Contents lists available at ScienceDirect
Computers, Environment and Urban Systems
journal homepage: www.elsevier.com/locate/ceus
The importance of spatio-temporal infrastructure assessment: Evidence for
5G from the Oxford–Cambridge Arc
Edward J. Oughton
⁎
, Tom Russell
Environmental Change Institute, University of Oxford, South Parks Road, Oxford OX1 3QY, UK
ARTICLE INFO
Keywords:
Infrastructure
Assessment
5G
Simulation
Spatio-temporal
Telecommunications
ABSTRACT
The roll-out of 5G infrastructure can provide enhanced high capacity, low latency communications enabling a
range of new use cases. However, to deliver the improvements 5G promises, we need to understand how to
enhance capacity and coverage, at reasonable cost, across space and over time. In this paper, we take a spatio-
temporal simulation modeling approach, using industry-standard engineering models of 5G wireless networks,
to test how different infrastructure strategies perform under scenarios of uncertain future demand. We use
coupled open-source models to analyze a UK growth corridor, a system-of-cities comprising 7 urban areas,
known as the Oxford-Cambridge Arc. We find that population growth has a marginal impact on total demand for
5G (up to 15%), as the main factor driving demand is the increase in per user data consumption resulting mainly
from video. Additionally, the results suggest only limited justification for deploying 5G based purely on the need
for more capacity. Strategies which reuse existing brownfield Macro Cell sites are enough to meet future demand
for Enhanced Mobile Broadband, except in the densest urban areas. While spatio-temporal analysis of infra-
structure is common in some sectors (e.g. transport, energy and water), there has been a lack of open analysis of
digital infrastructure. This study makes a novel contribution by providing an open and reproducible spatio-
temporal assessment of different 5G technologies at a time when 5G is starting to roll-out around the world.
1. Introduction
5G is mooted to revolutionize urban environments by providing
higher capacity communications with lower latency. Mobile con-
nectivity has already changed how we interact with our environment,
and the resulting novel, often big, datasets have enabled advances in
urban analytics and the science of cities (Chin, Huang, Horn, Kasanicky,
& Weibel, 2019; Fan et al., 2018; Li, Gao, Lu, & Zhang, 2019; Li &
Goldberg, 2018; Ríos & Muñoz, 2017; Semanjski, Gautama, Ahas, &
Witlox, 2017; Tu et al., 2019; Wan et al., 2018; Yuan, Raubal, & Liu,
2012; Zhai, Wu, Fan, & Wang, 2018). Yet, there is a relative lack of
analysis of the infrastructure which enables this data collection. Reli-
able digital connectivity, with increased capacity and reduced latency,
can be used by a number of highly anticipated technologies, such as
Intelligent Transport Systems (Aliedani & Loke, 2019; Gurumurthy &
Kockelman, 2018) and massive real-time machine connectivity (such as
the ‘Internet of Things’, ‘industry 4.0’ etc.) (Cao & Wachowicz, 2019).
These technologies are proposed as solutions to a range of economic,
social and environmental problems in cities (Bergés & Samaras, 2019).
Digital infrastructure can be defined as the technologies that deliver
the internet, including fiber optic cable, legacy copper and coaxial
cable, as well as cellular (2G–6G), Wi-Fi and satellite broadband tech-
nologies. Demand for mobile data has been satisfied over the last
decade with the fourth generation of cellular technology, known as 4G,
which provided mass-market mobile broadband services to smartphone
users, spurring the development of the digital ecosystem and creating
vast amounts of user data. However, as data consumption has grown
exponentially year-on-year since the Apple iPhone release in 2007,
driven mainly by increased video consumption, mobile networks have
struggled to keep up with demand. Consequently, the fifth generation
(5G) of cellular technology provides significant improvements which
are particularly needed in capacity-constrained urban areas (Rendon
Schneir et al., 2019). Mobile Network Operators (MNOs) around the
world have begun to roll-out 5G infrastructure and are offering en-
hanced mobile broadband, although it will likely be years before most
users are covered.
5G has been seen by governments around the world as a cornerstone
of a successful future industrial strategy (Lemstra, 2018), with the USA,
China, Europe, South Korea as well as many others vying for leadership
of this new group of technologies. While there has been significant
engineering research on 5G, there has been little geospatial assessment
of infrastructure needs. This is despite many computational urban and
https://doi.org/10.1016/j.compenvurbsys.2020.101515
Received 16 November 2019; Received in revised form 4 June 2020; Accepted 5 June 2020
⁎
Corresponding author.
