open OS Platforms, Service Platforms, Asset Sharing Platforms, Payment Platforms, etc….).
100% 5G coverage is not going to happen with 30 – 300 GHz millimeter-wave frequencies alone.
The “NGMN 5G white paper” , which I will in the subsequent parts refer to as the 5G vision paper, require the 5G coverage to be 100%.
At 100% cellular coverage it becomes somewhat academic whether we talk about population coverage or geographical (area) coverage. The best way to make sure you cover 100% of population is covering 100% of the geography. Of course if you cover 100% of the geography, you are “reasonably” ensured to cover 100% of the population.
While it is theoretically possible to cover 100% (or very near to) of population without covering 100% of the geography, it might be instructive to think why 100% geographical coverage could be a useful target in 5G;
Network-augmented driving and support for varous degrees of autonomous driving would require all roads to be covered (however small).
Internet of Things (IoT) Sensors and Actuators are likely going to be of use also in rural areas (e.g., agriculture, forestation, security, waterways, railways, traffic lights, speed-detectors, villages..) and would require a network to connect to.
Given many users personal area IoT networks (e.g., fitness & health monitors, location detection, smart-devices in general) ubiquitous becomes essential.
Internet of flying things (e.g., drones) are also likely to benefit from 100% area and aerial coverage.
However, many countries remain lacking in comprehensive geographical coverage. Here is an overview of the situation in EU28 (as of 2015);
For EU28 countries, 14% of all house holds in 2015 still had no LTE coverage. This was approx.30+ million households or equivalent to 70+ million citizens without LTE coverage. The 14% might seem benign. However, it covers a Rural neglect of 64% of households not having LTE coverage. One of the core reasons for the lack of rural (population and household) coverage is mainly an economic one. Due to the relative low number of population covered per rural site and compounded by affordability issues for the rural population, overall rural sites tend to have low or no profitability. Network sharing can however improve the rural site profitability as site-related costs are shared.
From an area coverage perspective, the 64% of rural households in EU28 not having LTE coverage is likely to amount to a sizable lack of LTE coverage area. This rural proportion of areas and households are also very likely by far the least profitable to cover for any operator possibly even with very progressive network sharing arrangements.
Fixed broadband, Fiber to the Premises (FTTP) and DOCSIS3.0, lacks further behind that of mobile LTE-based broadband. Maybe not surprisingly from an business economic perspective, in rural areas fixed broadband is largely unavailable across EU28.
The chart below illustrates the variation in lack of broadband coverage across LTE, Fiber to the Premises (FTTP) and DOCSIS3.0 (i.e., Cable) from a total country perspective (i.e., rural areas included in average).
We observe that most countries have very far to go on fixed broadband provisioning (i.e., FTTP and DOCSIS3.0) and even on LTE coverage lacks complete coverage. The rural coverage view (not shown here) would be substantially worse than the above Total view.
The 5G ambition is to cover 100% of all population and households. Due to the demographics of how rural households (and populations) are spread, it is also likely that fairly large geographical areas would need to be covered in order to come true on the 100% ambition.
It would appear that bridging this lack of broadband coverage would be best served by a cellular-based technology. Given the fairly low population density in such areas relative higher average service quality (i.e., broadband) could be delivered as long as the cell range is optimized and sufficient spectrum at a relative low carrier frequency (< 1 GHz) would be available. It should be remembered that the super-high 5G 1 – 10 Gbps performance cannot be expected in rural areas. Due to the lower carrier frequency range need to provide economic rural coverage both advanced antenna systems and very large bandwidth (e.g., such as found in the mm-frequency range) would not be available to those areas. Thus limiting the capacity and peak performance possible even with 5G.
I would suspect that irrespective of the 100% ambition, telecom providers would be challenged by the economics of cellular deployment and traffic distribution. Rural areas really sucks in profitability, even in fairly aggressive sharing scenarios. Although multi-party (more than 2) sharing might be a way to minimize the profitability burden on deep rural coverage.
The above chart shows the relationship between traffic distribution and sites. As a rule of thumb 50% of revenue is typically generated by 10% of all sites (i.e., in a normal legacy mobile network) and approx. 50% of (rural) sites share roughly 10% of the revenue. Note: in emerging markets the distribution is somewhat steeper as less comprehensive rural coverage typically exist. (Source:The ABC of Network Sharing – The Fundamentals.).
Irrespective of my relative pessimism of the wider coverage utility and economics of millimeter-wave (mm-wave) based coverage, there shall be no doubt that mm-wave coverage will be essential for smaller and smallest cell coverage where due to density of users or applications will require extreme (in comparison to today’s demand) data speeds and capacities. Millimeter-wave coverage-based architectures offer very attractive / advanced antenna solutions that further will allow for increased spectral efficiency and throughput. Also the possibility of using mm-wave point to multipoint connectivity as last mile replacement for fiber appears very attractive in rural and sub-urban clutters (and possible beyond if the cost of the electronics drop according the expeced huge increase in demand for such). This last point however is in my opinion independent of 5G as Facebook with their Terragraph development have shown (i.e., 60 GHz WiGig-based system). A great account for mm-wave wireless communications systems can be found in T.S. Rappaport et al.’s book “Millimeter Wave Wireless Communications” which not only comprises the benefits of mm-wave systems but also provides an account for the challenges. It should be noted that this topic is still a very active (and interesting) research area that is relative far away from having reached maturity.
In order to provide 100% 5G coverage for the mass market of people & things, we need to engage the traditional cellular frequency bands from 600 MHz to 3 GHz.
1 – 10 Gbps PEAK DATA RATE PER USER.
Getting a Giga bit per second speed is going to require a lot of frequency bandwidth, highly advanced antenna systems and lots of additional cells. And that is likely going to lead to a (very) costly 5G deployment. Irrespective of the anticipated reduced unit cost or relative cost per Byte or bit-per-second.
At 1 Gbps it would take approx. 16 seconds to download a 2 GB SD movie. It would take less than a minute for the HD version (i.e., at 10 Gbps it just gets better;-). Say you have a 16GB smartphone, you loose maybe up to 20+% for the OS, leaving around 13GB for things to download. With 1Gbps it would take less than 2 minutes to fill up your smartphones storage (assuming you haven’t run out of credit on your data plan or reached your data ceiling before then … of course unless you happen to be a customer of T-Mobile US in which case you can binge on = you have no problems!).
The biggest share of broadband usage comes from video streaming which takes up 60% to 80% of all volumetric traffic pending country (i.e., LTE terminal penetration dependent). Providing higher speed to your customer than is required by the applied video streaming technology and smartphone or tablet display being used, seems somewhat futile to aim for. The Table below provides an overview of streaming standards, their optimal speeds and typical viewing distance for optimal experience;
So … 1Gbps could be cool … if we deliver 32K video to our customers end device, i.e., 750 – 1600 Mbps optimal data rate. Though it is hard to see customers benefiting from this performance boost given current smartphone or tablet display sizes. The screen size really have to be ridiculously large to truly benefit from this kind of resolution. Of course Star Trek-like full emersion (i.e., holodeck) scenarios would arguably require a lot (=understatement) bandwidth and even more (=beyond understatement) computing power … though such would scenario appears unlikely to be coming out of cellular devices (even in Star Trek).
1 Gbps fixed broadband plans have started to sell across Europe. Typically on Fiber networks although also on DOCSIS3.1 (10Gbps DS/1 Gbps US) networks as well in a few places. It will only be a matter of time before we see 10 Gbps fixed broadband plans being offered to consumers. Irrespective of compelling use cases might be lacking it might at least give you the bragging rights of having the biggest.
From European Commissions “Europe’s Digital Progress Report 2016”, 22 % of European homes subscribe to fast broadband access of at least 30 Mbps. An estimated 8% of European households subscribe to broadband plans of at least 100 Mbps. It is worth noticing that this is not a problem with coverage as according with the EC’s “Digital Progress Report” around 70% of all homes are covered with at least 30 Mbps and ca. 50% are covered with speeds exceeding 100 Mbps.
The chart below illustrates the broadband speed coverage in EU28;
Even if 1Gbps fixed broadband plans are being offered, still majority of European homes are at speeds below the 100 Mbps. Possible suggesting that affordability and household economics plays a role as well as the basic perceived need for speed might not (yet?) be much beyond 30 Mbps?
Most aggregation and core transport networks are designed, planned, built and operated on a assumption of dominantly customer demand of lower than 100 Mbps packages. As 1Gbps and 10 Gbps gets commercial traction, substantial upgrades are require in aggregation, core transport and last but not least possible also on an access level (to design shorter paths). It is highly likely distances between access, aggregation and core transport elements are too long to support these much higher data rates leading to very substantial redesigns and physical work to support this push to substantial higher throughputs.
