Understanding the Economics of 5G Deployments

5G will fundamentally change mobility by delivering higher speeds and enabling more innovative use cases than past generations of wireless technology could support.

Drone perspectives on 5G economics

Head of 5G Strategy and Economics

Head of 5G Strategy and Economics

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These are compelling reasons for service providers to invest in 5G, particularly as the recent pandemic highlights the critical nature of mobile networks to the global economy.  As service providers capitalize on 5G, they must optimize the economics of these investments to ensure shareholder returns are maximized.

Ericsson’s 5G Strategy and Economics team has delivered over 70 engagements globally in recent years, helping service providers and investors to understand and optimize the economics and go-to-market strategy of next-generation wireless networks.  As shown in Figure 1, we have developed a 5G economics model that combines Ericsson’s best thinking on radio frequency (RF) theory with deep expertise in commercial strategy and economics to provide highly actionable insights to our clients.

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In this blog, I use this model to address several important questions about 5G that have emerged from our work with service providers in recent years:

  1. What are the primary economic drivers of 5G?
  2. How do economics vary under different deployment conditions?
  3. What are the economics of a typical next-generation network deployment?

 

Traffic Growth and Shannon’s Law

Growth in mobile traffic is among the foremost economic drivers of next generation wireless networks. As depicted in Figure 2, the Ericsson Mobility Report[1] projected traffic will grow from 10.7 gigabytes (GB) per sub per month in 2020 to 45 GB/sub/month by 2025. 

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This trend has profound implications for service providers, portending not only the need for future investments in network capacity, but with the prevalence of unlimited data plans, a continued decline in revenue per GB that makes unit economics – cost and margin per GB – increasingly important drivers of profitability.

Perhaps unknowingly in 1948, Claude Shannon framed the primary cost drivers for the wireless industry through his work in information theory.  Shannon characterized the wireless channel as the combination of spectrum, which service providers purchase from governments, and spectral efficiency, which service providers purchase from companies such as Ericsson, as illustrated in Figure 3.

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These two factors have evolved steadily with each generation of wireless technology as governments release more spectrum, and as technology companies extract more capacity from each ripple of spectrum deployed.

But each has limits.  Spectrum is a constrained natural resource, at least within the frequency bands considered suitable for cellular communication, and spectral efficiency is bounded by a ceiling defined by Shannon, notwithstanding technical advancements such as multi-user, multiple-input multiple-output technology (MU-MIMO).  So, in addition to pushing the envelope on spectrum and spectral efficiency, the industry is deploying new network models such as small cells and distributed antenna systems (DAS) that are more cost effective than traditional network models per unit, but that will drive higher unit volumes.  It is in this context that service providers must understand and optimize the economics of next-generation wireless investments. The studies that we have undertaken are directed to this need.

Economic Analysis of 5G deployments

In this section, we evaluate pure-play deployment scenarios for mobile broadband, fixed wireless access, and internet of things (MBB, FWA, and IoT, respectively).  Although these scenarios are not based on actual service provider deployments, the underlying assumptions reflect real-world conditions and yield useful if directional insights.  Results would differ for service provider-specific deployments, in which RF, demand-side, and economic assumptions may vary.

Using our 5G model, we demonstrate how performance varies under different deployment conditions for each of these scenarios, assuming a greenfield deployment within a 100 square kilometer market area over 7 years.  The traffic profiles in Figures 4 and 5 were assumed for each use case, representing the speeds and payloads per connection during the busy hour.

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Note that IoT encompasses a wide range of use cases, from lower traffic smart meter applications to higher traffic telemedicine solutions.  This study assumes a ‘generic’ IoT use case, based on the traffic profile shown in Figures 4 and 5.

As defined by the matrix in Figure 6, four primary sensitivities were considered (MIMO, connection density, spectrum band, and channel bandwidth) for each use case:

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Finally, all scenarios assume a 33% service provider market share, and utilize macros (taller cell sites) for coverage and micros (shorter cell sites) for capacity.  Based on these and other assumptions, summary cost per GB (CPGB) and revenue per GB (RPGB) are illustrated in Figure 7 for each use case.