E-mail address: edward.oughton@ouce.ox.ac.uk (E.J. Oughton).
Computers, Environment and Urban Systems 83 (2020) 101515
Available online 15 June 2020
0198-9715/ Crown Copyright © 2020 Published by Elsevier Ltd. This is an open access article under the CC BY license
(http://creativecommons.org/licenses/BY/4.0/).
T
environmental researchers developing new technologies which need
digital connectivity for a range of use cases including river monitoring
(Ueyama et al., 2017), detection of forest fires (Aslan, Korpeoglu, &
Ulusoy, 2012; Ballari, Wachowicz, Bregt, & Manso-Callejo, 2012),
monitoring waste and surface water runoff (Rettig, Khanna,
Heintzelman, & Beck, 2014; Sempere-Payá & Santonja-Climent, 2012),
congestion analysis (Kan et al., 2019) and assessing urban movements
(Kim, 2018).
Given that 5G will be needed to meet future data demand, and given
the range of demand-side and supply-side factors affecting the roll-out
of 5G infrastructure, two research questions are posed:
1. Under different scenarios of population growth, how will mobile
telecommunication data demand be affected?
2. Which 5G infrastructure deployment strategies best meet future
demand?
In order to answer these questions, a literature review is first carried
out. A spatio-temporal technoeconomic method is then proposed in
Section 3, with the strategies to be tested outlined in Section 3.5, before
results are presented in Section 4. The findings are then discussed in
relation to the research questions in Section 5, with conclusions pro-
vided in Section 6.
2. Literature review
This literature review first provides an introductory overview of 5G
and its new technological capabilities, before reviewing the spatio-
temporal modeling of digital infrastructure.
2.1. What is 5G?
Currently, the main use case justifying the roll-out of 5G is enhanced
mobile broadband (eMBB), which is the first use case class supported by
Release 15 of the 3GPP 5G specification (3GPP, 2019) (known as ‘Non
Standalone’ 5G as it utilizes a 4G LTE core network). However, a variety
of other use cases are proposed for 5G, including ‘massive machine type
communications’ and ‘ultra-reliable and low latency communications’
(Martín, Pérez-Leal, & Navío-Marco, 2019). Data exchange is expected
to take place between humans, between humans and machines, and
between machines, as illustrated in Fig. 1.
An important feature of 5G is the ability to deliver connectivity to
specific uses via ‘slicing’ techniques, allowing different levels of Quality
of Service (QoS) for specific applications including virtual and aug-
mented reality (Erol-Kantarci & Sukhmani, 2018; Imottesjo & Kain,
2018), public safety (Naqvi, Hassan, Pervaiz, & Ni, 2018; Usman,
Gebremariam, Raza, & Granelli, 2015), manufacturing (Rao & Prasad,
2018a), connected and autonomous vehicles (Giust et al., 2018; Ullah
et al., 2019), health (Lloret, Parra, Taha, & Tomás, 2017) and utilities
(Mouftah, Erol-Kantarci, & Rehmani, 2018; Rao & Prasad, 2018b).
A range of new engineering technologies associated with the supply
of 5G enable this dramatic improvement in the quality of data access.
These include technical 5G features such as network function virtuali-
zation, software-defined networks, use of millimeter wave spectrum
and massive Multiple-Input Multiple-Output (mMIMO). These supply-
side techniques are expected to have significant demand-side impacts
by enabling digital transformation across vertical industrial sectors
(Cave, 2018). In this review, which is focused on the spatial analysis of
5G, we will not provide a comprehensive engineering overview of dif-
ferent technologies, therefore interested readers should explore the
many existing reviews available (Akyildiz, Nie, Lin, & Chandrasekaran,
2016; Andrews et al., 2014; Jaber, Imran, Tafazolli, & Tukmanov, 2016;
Panwar, Sharma, & Singh, 2016).
One school of thought suggests that 5G will require increased den-
sification of the network through the building of millions of Small Cells,
particularly in crowded areas of very high demand (Paglierani et al.,
2019). While Macro Cells may serve up to 30 km in remote rural areas,
Small Cells are expected to serve anywhere from 1 to 2 km down to
200 m in the densest urban settings. As 5G technologies are still evol-
ving, we lack analysis on the implications of Small Cell deployment
strategies, and the associated policy ramifications. Fig. 2 provides a
stylized example of a Macro Cell coverage area, with a high density of
Small Cells operating within this area to provide local high capacity
hot-spots.