Most telecommunications companies will require very substantial investments in their existing transport networks all the way from access to aggregation through the optical core switching networks, out into the world wide web of internet to support 1Gbps to 10 Gbps. Optical switching cards needs to be substantially upgraded, legacy IP/MPLS architectures might no longer work very well (i.e., scale & complexity issue).
Most analysts today believe that incumbent fixed & mobile broadband telecommunications companies with a reasonable modernized transport network are best positioned for 5G compared to mobile-only operators or fixed-mobile incumbents with an aging transport infrastructure.
What about the state of LTE speeds across Europe? OpenSignal recurrently reports on the State of LTE, the following summarizes LTE speeds in Mbps as of June 2017 for EU28 (with the exception of a few countries not included in the OpenSignal dataset);
The OpenSignal measurements are based on more than half a million devices, almost 20 billion measurements over the period of the 3 first month of 2017.
The 5G speed ambition is by todays standards 10 to 30+ times away from present 2016/2017 household fixed broadband demand or the reality of provided LTE speeds.
Let us look at cellular spectral efficiency to be expected from 5G. Using the well known framework;
In essence, I can provide very high data rates in bits per second by providing a lot of frequency bandwidth B, use the most spectrally efficient technologies maximizing η, and/or add as many cells N that my economics allow for.
The average spectral efficiency is expected to be coming out in the order of 10 Mbps/MHz/cell using advanced receiver architectures, multi-antenna, multi-cell transmission and corporation. So pretty much all the high tech goodies we have in the tool box is being put to use of squeezing out as many bits per spectral Hz available and in a sustainable matter. Under very ideal Signal to Noise Ratio conditions, massive antenna arrays of up to 64 antenna elements (i.e., an optimum) seems to indicate that 50+ Mbps/MHz/Cell might be feasible in peak.
So for a spectral efficiency of 10 Mbps/MHz/cell and a demanded 1 Gbps data rate we would need 100 MHz frequency bandwidth per cell (i.e., using the above formula). Under very ideal conditions and relative large antenna arrays this might lead to a spectral requirement of only 20 MHz at 50 Mbps/MHz/Cell. Obviously, for 10 Gbps data rate we would require 1,000 MHz frequency bandwidth (1 GHz!) per cell at an average spectral efficiency of 10 Mbps/MHz/cell.
The spectral efficiency assumed for 5G heavily depends on successful deployment of many-antenna segment arrays (e.g., Massive MiMo, beam-forming antennas, …). Such fairly complex antenna deployment scenarios work best at higher frequencies, typically above 2GHz. Also such antenna systems works better at TDD than FDD with some margin on spectral efficiency. These advanced antenna solutions works perfectly in the millimeter wave range (i.e., ca. 30 – 300 GHz) where the antenna segments are much smaller and antennas can be made fairly (very) compact (note: resonance frequency of the antenna proportional to half the wavelength with is inverse proportional to the carrier frequency and thus higher frequencies need smaller material dimension to operate).
Below 2 GHz higher-order MiMo becomes increasingly impractical and the spectral efficiency regress to the limitation of a simple single-path antenna. Substantially lower than what can be achieved at much high frequencies with for example massive-MiMo.
So for the 1Gbps to 10 Gbps data rates to work out we have the following relative simple rationale;
High data rates require a lot of frequency bandwidth (>100 MHz to several GHz per channel).
Lots of frequency bandwidth are increasingly easier to find at high and very high carrier frequencies (i.e., why millimeter wave frequency band between 30 – 300 GHz is so appealing).
High and very high carrier frequencies results in small, smaller and smallest cells with very high bits per second per unit area (i.e., the area is very small!).
High and very high carrier frequency allows me to get the most out of higher order MiMo antennas (i.e., with lots of antenna elements),
Due to fairly limited cell range, I boost my overall capacity by adding many smallest cells (i.e., at the highest frequencies).
We need to watch out for the small cell densification which tends not to scale very well economically. The scaling becomes a particular problem when we need hundreds of thousands of such small cells as it is expected in most 5G deployment scenarios (i.e., particular driven by the x1000 traffic increase). The advanced antenna systems required (including the computation resources needed) to max out on spectral efficiency are likely going to be one of the major causes of breaking the economical scaling. Although there are many other CapEx and OpEx scaling factors to be concerned about for small cell deployment at scale.
Further, for mass market 5G coverage, as opposed to hot traffic zones or indoor solutions, lower carrier frequencies are needed. These will tend to be in the usual cellular range we know from our legacy cellular communications systems today (e.g., 600 MHz – 2.1 GHz). It should not be expected that 5G spectral efficiency will gain much above what is already possible with LTE and LTE-advanced at this legacy cellular frequency range. Sheer bandwidth accumulation (multi-frequency carrier aggregation) and increased site density is for the lower frequency range a more likely 5G path. Of course mass market 5G customers will benefit from faster reaction times (i.e., lower latencies), higher availability, more advanced & higher performing services arising from the very substantial changes expected in transport networks and data centers with the introduction of 5G.
Last but not least to this story … 80% and above of all mobile broadband customers usage, data as well as voice, happens in very few cells (e.g., 3!) … representing their Home and Work.
As most of the mobile cellular traffic happen at the home and at work (i.e., thus in most cases indoor) there are many ways to support such traffic without being concerned about the limitation of cell ranges.
The giga bit per second cellular service is NOT a service for the mass market, at least not in its macro-cellular form.
≤ 1 ms IN ROUND-TRIP DELAY.
A total round-trip delay of 1 or less millisecond is very much attuned to niche service. But a niche service that nevertheless could be very costly for all to implement.
I am not going to address this topic too much here. It has to a great extend been addressed almost to ad nauseam in 5G Economics – An Introduction (Chapter 1) and 5G Economics – The Tactile Internet (Chapter 2). I think this particular aspect of 5G is being over-hyped in comparison to how important it ultimately will turn out to be from a return on investment perspective.
Speed of light travels ca. 300 km per millisecond (ms) in vacuum and approx. 210 km per ms in fiber (some material dependency here). Lately engineers have gotten really excited about the speed of light not being fast enough and have made a lot of heavy thinking abou edge this and that (e.g., computing, cloud, cloudlets, CDNs,, etc…). This said it is certainly true that most modern data centers have not been build taking too much into account that speed of light might become insufficient. And should there really be a great business case of sub-millisecond total (i.e., including the application layer) roundtrip time scales edge computing resources would be required a lot closer to customers than what is the case today.
It is common to use delay, round-trip time or round-trip delay, or latency as meaning the same thing. Though it is always cool to make sure people really talk about the same thing by confirming that it is indeed a round-trip rather than single path. Also to be clear it is worthwhile to check that all people around the table talk about delay at the same place in the OSI stack or network path or whatever reference point agreed to be used.
In the context of the 5G vision paper it is emphasized that specified round-trip time is based on the application layer (i.e., OSI model) as reference point. It is certainly the most meaningful measure of user experience. This is defined as the End-2-End (E2E) Latency metric and measure the complete delay traversing the OSI stack from physical layer all the way up through network layer to the top application layer, down again, between source and destination including acknowledgement of a successful data packet delivery.
The 5G system shall provide 10 ms E2E latency in general and 1 ms E2E latency for use cases requiring extremely low latency.
The 5G vision paper states “Note these latency targets assume the application layer processing time is negligible to the delay introduced by transport and switching.” (Section 4.1.3 page 26 in “NGMN 5G White paper”).
In my opinion it is a very substantial mouthful to assume that the Application Layer (actually what is above the Network Layer) will not contribute significantly to the overall latency. Certainly for many applications residing outside the operators network borders, in the world wide web, we can expect a very substantial delay (i.e., even in comparison with 10 ms). Again this aspect was also addressed in my two first chapters.
Very substantial investments are likely needed to meet E2E delays envisioned in 5G. In fact the cost of improving latencies gets prohibitively more expensive as the target is lowered. The overall cost of design for 10 ms would be a lot less costly than designing for 1 ms or lower. The network design challenge if 1 millisecond or below is required, is that it might not matter that this is only a “service” needed in very special situations, overall the network would have to be designed for the strictest denominator.
Moreover, if remedies needs to be found to mitigate likely delays above the Network Layer, distance and insufficient speed of light might be the least of worries to get this ambition nailed (even at the 10 ms target). Of course if all applications are moved inside operator’s networked premises with simpler transport paths (and yes shorter effective distances) and distributed across a hierarchical cloud (edge, frontend, backend, etc..), the assumption of negligible delay in layers above the Network Layer might become much more likely. However, it does sound a lot like America Online walled garden fast forward to the past kind of paradigm.