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MBB is the most profitable and robust use case, with unit margins (RPGB – CPGB) positive under all conditions evaluated in this study.  FWA performs well under some but not all conditions.  Because FWA’s traffic profile far exceeds that of MBB, partly due to more stringent quality of service (QoS) requirements, its RPGB is substantially lower than MBB’s, as are its unit costs.  However, while not shown here, FWA can create additional value for service providers by reducing churn through the triple-play and quad-play bundles that it enables.  Like FWA, IoT performs well under some but not all conditions.  IoT also has higher unit costs on average than those of MBB and FWA, largely due to its relatively low assumed traffic profile.

The heat map in Figure 8 reveals additional details behind the summary the view in Figure 7, in which CPGB is reported by bandwidth, MIMO, spectrum band, and channel bandwidth.

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This matrix highlights the wide variability of performance under different conditions.  MBB performs well in all but rural scenarios with high-band spectrum.  It excels with mid-band spectrum whose unique mix of coverage and capacity aligns well with MBB’s traffic and subscriber density profile.  FWA performs best with wider channels of mid-band spectrum, and with high-band spectrum in denser markets and Connect America Fund (CAF) or Rural Digital Opportunity Fund (RDOF) subsidized rural markets.  It also benefits from MU-MIMO, which restores profitability in some mid-band scenarios that otherwise would be unprofitable.  FWA also can be served economically with wider channels of low-band spectrum in rural markets that benefit from CAF funding.  IoT is best served by low- and mid-band spectrum, although more advanced, high-traffic IoT use cases likely will benefit from the increased capacity of high-band spectrum assets.

Note that high-band is largely unaffected by changes in channel bandwidth and MIMO.  This can be explained in part by high-band’s propagation characteristics, which yield deployments that are range-limited by the uplink – that is, to achieve full market coverage, more sites are deployed than needed to serve traffic demand, resulting in a capacity surplus that reduces the impact of MU-MIMO, at least initially.  This uplink limitation also makes coverage dimensioning largely insensitive to channel bandwidth.

Finally, MU-MIMO yields modest capacity gains, assuming conservatively that up to 8 users can be served simultaneously.  For FWA, MU-MIMO gains likely would be higher than assumed because customer premise equipment (CPE) is stationary, allowing radio circuitry to optimize performance more fully than for mobile devices.  It's also important to note that the benefits of carrier aggregation (CA), in which multiple frequency bands are combined to improve network performance, are not fully reflected in this study, and would further improve deployment economics.

 

Key take-aways

This study provides several insights worth noting. 

  • Optimizing value per GB requires close attention to a wide range of parameters to identify the conditions under which networks perform best.
  • MBB is a uniquely attractive use case with its mix of higher ARPU and moderate traffic and performance demand. These attributes yield positive economics that are resilient to a wide range of deployment conditions. 
  • Mid-band can support a wide variety of use cases due to its unique coverage and capacity characteristics, in part explaining the strong interest in CBRS, C-Band, and others within the ~2 to 6 GHz range.
  • Markets with more connections per unit area generally yield better economic performance than those with fewer such connections because density drives utilization, yielding more revenue and margin per dollar of investment.
  • MU-MIMO can improve performance by reducing and forestalling investments needed to keep pace with market demand, even with the modest gains assumed in this study.
  • Notwithstanding its higher traffic demand per connection, lower RPGB, and more stringent QoS requirements, FWA has the potential to create value for service providers under the right conditions by delivering incremental revenue, and also by reducing churn by enabling triple-play and quad-play bundles.

The 5G Strategy and Economics team at Ericsson combines deep RF expertise with strengths in commercial strategy and economics to help service providers and investors optimize returns in next-generation networks.  Click the link below to download the full paper or contact me to learn more about our experience and capabilities.

Download the full paper

 

[1] Released June, 2020. https://www.ericsson.com/en/mobility-report

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