There are a range of views on the potential impact of 5G, ranging
from optimistic (International Telecommunication Union, 2015) to
conservative (Webb, 2016). While impressive capacity can be achieved
with 4G LTE and 4G LTE-Advanced technologies, the mobile industry
will need to move to 5G and other technology generations over the
long-term in order to help reduce the cost per bit associated with data
transfer, as well as addressing a broader range of new use cases.
However, since the Average Revenue Per User (ARPU) has either been
static or declining in many major economies over the past decade, and
even declining globally (GSMA, 2020), there is not a huge appetite for
Fig. 1. 5G use cases (adapted from 5G Americas, 2017).
E.J. Oughton and T. Russell
Computers, Environment and Urban Systems 83 (2020) 101515
2
mass infrastructure deployment due to capital constraints.
2.2. Review of digital infrastructure assessment
This section focusses on past analyses which have modeled the
spatial and temporal aspects of digital communications infrastructure.
All infrastructure assets exist in space and time, therefore are inherently
subject to spatio-temporal dynamics (Kasraian, Maat, & van Wee, 2019;
Makarchuk & Saxe, 2019; Pacsi, Sanders, Webber, & Allen, 2014; Peer,
Garrison, Timms, & Sanders, 2016; Sanders, 2015; Serok, Levy, Havlin,
& Blumenfeld-Lieberthal, 2019). As infrastructure is highly durable and
becomes a sunk cost in an existing asset portfolio, these assets ulti-
mately become subject to path-dependent lock-in effects, where mi-
gration away from a particular technological trajectory becomes costly
due to increasing returns to scale (Arthur, 1994). These generic prop-
erties affect how we understand and model these complex adaptive
systems in order to inform their future design, operation and main-
tenance (Chester & Allenby, 2019; Gilrein et al., 2019; Oughton, Usher,
Tyler, & Hall, 2018; Saxe & MacAskill, 2019).
In other critical infrastructure systems, such as transportation, there
is a well-established set of open-source spatio-temporal tools, methods
and models that can be applied to answer questions pertaining to in-
teractions between land use, transportation and the environment
(Capelle, Sturm, Vidard, & Morton, 2019; Harvey et al., 2019; Kii,
Moeckel, & Thill, 2019). Some private companies have developed their
own internal capabilities to assess how the movement of people and
vehicles may place new demand on the cellular network. However,
these activities are not made openly available. Thus, in terms of open-
source tools, methods and models accessible to the research commu-
nity, there are a limited number available for the spatio-temporal
analysis of digital infrastructure.
Spatial engineering approaches to wireless networks are frequently
considered from a theoretical perspective, often using random processes
to synthesize networks for assessment. Topology planning is essential
for efficient capacity utilization and thus cost efficiency (Haddaji,
Bayati, Nguyen, & Cheriet, 2018; Jaber et al., 2016; Lee & Murray,
2010; Taufique, Jaber, Imran, Dawy, & Yacoub, 2017). Models often
use spatial point methods, where user location is treated as a stationary
Poisson point process, base station locations as a stationary Poisson
cluster process and connecting internet nodes as a stationary mixed
Poisson process (Suryaprakash & Fettweis, 2014). Frequently, sto-
chastic geometry is applied for system-level performance analysis with
the network treated as a hierarchical process using a Poisson tree, given
certain radio channel conditions and interference (Martin-Vega, Di
Renzo, Aguayo-Torres, Gomez, & Duong, 2015). Wireless planning is
often treated as an optimization problem, although most analyses focus
on a single variable (e.g. cost) (Taghizadeh, Sirvi, Narasimha, Calvo, &
Mathar, 2018), rather than considering the multiple objectives which
are needed to reflect key performance parameters (Li et al., 2016).
Spatial optimization techniques are well suited for wireless networks
because they can help define the maximum level of coverage, with the
highest degree of reliability (Akella, Delmelle, Batta, Rogerson, & Blatt,
2010; Lee & Murray, 2010; Shillington & Tong, 2011). This includes
using mixed integer programming approaches to help simultaneously
solve problems such as basestation location, frequency channel as-
signment and the support of emergency communication services (Akella
et al., 2005). Increasingly, natural language processing and machine
learning is being combined with data mining social media to develop
empirical geospatial analytics on wireless network performance (Du
et al., 2018).