So with 1 ms E2E delay … yeah yeah … “play it again Sam” … relevant applications clearly need to be inside network boundary and being optimized for processing speed or silly & simple (i.e., negligible delay above the Network Layer), no queuing delay (to the extend of being in-efficiency?), near-instantaneous transmission (i.e., negligible transmission delay) and distances likely below tenth of km (i.e., very short propagation delay).
When the speed of light is too slow there are few economic options to solve that challenge.
≥ 10,000 Gbps / Km2 DATA DENSITY.
The data density is maybe not the most sensible measure around. If taken too serious could lead to hyper-ultra dense smallest network deployments.
This has always been a fun one in my opinion. It can be a meaningful design metric or completely meaningless.
There is of course nothing particular challenging in getting a very high throughput density if an area is small enough. If I have a cellular range of few tens of meters, say 20 meters, then my cell area is smaller than 1/1000 of a km2. If I have 620 MHz bandwidth aggregated between 28 GHz and 39 GHz (i.e., both in the millimeter wave band) with a 10 Mbps/MHz/Cell, I could support 6,200 Gbps/km2. That’s almost 3 Petabyte in an hour or 10 years of 24/7 binge watching of HD videos. Note given my spectral efficiency is based on an average value, it is likely that I could achieve substantially more bandwidth density and in peaks closer to the 10,000 Gbps/km2 … easily.
Pretty Awesome Wow!
The basic; a Terabit equals 1024 Gigabits (but I tend to ignore that last 24 … sorry I am not).
With a traffic density of ca. 10,000 Gbps per km2, one would expect to have between 1,000 (@ 10 Gbps peak) to 10,000 (@ 1 Gbps peak) concurrent users per square km.
At 10 Mbps/MHz/Cell one would expect to have a 1,000 Cell-GHz/km2. Assume that we would have 1 GHz bandwidth (i.e., somewhere in the 30 – 300 GHz mm-wave range), one would need 1,000 cells per km2. On average with a cell range of about 20 meters (smaller to smallest … I guess what Nokia would call an Hyper-Ultra-Dense Network;-). Thus each cell would minimum have between 1 to 10 concurrent users.
Just as a reminder! 1 minutes at 1 Gbps corresponds to 7.5 GB. A bit more than what you need for a 80 minute HD (i.e., 720pp) full movie stream … in 1 minutes. So with your (almost) personal smallest cell what about the remaining 59 minutes? Seems somewhat wasteful at least until kingdom come (alas maybe sooner than that).
It would appear that the very high 5G data density target could result in very in-efficient networks from a utilization perspective.
≥ 1 MN / Km2 DEVICE DENSITY.
One million 5G devices per square kilometer appears to be far far out in a future where one would expect us to be talking about 7G or even higher Gs.
1 Million devices seems like a lot and certainly per km2. It is 1 device per square meter on average. A 20 meter cell-range smallest cell would contain ca. 1,200 devices.
To give this number perspective lets compare it with one of my favorite South-East Asian cities. The city with one of the highest population densities around, Manila (Philippines). Manila has more than 40 thousand people per square km. Thus in Manila this would mean that we would have about 24 devices per person or 100+ per household per km2. Overall, in Manila we would then expect approx. 40 million devices spread across the city (i.e., Manila has ca. 1.8 Million inhabitants over an area of 43 km2. Philippines has a population of approx. 100 Million).
Just for the curious, it is possible to find other more populated areas in the world. However, these highly dense areas tends to be over relative smaller surface areas, often much smaller than a square kilometer and with relative few people. For example Fadiouth Island in Dakar have a surface area of 0.15 km2 and 9,000 inhabitants making it one of the most pop densest areas in the world (i.e., 60,000 pop per km2).
I hope I made my case! A million devices per km2 is a big number.
Let us look at it from a forecasting perspective. Just to see whether we are possibly getting close to this 5G ambition number.
IHS forecasts 30.5 Billion installed devices by 2020, IDC is also believes it to be around 30 Billion by 2020. Machina Research is less bullish and projects 27 Billion by 2025 (IHS expects that number to be 75.4 Billion) but this forecast is from 2013. Irrespective, we are obviously in the league of very big numbers. By the way 5G IoT if at all considered is only a tiny fraction of the overall projected IoT numbers (e.g., Machine Research expects 10 Million 5G IoT connections by 2024 …that is extremely small numbers in comparison to the overall IoT projections).
To break this number down to something that could be more meaningful than just being Big and impressive, let just establish a couple of worldish numbers that can help us with this;
2020 population expected to be around 7.8 Billion compared to 2016 7.4 Billion.
Global pop per HH is ~3.5 (average number!) which might be marginally lower in 2020. Urban populations tend to have less pop per households ca. 3.0. Urban populations in so-called developed countries are having a pop per HH of ca. 2.4.
ca. 55% of world population lives in Urban areas. This will be higher by 2020.
Less than 20% of world population lives in developed countries (based on HDI). This is a 2016 estimate and will be higher by 2020.
World surface area is 510 Million km2 (including water).
of which ca. 150 million km2 is land area
of which ca. 75 million km2 is habitable.
of which 3% is an upper limit estimate of earth surface area covered by urban development, i.e., 15.3 Million km2.
of which approx. 1.7 Million km2 comprises developed regions urban areas.
ca. 37% of all land-based area is agricultural land.
Using 30 Billion IoT devices by 2020 is equivalent to;
ca. 4 IoT per world population.
ca. 14 IoT per world households.
ca. 200 IoT per km2 of all land-based surface area.
ca. 2,000 IoT per km2 of all urban developed surface area.
If we limit IoT’s in 2020 to developed countries, which wrongly or rightly exclude China, India and larger parts of Latin America, we get the following by 2020;
ca. 20 IoT per developed country population.
ca. 50 IoT per developed country households.
ca. 18,000 IoT per km2 developed country urbanized areas.
Given that it would make sense to include larger areas and population of both China, India and Latin America, the above developed country numbers are bound to be (a lot) lower per Pop, HH and km2. If we include agricultural land the number of IoTs will go down per km2.
So far far away from a Million IoT per km2.
What about parking spaces, for sure IoT will add up when we consider parking spaces!? … Right? Well in Europe you will find that most big cities will have between 50 to 200 (public) parking spaces per square kilometer (e.g., ca. 67 per km2 for Berlin and 160 per km2 in Greater Copenhagen). Aha not really making up to the Million IoT per km2 … what about cars?
In EU28 there are approx. 256 Million passenger cars (2015 data) over a population of ca. 510 Million pops (or ca. 213 million households). So a bit more than 1 passenger car per household on EU28 average. In Eu28 approx. 75+% lives in urban area which comprises ca. 150 thousand square kilometers (i.e., 3.8% of EU28’s 4 Million km2). So one would expect little more (if not a little less) than 1,300 passenger cars per km2. You may say … aha but it is not fair … you don’t include motor vehicles that are used for work … well that is an exercise for you (too convince yourself why that doesn’t really matter too much and with my royal rounding up numbers maybe is already accounted for). Also consider that many EU28 major cities with good public transportation are having significantly less cars per household or population than the average would allude to.
Surely, public street light will make it through? Nope! Typical bigger modern developed country city will have on average approx. 85 street lights per km2, although it varies from 0 to 1,000+. Light bulbs per residential household (from a 2012 study of the US) ranges from 50 to 80+. In developed countries we have roughly 1,000 households per km2 and thus we would expect between 50 thousand to 80+ thousand lightbulbs per km2. Shops and business would add some additions to this number.
With a cumulated annual growth rate of ca. 22% it would take 20 years (from 2020) to reach a Million IoT devices per km2 if we will have 20 thousand per km2 by 2020. With a 30% CAGR it would still take 15 years (from 2020) to reach a Million IoT per km2.
The current IoT projections of 30 Billion IoT devices in operation by 2020 does not appear to be unrealistic when broken down on a household or population level in developed areas (even less ambitious on a worldwide level). The 18,000 IoT per km2 of developed urban surface area by 2020 does appear somewhat ambitious. However, if we would include agricultural land the number would become possible a more reasonable.
If you include street crossings, traffic radars, city-based video monitoring (e.g., London has approx. 300 per km2, Hong Kong ca. 200 per km2), city-based traffic sensors, environmental sensors, etc.. you are going to get to sizable numbers.
Maybe the 1 Million Devices per km2 ambition is not one of the most important 5G design criteria’s for the short term (i.e., next 10 – 20 years).
Oh and most IoT forecasts from the period 2015 – 2016 does not really include 5G IoT devices in particular. The chart below illustrates Machina Research IoT forecast for 2024 (from August 2015). In a more recent forecast from 2016, Machine Research predict that by 2024 there would be ca. 10 million 5G IoT connections or 0.04% of the total number of forecasted connections;
The winner is … IoTs using WiFi or other short range communications protocols. Obviously, the cynic in me (mea culpa) would say that a mm-wave based 5G connections can also be characterized as short range … so there might be a very interesting replacement market there for 5G IoT … maybe? 😉
Expectations to 5G-based IoT does not appear to be very impressive at least over the next 10 years and possible beyond.
The un-importance of 5G IoT should not be a great surprise given most 5G deployment scenarios are focused on millimeter-wave smallest 5G cell coverage which is not good for comprehensive coverage of IoT devices not being limited to those very special 5G coverage situations being thought about today.
Only operators focusing on comprehensive 5G coverage re-purposing lower carrier frequency bands (i.e., 1 GHz and lower) can possible expect to gain a reasonable (as opposed to niche) 5G IoT business. T-Mobile US with their 600 MHz 5G strategy might very well be uniquely positions for taking a large share of future proof IoT business across USA. Though they are also pretty uniquely position for NB-IoT with their comprehensive 700MHz LTE coverage.
For 5G IoT to be meaningful (at scale) the conventional macro-cellular networks needs to be in play for 5G coverage .,, certainly 100% 5G coverage will be a requirement. Although, even with 5G there maybe 100s of Billion of non-5G IoT devices that require coverage and management.
≤ 500 km/h SERVICE SUPPORT.
Sure why not? but why not faster than that? At hyperloop or commercial passenger airplane speeds for example?
Before we get all excited about Gbps speeds at 500 km/h, it should be clear that the 5G vision paper only proposed speeds between 10 Mbps up-to 50 Mbps (actually it is allowed to regress down to 50 kilo bits per second). With 200 Mbps for broadcast like services.
So in general, this is a pretty reasonable requirement. Maybe with the 200 Mbps for broadcasting services being somewhat head scratching unless the vehicle is one big 16K screen. Although the users proximity to such a screen does not guaranty an ideal 16K viewing experience to say the least.
What moves so fast?
The fastest train today is tracking at ca. 435 km/h (Shanghai Maglev, China).
Typical cruising airspeed for a long-distance commercial passenger aircraft is approx. 900 km/h. So we might not be able to provide the best 5G experience in commercial passenger aircrafts … unless we solve that with an in-plane communications system rather than trying to provide Gbps speed by external coverage means.
Why take a plane when you can jump on the local Hyperloop? The proposed Hyperloop should track at an average speed of around 970 km/h (faster or similar speeds as commercial passengers aircrafts), with a top speed of 1,200 km/h. So if you happen to be in between LA and San Francisco in 2020+ you might not be able to get the best 5G service possible … what a bummer! This is clearly an area where the vision did not look far enough.
Providing services to moving things at a relative fast speed does require a reasonable good coverage. Whether it being train track, hyperloop tunnel or ground to air coverage of commercial passenger aircraft, new coverage solutions would need to be deployed. Or alternative in-vehicular coverage solutions providing a perception of 5G experience might be an alternative that could turn out to be more economical.
The speed requirement is a very reasonable one particular for train coverage.
50% TOTAL NETWORK ENERGY REDUCTION.
If 5G development could come true on this ambition we talk about 10 Billion US Dollars (for the cellular industry). Equivalent to a percentage point on the margin.
There are two aspects of energy efficiency in a cellular based communication system.
User equipment that will benefit from longer intervals without charging and thus improve customers experience and overall save energy from less frequently charges.
Network infrastructure energy consumption savings will directly positively impact a telecom operators Ebitda.
Energy efficient Smartphones
The first aspect of user equipment is addressed by the 5G vision paper under “4.3 Device Requirements” sub-section “4.3.3 Device Power Efficiency”; “Battery life shall be significantly increased: at least 3 days for a smartphone, and up tp 15 years for a low-cost MTC device.”(note: MTC = Machine Type Communications).
Apple’s iPhone 7 battery life (on a full charge) is around 6 hours of constant use with 7 Plus beating that with ca. 3 hours (i.e., total 9 hours). So 3 days will go a long way.
It is however unclear whether the 3 extra days of a 5G smartphone battery life-time is supposed to be under active usage conditions or just in idle mode. Obviously in order to matter materially to the consumer one would expect this vision to apply to active usage (i.e., 4+ hours a day at 100s of Mbps – 1Gbps operations).
Energy efficient network infrastructure.
The 5G vision paper defines energy efficiency as number of bits that can be transmitted over the telecom infrastructure per Joule of Energy.
The total energy cost, i.e., operational expense (OpEx), of telecommunications network can be considerable. Despite our mobile access technologies having become more energy efficient with each generation, the total OpEx of energy attributed to the network infrastructure has increased over the last 10 years in general. The growth in telco infrastructure related energy consumption has been driven by the consumer demand for broadband services in mobile and fixed including incredible increase in data center computing and storage requirements.
In general power consumption OpEx share of total technology cost amounts to 8% to 15% (i.e., for Telcos without heavy reliance of diesel). The general assumption is that with regular modernization, energy efficiency gain in newer electronics can keep growth in energy consumption to a minimum compensating for increased broadband and computing demand.
Note: Technology Opex (including NT & IT) on average lays between 18% to 25% of total corporate Telco Opex. Out of the Technology Opex between 8% to 15% (max) can typically be attributed to telco infrastructure energy consumption. The access & aggregation contribution to the energy cost typically would towards 80% plus. Data centers are expected to increasingly contribute to the power consumption and cost as well. Deep diving into the access equipment power consumption, ca. 60% can be attributed to rectifiers and amplifiers, 15% by the DC power system & miscellaneous and another 25% by cooling.
5G vision paper is very bullish in their requirement to reduce the total energy and its associated cost; it is stated “5G should support a 1,000 times traffic increase in the next 10 years timeframe, with an energy consumption by the whole network of only half that typically consumed by today’s networks. This leads to the requirement of an energy efficiency of x2,000 in the next 10 years timeframe.” (sub-section “4.6.2 Energy Efficiency” NGMN 5G White Paper).
This requirement would mean that in a pure 5G world (i.e., all traffic on 5G), the power consumption arising from the cellular network would be 50% of what is consumed today. In 2016 terms the Mobile-based Opex saving would be in the order of 5 Billion US$ to 10+ Billion US$ annually. This would be equivalent to 0.5% to 1.1% margin improvement globally (note: using GSMA 2016 Revenue & Growth data and Pyramid Research forecast). If energy price would increase over the next 10 years the saving / benefits would of course be proportionally larger.
As we have seen in the above, it is reasonable to expect a very considerable increase in cell density as the broadband traffic demand increases from peak bandwidth (i.e., 1 – 10 Gbps) and traffic density (i.e., 1 Tbps per km2) expectations.
Depending on the demanded traffic density, spectrum and carrier frequency available for 5G between 100 to 1,000 small cell sites per km2 could be required over the next 10 years. This cell site increase will be required in addition to existing macro-cellular network infrastructure.
Today (in 2017) an operator in EU28-sized country may have between ca. 3,500 to 35,000 cell sites with approx. 50% covering rural areas. Many analysts are expecting that for medium sized countries (e.g., with 3,500 – 10,000 macro cellular sites), operators would eventually have up-to 100,000 small cells under management in addition to their existing macro-cellular sites. Most of those 5G small cells and many of the 5G macro-sites we will have over the next 10 years, are also going to have advanced massive MiMo antenna systems with many active antenna elements per installed base antenna requiring substantial computing to gain maximum performance.
It appears with today’s knowledge extremely challenging (to put it mildly) to envision a 5G network consuming 50% of today’s total energy consumption.
It is highly likely that the 5G radio node electronics in a small cell environment (and maybe also in a macro cellular environment?) will consume less Joules per delivery bit (per second) due to technology advances and less transmitted power required (i.e., its a small or smallest cell). However, this power efficiency technology and network cellular architecture gain can very easily be destroyed by the massive additional demand of small, smaller and smallest cells combined with highly sophisticated antenna systems consuming additional energy for their compute operations to make such systems work. Furthermore, we will see operators increasingly providing sophisticated data center resources network operations as well as for the customers they serve. If the speed of light is insufficient for some services or country geographies, additional edge data centers will be introduced, also leading to an increased energy consumption not present in todays telecom networks. Increased computing and storage demand will also make the absolute efficiency requirement highly challenging.