As well as assessing the spatial topological design of wireless net-
works, which is common in the engineering literature (González,
Hakula, Rasila, & Hämäläinen, 2018), consideration also needs to be
paid to temporal decisions. There can be significant implications for
future upgrade costs, given the path-dependent nature of infrastructure
assets. While spatio-temporal analysis is studied in detail using in-
ductive statistical techniques on empirical data (Liu, Liu, Tang, Deng, &
Liu, 2019; Montero-Lorenzo, Fernández-Avilés, Mondéjar-Jiménez, &
Vargas-Vargas, 2013; Song, Zhao, Zhong, Nielsen, & Prishchepov,
2019), deductive spatio-temporal simulation approaches are still an
emerging area of research (Xie, Yang, Zhou, & Huang, 2010; Yang, Yu,
Hu, Jiang, & Li, 2017). National spatio-temporal assessment of 5G has
been undertaken for the UK (Oughton & Frias, 2018; Oughton, Frias,
Russell, Sicker, & Cleevely, 2018), but there has been little sub-national
focus. In general, relatively little (openly accessible) spatio-temporal
modeling has been undertaken for telecommunications when compared
to the level of assessment in transportation networks. Much of the
geospatial analysis of fixed and mobile broadband networks over the
past two decades has relied on building an empirical statistical evidence
base from inductive methods (Grubesic, 2006, 2008, 2010; Mack &
Grubesic, 2014; Tranos, 2013; Tranos & Mack, 2015), in order to inform
future decisions.
The standard approach for assessing the economics of cellular net-
works is in terms of the Total Cost of Ownership (TCO) under different
deployment scenarios, including all infrastructure, energy and main-
tenance costs, as well as the potential leasing of both spectrum and fiber
(Yaghoubi et al., 2018). There is a need to analyze a full range of de-
ployment options, as a lack of planning prior to deployment can result
in significantly higher costs, and greater inefficiency in energy con-
sumption. Such an approach is common in the engineering literature
(Cano, Carello, Cesana, Passacantando, & Sansò, 2019; Frias & Pérez,
2012; Giglio & Pagano, 2019; Yunas, Ansari, & Valkama, 2016), but
these techniques are rarely applied spatially, for example at the sub-
national level.
2.3. Infrastructure assessment and 5G
Infrastructure assessment provides a comprehensive overview of the
future supply and demand of energy, transport, digital, water and waste
services under different potential scenarios (Chester & Allenby, 2019;
Fig. 2. A typical 5G Macro Cell containing many Small Cell hotspots.
E.J. Oughton and T. Russell
Computers, Environment and Urban Systems 83 (2020) 101515
3
Garcia et al., 2019; Hall et al., 2016; Saxe, Casey, Guthrie, Soga, &
Cruickshank, 2015; Saxe, Miller, & Guthrie, 2017). This is essential
evidence for making effective policy decisions given the huge chal-
lenges faced in delivering the infrastructure needed over coming dec-
ades. In infrastructure sectors such as energy or transportation one of
the most pressing issues is environmental sustainability, whereas in
digital infrastructure the two major issues are: connecting all users who
are still yet to be connected (approximately 3 billion globally are yet to
acquire basic internet access); and addressing the major disparities
between basic broadband services (e.g. 2 Mbps) and those with high
capacity connections.
One of the first infrastructure assessments of 5G was the UK
Government's Connected Future report (National Infrastructure
Commission, 2016) along with affiliated supporting evidence (Frontier
Economics, 2016; LS telcom, 2016; Oughton & Frias, 2016; Real
Wireless, 2016). Analysis has estimated it would take until 2027 for the
majority of the population to be covered by 5G (Oughton & Frias,
2018), with the UK government now having adopted this target in both
Ofcom's Connected Nation report and in the UK's Future Telecom Re-
view (Department for Digital, Culture, Media, and Sport, 2018; Ofcom,
2018a). Currently we still lack quantified evidence of how this might be
achieved in practice. Throughout the UK coverage issues still exist in
many areas, with only 76% of premises receiving an indoor 4G signal
from all operators, approximately 64% of the geographic area (Ofcom,
2018b). Past UK spectrum coverage obligations include 90% population
coverage on the 3G bands including 900, 1800 and 2100 MHz, and then
a 98% population requirement on the 4G LTE 800 MHz with 90%
confidence at indoor locations with a downlink speed of not less than
2 Mbps (2600 MHz has no coverage obligation) (Cave and Nicholls,
2017). Having completed a thorough literature review on 5G infra-
structure assessment, a method capable of answering the research
questions will now be presented.