Will 5G be able to deliver bits (per second) more efficiently … Yes!
Will 5G be able to reduce the overall power consumption of todays telecom networks with 50% … highly unlikely.
In my opinion the industry will have done a pretty good technology job if we can keep the existing energy cost at the level of today (or even allowing for unit price increases over the next 10 years).
The Total power reduction of our telecommunications networks will be one of the most important 5G development tasks as the industry cannot afford a new technology that results in waste amount of incremental absolute cost. Great relative cost doesn’t matter if it results in above and beyond total cost.
≥ 99.999% NETWORK AVAILABILITY & DATA CONNECTION RELIABILITY.
A network availability of 5Ns across all individual network elements and over time correspond to less than a second a day downtime anywhere in the network. Few telecom networks are designed for that today.
5 Nines (5N) is a great aspiration for services and network infrastructures. It also tends to be fairly costly and likely to raise the level of network complexity. Although in the 5G world of heterogeneous networks … well its is already complicated.
5N Network Availability.
From a network and/or service availability perspective it means that over the cause of the day, your service should not experience more than 0.86 seconds of downtime. Across a year the total downtime should not be more than 5 minutes and 16 seconds.
The way 5N Network Availability is define is “The network is available for the targeted communications in 99.999% of the locations where the network is deployed and 99.999% of the time”. (from “4.4.4 Resilience and High Availability”, NGMN 5G White Paper).
Thus in a 100,000 cell network only 1 cell is allowed experience a downtime and for no longer than less than a second a day.
It should be noted that there are not many networks today that come even close to this kind of requirement. Certainly in countries with frequent long power outages and limited ancillary backup (i.e., battery and/or diesel) this could be a very costly design requirement. Networks relying on weather-sensitive microwave radios for backhaul or for mm-wave frequencies 5G coverage would be required to design in a very substantial amount of redundancy to keep such high geographical & time availability requirements
In general designing a cellular access network for this kind of 5N availability could be fairly to very costly (i.e., Capex could easily run up in several percentage points of Revenue).
One way out from a design perspective is to rely on hierarchical coverage. Thus, for example if a small cell environment is un-available (=down!) the macro-cellular network (or overlay network) continues the service although at a lower service level (i.e., lower or much lower speed compared to the primary service). As also suggested in the vision paper making use of self-healing network features and other real-time measures are expected to further increase the network infrastructure availability. This is also what one may define as Network Resilience.
Nevertheless, the “NGMN 5G White Paper” allows for operators to define the level of network availability appropriate from their own perspective (and budgets I assume).
5N Data Packet Transmission Reliability.
The 5G vision paper, defines Reliability as “… amount of sent data packets successfully delivered to a given destination, within the time constraint required by the targeted service, divided by the total number of sent data packets.”. (“4.4.5 Reliability” in “NGMN 5G White Paper”).
It should be noted that the 5N specification in particular addresses specific use cases or services of which such a reliability is required, e.g., mission critical communications and ultra-low latency service. The 5G allows for a very wide range of reliable data connection. Whether the 5N Reliability requirement will lead to substantial investments or can be managed within the overall 5G design and architectural framework, might depend on the amount of traffic requiring 5Ns.
The 5N data packet transmission reliability target would impose stricter network design. Whether this requirement would result in substantial incremental investment and cost is likely dependent on the current state of existing network infrastructure and its fundamental design.
If you have read Michael Lewis book “Flash Boys”, I will have absolutely no problem convincing you that a few milliseconds improvement in transport time (i.e., already below 20 ms) of a valuable signal (e.g., containing financial information) can be of tremendous value. It is all about optimizing transport distances, super efficient & extremely fast computing and of course ultra-high availability. The ultra-low transport and process latencies is the backbone (together with the algorithms obviously) of the high frequency trading industry that takes a market share of between 30% (EU) and 50% (US) of the total equity trading volume.
In a recent study by The Boston Consulting Group (BCG) “Uncovering Real Mobile Data Usage and Drivers of Customer Satisfaction” (Nov. 2015) study it was found that latency had a significant impact on customer video viewing satisfaction. For latencies between 75 – 100 milliseconds 72% of users reported being satisfied. The user experience satisfaction level jumped to 83% when latency was below 50 milliseconds. We have most likely all experienced and been aggravated by long call setup times (> couple of seconds) forcing us to look at the screen to confirm that a call setup (dialing) is actually in progress.
Latency and reactiveness or responsiveness matters tremendously to the customers experience and whether it is a bad, good or excellent one.
The Tactile Internet idea is an integral part of the “NGMN 5G Vision” and part of what is characterized as Extreme Real-Time Communications. It has further been worked out in detail in the ITU-T Technology Watch Report “The Tactile Internet” from August 2014.
The word “Tactile” means perceptible by touch. It closely relates to the ambition of creating a haptic experience. Where haptic means a sense of touch. Although we will learn that the Tactile Internet vision is more than a “touchy-feeling” network vision, the idea of haptic feedback in real-time (~ sub-millisecond to low millisecond regime) is very important to the idea of a Tactile Network experience (e.g., remote surgery).
The Tactile Internet is characterized by
Ultra-low latency; 1 ms and below latency (as in round-trip-time / round-trip delay).
Ultra-high availability; 99.999% availability.
Ultra-secure end-2-end communications.
Persistent very high bandwidths capability; 1 Gbps and above.
The Tactile Internet is one of the corner stones of 5G. It promises ultra-low end-2-end latencies in the order of 1 millisecond at Giga bits per second speeds and with five 9’s of availability (translating into a 500 ms per day average un-availability).
Interestingly, network predictability and variation in latency have not been receiving too much focus within the Tactile Internet work. Clearly, a high degree of predictability as well as low jitter (or latency variation), could be very desirable property of a tactile network. Possibly even more so than absolute latency in its own right. A right sized round-trip-time with imposed managed latency, meaning a controlled variation of latency, is very essential to the 5G Tactile Internet experience.
It’s 5G on speed and steroids at the same time.
Let us talk about the elephant in the room.
We can understand Tactile latency requirements in the following way;
An Action including (possible) local Processing, followed by some Transport and Remote Processing of data representing the Action, results in a Re-action again including (possible) local Processing. According with Tactile Internet Vision, the time of this whole even from Action to Re-action has to have run its cause within 1 millisecond or one thousand of a second. In many use cases this process is looped as the Re-action feeds back, resulting in another action. Note in the illustration below, Action and Re-action could take place on the same device (or locality) or could be physically separated. The processes might represent cloud-based computations or manipulations of data or data manipulations local to the device of the user as well as remote devices. It needs to be considered that the latency time scale for one direction is not at all given to be the same in the other direction (even for transport).
The simplest example is the mouse click on a internet link or URL (i.e., the Action) resulting a translation of the URL to an IP address and the loading of the resulting content on your screen (i.e., part of the process) with the final page presented on the your device display (i.e., Re-action). From the moment the URL is mouse-clicked until the content is fully presented should take no longer than 1 ms.
A more complex use case might be remote surgery. In which a surgical robot is in one location and the surgeon operator is at another location manipulating the robot through an operation. This is illustrated in the above picture. Clearly, for a remote surgical procedure to be safe (i.e., within the margins of risk of not having the possibility of any medical assisted surgery) we would require a very reliable connection (99.999% availability), sufficient bandwidth to ensure adequate video resolution as required by the remote surgeon controlling the robot, as little as possible latency allowing the feel of instantaneous (or predictable) reaction to the actions of the controller (i.e., the surgeons) and of course as little variation in the latency (i.e., jitter) allowing system or human correction of the latency (i.e., high degree of network predictability).
The first Complete Trans-Atlantic Robotic Surgery happened in 2001. Surgeons in New York (USA) remotely operated on a patient in Strasbourg, France. Some 7,000 km away or equivalent to 70 ms in round-trip-time (i.e., 14,000 km in total) for light in fiber. The total procedural delay from hand motion (action) to remote surgical response (reaction) showed up on their video screen took 155 milliseconds. From trials on pigs any delay longer than 330 ms was thought to be associated with an unacceptable degree of risk for the patient. This system then did not offer any haptic feedback to the remote surgeon. This remains the case for most (if not all) remote robotic surgical systems in option today as the latency in most remote surgical scenarios render haptic feedback less than useful. An excellent account for robotic surgery systems (including the economics) can be found at this web site “All About Robotic Surgery”. According to experienced surgeons at 175 ms (and below) a remote robotic operation is perceived (by the surgeon) as imperceptible.
It should be clear that apart from offering long-distance surgical possibilities, robotic surgical systems offers many other benefits (less invasive, higher precision, faster patient recovery, lower overall operational risks, …). In fact most robotic surgeries are done with surgeon and robot being in close proximity.
Another example of coping with lag or latency is a Predator drone pilot. The plane is a so-called unmanned combat aerial vehicle and comes at a price of ca. 4 Million US$ (in 2010) per piece. Although this aerial platform can perform missions autonomously it will typically have two pilots on the ground monitoring and possible controlling it. The typical operational latency for the Predator can be as much as 2,000 milliseconds. For takeoff and landing, where this latency is most critical, typically the control is handed to to a local crew (either in Nevada or in the country of its mission). The Predator cruise speed is between 130 and 165 km per hour. Thus within the 2 seconds lag the plane will have move approximately 100 meters (i.e., obviously critical in landing & take off scenarios). Nevertheless, a very high degree of autonomy has been build into the Predator platform that also compensates for the very large latency between plane and mission control.
Back to the Tactile Internet latency requirements;
In LTE today, the minimum latency (internal to the network) is around 12 ms without re-transmission and with pre-allocated resources. However, the normal experienced latency (again internal to the network) would be more in the order of 20 ms including 10% likelihood of retransmission and assuming scheduling (which would be normal). However, this excludes any content fetching, processing, presentation on the end-user device and the transport path beyond the operators network (i.e., somewhere in the www). Transmission outside the operator network typically between 10 and 20 ms on-top of the internal latency. The fetching, processing and presentation of content can easily add hundreds of milliseconds to the experience. Below illustrations provides a high level view of the various latency components to be considered in LTE with the transport related latencies providing the floor level to be expected;
In 5G the vision is to achieve a factor 20 better end-2-end (within the operators own network) round-trip-time compared to LTE; thus 1 millisecond.
So … what happens in 1 millisecond?
Light will have travelled ca. 200 km in fiber or 300 km in free-space. A car driving (or the fastest baseball flying) 160 km per hour will have moved 4 cm. A steel ball falling to the ground (on Earth) would have moved 5 micro meter (that’s 5 millionth of a meter). In a 1Gbps data stream, 1 ms correspond to ca. 125 Kilo Bytes worth of data. A human nerve impulse last just 1 ms (i.e., in a 100 millivolt pulse).
It should be clear that the 1 ms poses some very dramatic limitations;
The useful distance over which a tactile applications would work (if 1 ms would really be the requirements that is!) will be short ( likely a lot less than 100 km for fiber-based transport)
The air-interface (& number of control plane messages required) needs to reduce dramatically from milliseconds down to microseconds, i.e., factor 20 would require no more than 100 microseconds limiting the useful cell range).
Compute & processing requirements, in terms of latency, for UE (incl. screen, drivers, local modem, …), Base Station and Core would require a substantial overhaul (likely limiting level of tactile sophistication).
Require own controlled network infrastructure (at least a lot easier to manage latency within), avoiding any communication path leaving own network (walled garden is back with a vengeance?).
Network is the sole responsible for latency and can be made arbitrarily small (by distance and access).
Very small cells, very close to compute & processing resources, would be most likely candidates for fulfilling the tactile internet requirements.
Thus instead of moving functionality and compute up and towards the cloud data center we (might) have an opposing force that requires close proximity to the end-users application. Thus, the great promise of cloud-based economical efficiency is likely going to be dented in this scenario by requiring many more smaller data centers and maybe even micro-data centers moving closer to the access edge (i.e., cell site, aggregation site, …). Not surprisingly, Edge Cloud, Edge Data Center, Edge X is really the new Black …The curse of the edge!?
Looking at several network and compute design considerations a tactile application would require no more than 50 km (i.e., 100 km round-trip) effective round-trip distance or 0.5 ms fiber transport (including switching & routing) round-trip-time. Leaving another 0.5 ms for air-interface (in a cellular/wireless scenario), computing & processing. Furthermore, the very high degree of imposed availability (i.e., 99.999%) might likewise favor proximity between the Tactile Application and any remote Processing-Computing. Obviously,
So in all likelihood we need processing-computing as near as possible to the tactile application (at least if one believes in the 1 ms and about target).
One of the most epic (“in the Dutch coffee shop after a couple of hours category”) promises in “The Tactile Internet” vision paper is the following;
“Tomorrow, using advanced tele-diagnostic tools, it could be available anywhere, anytime; allowing remote physical examination even by palpation (examination by touch). The physician will be able to command the motion of a tele-robot at the patient’s location and receive not only audio-visual information but also critical haptic feedback.” (page 6, section 3.5).
All true, if you limited the tele-robot and patient to a distance of no more than 50 km (and likely less!) from the remote medical doctor. In this setup and definition of the Tactile Internet, having a top eye surgeon placed in Delhi would not be able to operate child (near blindness) in a remote village in Madhya Pradesh (India) approx. 800+ km away. Note India has the largest blind population in the world (also by proportion) with 75% of cases avoidable by medical intervention. At best, these specifications allow the doctor not to be in the same room with the patient.
Markus Rank et al did systematic research on the perception of delay in haptic tele-presence systems (Presence, October 2010, MIT Press) and found haptic delay detection thresholds between 30 and 55 ms. Thus haptic feedback did not appear to be sensitive to delays below 30 ms, fairly close to the lowest reported threshold of 20 ms. This combined with experienced tele-robotic surgeons assessing that below 175 ms the remote procedure starts to be perceived as imperceptible, might indicate that the 1 ms, at least for this particular use case, is extremely limiting.
The extreme case would be to have the tactile-related computing done at the radio base station assuming that the tactile use case could be restricted to the covered cell and users supported by that cell. I name this the micro-DC (or micro-cloud or more like what some might call the cloudlet concept) idea. This would be totally back to the older days with lots of compute done at the cell site (and likely kill any traditional legacy cloud-based efficiency thinking … love to use legacy and cloud in same sentence). This would limit the round-trip-time to air-interface latency and compute/processing at the base station and the device supporting the tactile application.
It is normal to talk about the round-trip-time between an action and the subsequent reaction. It is also the time it takes a data or signal to travel from a specific source to a specific destination and back again (i.e., round trip). In case of light in fiber, a 1 millisecond limit on the round-trip-time would imply that the maximum distance that can be travelled (in the fiber) between source to destination and back to the source is 200 km. Limiting the destination to be no more than 100 km away from the source. In case of substantial processing overhead (e.g., computation) the distance between source and destination requires even less than 100 km to allow for the 1 ms target.
THE HUMAN SENSES AND THE TACTILE INTERNET.
The “touchy-feely” aspect, or human sensing in general, is clearly an inspiration to the authors of “The Tactile Internet” vision as can be seen from the following quote;
“We experience interaction with a technical system as intuitive and natural only if the feedback of the system is adapted to our human reaction time. Consequently, the requirements for technical systems enabling real-time interactions depend on the participating human senses.” (page 2, Section 1).
The following human-reaction times illustration shown below is included in “The Tactile Internet” vision paper. Although it originates from Fettweis and Alamouti’s paper titled “5G: Personal Mobile Internet beyond What Cellular Did to Telephony“. It should be noted that the description of the Table is order of magnitude of human reaction times; thus, 10 ms might also be 100 ms or 1 ms and so forth and therefor, as we shall see, it would be difficult to a given reaction time wrong within such a range.
The important point here is that the human perception or senses impact very significantly the user’s experience with a given application or use case.
The responsiveness of a given system or design is incredible important for how well a service or product will be perceived by the user. The responsiveness can be defined as a relative measure against our own sense or perception of time. The measure of responsiveness is clearly not unique but depends on what senses are being used as well as the user engaged.The human mind is not fond of waiting and waiting too long causes distraction, irritation and ultimate anger after which the customer is in all likelihood lost. A very good account of considering the human mind and it senses in design specifications (and of course development) can be found in Jeff Johnson’s 2010 book “Designing with the Mind in Mind”.
The understanding of human senses and the neurophysiological reactions to those senses are important for assessing a given design criteria’s impact on the user experience. For example, designing for 1 ms or lower system reaction times when the relevant neurophysiological timescale is measured in 10s or 100s of milliseconds is likely not resulting in any noticeable (and monetizable) improvement in customer experience. Of course there can be many very good non-human reasons for wanting low or very low latencies.
While you might get the impression, from the above table above from Fettweis et al and countless Tactile Internet and 5G publications referring back to this data, that those neurophysiological reactions are natural constants, it is unfortunately not the case. Modality matters hugely. There are fairly great variations in reactions time within the same neurophysiological response category depending on the individual human under test but often also depending on the underlying experimental setup. In some instances the reaction time deduced would be fairly useless as a design criteria for anything as the detection happens unconsciously and still require the relevant part of the brain to make sense of the event.