3. Method
A demand-led simulation modeling approach is taken to develop a
high-resolution spatially explicit implementation of a telecommunica-
tion Long Run Incremental Cost (LRIC) model. A range of software
development techniques have been used to develop the model, in-
cluding unit testing, in-line documentation and open-source model code
(Oughton & Russell, 2019, 2020), all of which aim to improve trans-
parency, reproducibility and confidence in the results. The system
model will now be described, followed by the demand and capacity
assessment components. The upgrade strategies to the tested are out-
lined in Section 3.5.
3.1. System model description
Using a national multi-level network, assets are geographically
nested in a spatial hierarchy enabling a spatio-temporal simulation
model for the period 2020–2030. A Network Manager object contains
all the necessary data and methods for simulating different deployment
strategies and is operated via a model runner script, as illustrated in
Fig. 3.
Data inputs include spatially disaggregated demographic scenarios
and scenarios of how per-user data demand may evolve in the future,
which is a standard way to a assess how future population change may
affect demand for services (Mayaud, Tran, Pereira, & Nuttall, 2019).
Geospatial information is required for site locations, as well as data on
the available spectrum portfolio allocation by carrier frequency,
bandwidth and technology generation.
3.2. Demand assessment
Predicting future demand is challenging because a key characteristic
of the digital ecosystem is rapid innovation which can drive
technological change. The adoption of smartphones led to significantly
increased per-user data demand resulting predominantly from aug-
mented video consumption. Average per user data demand has risen
from approximately 0.2 GB per month in 2012 to 1.9 GB per month in
2017 (Ofcom, 2018c).
The widely-used Cisco traffic forecast expects mobile traffic to
continue to grow significantly over coming years, with mobile data
traffic in the UK expected to grow at 38.5% Compound Annual Growth
Rate (CAGR), from 2017 to 2022 (Cisco, 2017). The Cisco estimates are
commonly used in the literature (Rendon Schneir et al., 2019), hence a
set of data demand forecasts are created for Low, Baseline and High
scenarios. One of the largest unknown factors is the adoption of un-
limited data plans which could have a substantial impact on future data
growth.
In this model, key demand drivers for cellular capacity include (i)
the per user throughput rate and (ii) the number of users in an area. The
average User Data Rate is determined from the scenario forecasts,
providing estimated traffic demand in gigabytes per user per month in
Britain (UD
i
GBpm
) from which the busy hour individual demand in
megabits per user per second (UD
i
Mbps
) is estimated for the i
th
user ac-
cording to (1)
=UD UD
t b
1 1
1024 8
1
3600
i
Mbps
i
GBpm
(1)
Monthly traffic is converted to daily based on 30 days per month (t),
and where 40% of daily traffic (b) takes place in the busy hour. The
demand result is converted from gigabytes to megabytes, from bytes to
bits, and finally from hourly demand to per second demand.
Additionally, a minimum guaranteed user speed of 5 Mbps is applied as
an evolution of the UK's 4G LTE coverage obligation of 2 Mbps.
The Users Per Area is estimated from the population (P
i
) of re-
sidents per postcode sector using a set of demographic scenarios which
use an open-source spatial interaction model, simim (Smith, Russell, &
Usher, 2019). Variations to internal migration are then based on
changing regional attractiveness, driven primarily by the dwelling
scenarios described later in Section 3.5. Population is produced and
validated for 380 Local Authority Districts using census data and Office
for National Statistics projections, then disaggregated to 9000 national
postcode sectors using a set of weights based on the share of domestic
postal delivery points.
Given user demand changes through the day, as users move about
dynamically in space, we consider the residential population estimates
as representing a nighttime profile. To supplement the daytime users,
employment data are taken from the UK Business Register and
Employment Survey (Nomis, 2018) by Lower Level Super Output Area
and are aggregated to the postcode sector level, to help capture this
spatio-temporal dynamic. If the daytime employment exceeds the re-
sident population, the resident population is augmented by the addi-
tional employment amount. This means each postcode sector broadly
represents the busiest time of day based on population movement.