Based on IAAF (International Athletic Association Federation) rules, an athlete is deemed to have had a false start if that athlete moves sooner than 100 milliseconds after the start signal. The neurophysiological process relevant here is the neuromuscular reaction to the sound heard (i.e., the big bang of the pistol) by the athlete. Research carried out by Paavo V. Komi et al has shown that the reaction time of a prepared (i.e., waiting for the bang!) athlete can be as low as 80 ms. This particular use case relates to the auditory reaction times and the subsequent physiological reaction. P.V. Komi et al also found a great variation in the neuromuscular reaction time to the sound (even far below the 80 ms!).
Neuromuscular reactions to unprepared events typically typically measures in several hundreds of milliseconds (up-to 700 ms) being somewhat faster if driven by auditory senses rather than vision. Note that reflex time scales are approximately 10 times faster or in the order of 80 – 100 ms.
The international Telecommunications Union (ITU) Recommendation G.114, defines for voice applications an upper acceptable one-way (i.e., its you talking you don’t want to be talked back to by yourself) delay of 150 ms. Delays below this limit would provide an acceptable degree of voice user experience in the sense that most users would not hear the delay. It should be understood that a great variation in voice delay sensitivity exist across humans. Voice conversations would be perceived as instantaneous by most below the 100 ms (thought the auditory perception would also depend on the intensity/volume of the voice being listened to).
Finally, let’s discuss human vision. Fettweis et al in my opinion mixes up several psychophysical concepts of vision and TV specifications. Alluding to 10 millisecond is the visual “reaction” time (whatever that now really means). More accurately they describe the phenomena of flicker fusion threshold which describes intermittent light stimulus (or flicker) is perceived as completely steady to an average viewer. This phenomena relates to persistence of vision where the visual system perceives multiple discrete images as a single image (both flicker and persistence of vision are well described in both by Wikipedia and in detail by Yhong-Lin Lu el al “Visual Psychophysics”). There, are other reasons why defining flicker fusion and persistence of vision as a human reaction reaction mechanism is unfortunate.
The 10 ms for vision reaction time, shown in the table above, is at the lowest limit of what researchers (see references 14, 15, 16 ..) find to be the early stages of vision can possible detect (i.e., as opposed to pure guessing ). Mary C. Potter of M.I.T.’s Dept. of Brain & Cognitive Sciences, seminal work on human perception in general and visual perception in particular shows that the human vision is capable very rapidly to make sense of pictures, and objects therein, on the timescale of 10 milliseconds (i.e., 13 ms actually is the lowest reported by Potter). From these studies it is also found that preparedness (i.e., knowing what to look for) helps the detection process although the overall detection results did not differ substantially from knowing the object of interest after the pictures were shown. Note that the setting of these visual reaction time experiments all happens in a controlled laboratory setting with the subject primed to being attentive (e.g., focus on screen with fixation cross for a given period, followed by blank screen for another shorter period, and then a sequence of pictures each presented for a (very) short time, followed again by a blank screen and finally a object name and the yes-no question whether the object was observed in the sequence of pictures). Often these experiments also includes a certain degree of training before the actual experiment took place. The relevant memory of the target object, In any case and unless re-enforced, will rapidly dissipates. in fact the shorter the viewing time, the quicker it will disappear … which might be a very healthy coping mechanism.
To call this visual reaction time of 10+ ms typical is in my opinion a bit of a stretch. It is typical for that particular experimental setup and very nicely provides important insights into the visual systems capabilities.
One of the more silly things used to demonstrate the importance of ultra-low latencies have been to time delay the video signal send to a wearer’s goggles and then throw a ball at him in the physical world … obviously, the subject will not catch the ball (might as well as thrown it at the back of his head instead). In the Tactile Internet vision paper it the following is stated; “But if a human is expecting speed, such as when manually controlling a visual scene and issuing commands that anticipate rapid response, 1-millisecond reaction time is required” (on page 3). And for the record spinning a basketball on your finger has more to do with physics than neurophysiology and human reaction times.
In more realistic settings it would appear that the (prepared) average reaction time of vision is around or below 40 ms. With this in mind, a baseball moving (when thrown by a power pitcher) at 160 km per hour (or ca. 4+ cm per ms) would take a approx. 415 ms to reach the batter (using an effective distance of 18.44 meters). Thus the batter has around 415 ms to visually process the ball coming and hit it at the right time. Given the latency involved in processing vision the ball would be at least 40 cm (@ 10 ms) closer to the batter than his latent visionary impression would imply. Assuming that the neuromuscular reaction time is around 100±20 ms, the batter would need to compensate not only for that but also for his vision process time in order to hit the ball. Based on batting statistics, clearly the brain does compensate for its internal latencies pretty well. In the paper “Human time perception and its illusions” D.M. Eaglerman that the visual system and the brain (note: visual system is an integral part of the brain) is highly adaptable in recalibrating its time perception below the sub-second level.
It is important to realize that in literature on human reaction times, there is a very wide range of numbers for supposedly similar reaction use cases and certainly a great deal of apparent contradictions (though the experimental frameworks often easily accounts for this).
The supporting data for the numbers shown in the above figure can be found via the hyperlink in the above text or in the references below.
Thus, in my opinion, also supported largely by empirical data, a good latency E2E design target for a Tactile network serving human needs, would be between 20 milliseconds and 10 milliseconds. With the latency budget covering the end user device (e.g., tablet, VR/AR goggles, IOT, …), air-interface, transport and processing (i.e., any computing, retrieval/storage, protocol handling, …). It would be unlikely to cover any connectivity out of the operator”s network unless such a connection is manageable from latency and jitter perspective though distance would count against such a strategy.
This would actually be quiet agreeable from a network perspective as the distance to data centers would be far more reasonable and likely reduce the aggressive need for many edge data centers using the below 10 ms target promoted in the Tactile Internet vision paper.
There is however one thing that we are assuming in all the above. It is assumed that the user’s local latency can be managed as well and made almost arbitrarily small (i.e., much below 1 ms). Hardly very reasonable even in the short run for human-relevant communications ecosystems (displays, goggles, drivers, etc..) as we shall see below.
For a gaming environment we would look at something like the below illustration;
Lets ignore the use case of local games (i.e., where the player only relies on his local computing environment) and focus on games that rely on a remote gaming architecture. This could either be relying on a client-server based architecture or cloud gaming architecture (e.g., typical SaaS setup). In general the the client-server based setup requires more performance of the users local environment (e.g., equipment) but also allows for more advanced latency compensating strategies enhancing the user perception of instantaneous game reactions. In the cloud game architecture, all game related computing including rendering/encoding (i.e., image synthesis) and video output generation happens in the cloud. The requirements to the end users infrastructure is modest in the cloud gaming setup. However, applying latency reduction strategies becomes much more challenging as such would require much more of the local computing environment that the cloud game architecture tries to get away from. In general the network transport related latency would be the same provide the dedicated game servers and the cloud gaming infrastructure would reside within the same premises. In Choy et al’s 2012 paper “The Brewing Storm in Cloud Gaming: A Measurement Study on Cloud to End-User Latency” , it is shown, through large scale measurements, that current commercial cloud infrastructure architecture is unable to deliver the latency performance for an acceptable (massive) multi-user experience. Partly simply due to such cloud data centers are too far away from the end user. Moreover, the traditional commercial cloud computing infrastructure is simply not optimized for online gaming requiring augmentation of stronger computing resources including GPUs and fast memory designs. Choy et al do propose to distribute the current cloud infrastructure targeting a shorter distance between end user and the relevant cloud game infrastructure. Similar to what is already happening today with content distribution networks (CDNs) being distributed more aggressively in metropolitan areas and thus closer to the end user.
A comprehensive treatment on latencies, or response time scales, in games and how these relates to user experience can be found in Kjetil Raaen’s Ph.D. thesis “Response time in games: Requirements and improvements” as well as in the comprehensive relevant literature list found in this thesis.
From the many studies (as found in Raaen’s work, the work of Mark Claypool and much cited 2002 study by Pantel et al) on gaming experience, including massive multi-user online game experience, shows that players starts to notice delay of about 100 ms of which ca. 20 ms comes from play-out and processing delay. Thus, quiet a far cry from the 1 millisecond. From the work, and not that surprising, sensitivity to gaming latency depends on the type of game played (see the work of Claypool) and how experienced a gamer is with the particular game (e.g., Pantel er al). It should also be noted that in a VR environment, you would want to the image that arrives at your visual system to be in synch with your heads movement and the directions of your vision. If there is a timing difference (or lag) between the direction of your vision and the image presented to your visual system, the user experience becomes rapidly poor causing discomfort by disorientation and confusion (possible leading to a physical reaction such as throwing up). It is also worth noting that in VR there is a substantially latency component simple from the image rendering (e.g., 60 MHz frame rate provides a new frame on average every 16.7 millisecond). Obviously chunking up the display frame rate will reduce the rendering related latency. However, several latency compensation strategies (to compensate for you head and eye movements) have been developed to cope with VR latency (e.g., time warping and prediction schemes).
Anyway, if you would be of the impression that VR is just about showing moving images on the inside of some awesome goggles … hmmm do think again and keep dreaming of 1 millisecond end-2end network centric VR delivery solutions (at least for the networks we have today). Of course 1 ms target is possible really a Proxima-Centauri shot as opposed to a just moonshot.
With a target of no more than 20 milliseconds lag or latency and taking into account the likely reaction time of the users VR system (future system!), that likely leaves no more (and likely less) than 10 milliseconds for transport and any remote server processing. Still this could allow for a data center to be 500 km (5 ms round.trip time in fiber) away from the user and allow another 5 ms for data center processing and possible routing delay along the way.
One might very well be concerned about the present Tactile Internet vision and it’s focus on network centric solutions to the very low latency target of 1 millisecond. The current vision and approach would force (fixed and mobile) network operators to add a considerable amount of data centers in order to get the physical transport time down below the 1 millisecond. This in turn drives the latest trend in telecommunication, the so-called edge data center or edge cloud. In the ultimate limit, such edge data centers (however small) might be placed at cell site locations or fixed network local exchanges or distribution cabinets.
Furthermore, the 1 millisecond as a goal might very well have very little return on user experience (UX) and substantial cost impact for telecom operators. A diligent research through academic literature and wealth of practical UX experiments indicates that this indeed might be the case.
Such a severe and restrictive target as the 1 millisecond is, it severely narrows the Tactile Internet to scenarios where sensing, acting, communication and processing happens in very close proximity of each other. In addition the restrictions to system design it imposes, further limits its relevance in my opinion. The danger is, with the expressed Tactile vision, that too little academic and industrious thinking goes into latency compensating strategies using the latest advances in machine learning, virtual reality development and computational neuroscience (to name a few areas of obvious relevance). Further network reliability and managed latency, in the sense of controlling the variation of the latency, might be of far bigger importance than latency itself below a certain limit.
So if 1 ms is no use to most men and beasts … why bother with this?
While very low latency system architectures might be of little relevance to human senses, it is of course very likely (as it is also pointed out in the Tactile Internet Vision paper) that industrial use cases could benefit from such specifications of latency, reliability and security.
For example in machine-to-machine or things-to-things communications between sensors, actuators, databases, and applications very short reaction times in the order of sub-milliseconds to low milliseconds could be relevant.
We will look at this next.
THE TACTILE INTERNET USE CASES & BUSINESS MODELS.
An open mind would hope that most of what we do strives to out perform human senses, improve how we deal with our environment and situations that are far beyond mere mortal capabilities. Alas I might have read too many Isaac Asimov novels as a kid and young adult.
In particular where 5G has its present emphasis of ultra-high frequencies (i.e., ultra small cells), ultra-wide spectral bandwidth (i.e., lots of Gbps) together with the current vision of the Tactile Internet (ultra-low latencies, ultra-high reliability and ultra-high security), seem to be screaming for being applied to Industrial facilities, logistic warehouses, campus solutions, stadiums, shopping malls, tele-, edge-cloud, networked robotics, etc… In other words, wherever we have a happy mix of sensors, actuators, processors, storage, databases and software based solutions across a relative confined area, 5G and the Tactile Internet vision appears to be a possible fit and opportunity.
In the following it is important to remember;
1 ms round-trip time ~ 100 km (in fiber) to 150 km (in free space) in 1-way distance from the relevant action if only transport distance mattered to the latency budget.
Considering the total latency budget for a 1 ms Tactile application the transport distance is likely to be no more than 20 – 50 km or less (i.e., right at the RAN edge).
One of my absolute current favorite robotics use case that comes somewhat close to a 5G Tactile Internet vision, done with 4G technology, is the example of Ocado’s warehouse automation in UK. Ocado is the world’s largest online-only grocery retailer with ca. 50 thousand lines of goods, delivering more than 200,000 orders a week to customers around the United Kingdom. The 4G network build (by Cambridge Consultants) to support Ocado’s automation is based on LTE at unlicensed 5GHz band allowing Ocado to control 1,000 robots per base station. Each robot communicates with the Base Station and backend control systems every 100 ms on average as they traverses ca. 30 km journey across the warehouse 1,250 square meters. A total of 20 LTE base stations each with an effective range of 4 – 6 meters cover the warehouse area. The LTE technology was essential in order to bring latency down to an acceptable level by fine tuning LTE to perform under its lowest possible latency (<10 ms).
5G will bring lower latency, compared to an even optimized LTE system, that in a similar setup as the above described for Ocado, could further increase the performance. Obviously very high network reliability promised by 5G of such a logistic system needs to be very high to reduce the risk of disruption and subsequent customer dissatisfaction of late (or no) delivery as well as the exposure to grocery stock turning bad.
This all done within the confines of a warehouse building.
ROBOTICS AND TACTILE CONDITIONS
First of all lets limit the Robotics discussion to use cases related to networked robots. After all if the robot doesn’t need a network (pretty cool) it pretty much a singleton and not so relevant for the Tactile Internet discussion. In the following I am using the word Cloud in a fairly loose way and means any form of computing center resources either dedicated or virtualized. The cloud could reside near the networked robotic systems as well as far away depending on the overall system requirements to timing and delay (e.g., that might also depend on the level of robotic autonomy).
Getting networked robots to work well we need to solve a host of technical challenges, such as
Latency.
Jitter (i.e., variation of latency).
Connection reliability.
Network congestion.
Robot-2-Robot communications.
Robot-2-ROS (i.e., general robotics operations system).
Power budget (e.g., power limitations, re-charging).
Redundancy.
Sensor & actuator fusion (e.g., consolidate & align data from distributed sources for example sensor-actuator network).
Context.
Autonomy vs human control.
Machine learning / machine intelligence.
Safety (e.g., human and non-human).
Security (e.g., against cyber threats).
User Interface.
System Architecture.
etc…
The network connection-part of the networked robotics system can be either wireless, wired, or a combination of wired & wireless. Connectivity could be either to a local computing cloud or data center, to an external cloud (on the internet) or a combination of internal computing for control and management for applications requiring very low-latency very-low jitter communications and external cloud for backup and latency-jitter uncritical applications and use cases.
For connection types we have Wired (e.g., LAN), Wireless (e.g., WLAN) and Cellular (e.g., LTE, 5G). There are (at least) three levels of connectivity we need to consider; inter-robot communications, robot-to-cloud communications (or operations and control systems residing in Frontend-Cloud or computing center), and possible Frontend-Cloud to Backend-Cloud (e..g, for backup, storage and latency-insensitive operations and control systems). Obviously, there might not be a need for a split in Frontend and Backend Clouds and pending on the use case requirements could be one and the same. Robots can be either stationary or mobile with a need for inter-robot communications or simply robot-cloud communications.
Various networked robot connectivity architectures are illustrated below;
ACKNOWLEDGEMENT
I greatly acknowledge my wife Eva Varadi for her support, patience and understanding during the creative process of creating this Blog.
“Neurophysiology: A Conceptual Approach” by Roger Carpenter & Benjamin Reddi (Fifth Edition, 2013 CRC Press).Definitely a very worthy read by anyone who want to understand the underlying principles of sensory functions and basic neural mechanisms.
“Designing with the Mind in Mind” by Jeff Johnson (2010, Morgan Kaufmann). Lots of cool information of how to design a meaningful user interface and of basic user expirence principles worth thinking about.
“On the impact of delay on real-time multiplayer games” by Lothar Pantel and Lars C. Wolf (Proceedings of the 12th International Workshop on Network and Operating Systems Support for Digital Audio and Video, NOSSDAV ’02, New York, NY, USA, pp. 23–29. ACM.).
“World first in radio design” by Cambridge Consultants. Describing the work Cambridge Consultants did with Ocado (UK-based) to design the worlds most automated technologically advanced warehouse based on 4G connected robotics. Please do see the video enclosed in page.
“Ocado: next-generation warehouse automation” by Cambridge Consultants.