Market Share is defined as a scenario parameter used to model a
‘hypothetical operator’. We use a market share (share
i
) of 30% of users,
broadly in line with the UK's Mobile Call Termination Market Review
(Ofcom, 2018d). It is also reasonable to expect that not all users will
simultaneously access the network at once, as is standard practice for
network dimensioning traffic throughput (Holma & Toskala, 2012), and
therefore an overbooking factor (OBF) of 20 is used. Smartphone pe-
netration (penetration
i
) in Britain is 80%, so only this proportion of the
population will access high capacity wireless services such as 4G LTE or
5G.
The total number of active users (AU
i
) accessing the cellular net-
work in an area can therefore be estimated using eq. (3)
=AU
P share penetration
OBF
( ) ( )
i
i i i
(3)
Resulting in the total demand (TD
i
Mbps
) for the i
th
area as follows in
E.J. Oughton and T. Russell
Computers, Environment and Urban Systems 83 (2020) 101515
4
eq. (4)
=TD UD AU
i
Mbps
i
Mbps
i
(4)
Having formally defined the demand assessment method, the ca-
pacity assessment method can now be articulated.
3.3. Capacity assessment
The capacity assessment module quantifies cellular capacity ex-
pansion using three methods: improving spectral efficiency via new
technology generation (e.g. 4G–5G); the provision of new spectrum
bands; and the deployment of new cells to densify the network.
The mean Network Spectral Efficiency (η
i
f
) (bps/Hz/km
2
) is esti-
mated per frequency (f) for the ith area, given a set number of cells
(β
cells
) per site, and the density of sites (γ
sites
) in an area, as in eq. (5)
=
i
f
cells sites
(5)
This is carried out using a stochastic geometry approach via the
open-source python simulator for integrated modeling of 5G, pysim5G
(Oughton, 2019; Oughton, Katsaros, Entezami, Kaleshi, & Crowcroft,
2019). First, pysim5G estimates the Signal to Interference plus Noise
Ratio (SINR) in different urban and rural environments using industry-
standard statistical propagation models. Next, the ETSI coding and
modulation lookup tables for 5G are used to map received signal to
SINR (European Telecommunications Standards Institute, 2018). The
spectral efficiency for 8×8 Multiple Input Multiple Output (MIMO)
antenna is mapped to the SINR (Tse & Viswanath, 2005). The estimated
capacity per square kilometer can then be obtained (C
i
Mbps
) for the ith
area by multiplying the spectral efficiency (η
i
f
) by the bandwidth of the
carrier frequency (BW
f
), as in eq. (6)
=C BW
i
Mbps
f
i
f
f
(6)
Finally, to ensure a specific Quality of Service, the stochastic ap-
proach allows the 10th percentile value to be extracted from the dis-
tribution of simulation results for each frequency. This means that the
network capacity is assessed for the cell edge user with 90% reliability.
Using the geographic area (a
i
) in square kilometers, the resulting
mean guaranteed capacity per active user (C
AU
Mbps
) during the busiest
hour can be calculated, as per eq. (7).
=C C a AU a( )/( )
AU
Mbps
i
Mbps
i i i
(7)
To obtain a set of representative physical site assets the Sitefinder
data (Ofcom, 2012) is updated to be consistent with existing 4G cov-
erage statistics released by Ofcom's Connected Nation report. In recent
years, passive infrastructure sharing agreements have essentially cre-
ated two physical networks in the UK, the first between Vodafone and
O2 Telefonica (‘Cornerstone’) and the second between BT/EE and
Hutchinson Three. We consider the Vodafone and O2 Telefonica
(‘Cornerstone’) sites as the key supply-side input for (predominantly
Macro Cell) sites. Representative site locations are obtained by taking
latitude and longitude coordinates for individual cell assets, buffered by
100 m, with the polygon centroid of touching buffers forming a rea-
sonably accurate location approximation. Due to site sharing, a single
MNO has access to roughly 50% of sites, resulting in 22,589 national
sites. The statistics are disaggregated by ranking the revenue potential
of each postcode sector and calculating the cumulative geographic area
covered using the expectation that MNOs rationally deliver 4G coverage
to the highest revenue sites first. This approach is consistent with how
Fig. 3. Digital communications system-level evaluation framework.
E.J. Oughton and T. Russell
Computers, Environment and Urban Systems 83 (2020) 101515
5
