Cost efficient capacity expansion strategies using
multi-access networks
Klas Johansson
Anders Furuskär
Wireless@KTH, Royal Institute of Technology
Electrum 418, SE-164 40 Kista, Sweden
Email: klasj@radio.kth.se
Wireless Access Networks, Ericsson Research
Kista, Sweden
Email: anders.furuskar@ericsson.com
Abstract— Multi-access networks and hierarchical cell structures are two common capacity expansion strategies for mobile
network operators. In both cases costs can be minimized for
a set of available radio access technologies, given heterogeneous
requirements on area coverage, capacity and quality of service. In
this paper we quantify the infrastructure cost for a multi-access
network composed of macro cellular HSDPA base stations and
IEEE 802.11g WLAN access points. The network is dimensioned
for an urban environment using a stochastic model for heterogeneous traffic density.
With the used assumptions and modelling it is shown that
a combination of HSDPA base stations deployed with 400m
cell radius together with WLAN in hot spots are sufficient for
average traffic densities up to around 50Mbps/km2 (50 times
the traffic of typical private voice users today). In order to
evaluate the sensitivity to different design features, we introduce
the elasticity of infrastructure cost and can thereby show that it
is more important to improve capacity in HSDPA than coverage
per 802.11g access point. However, with a sparse deployment
of HSDPA macro cells (800m radius) infrastructure cost is
more elastic to 802.11g coverage. The paper also indicates
some possibilities to differentiate future radio access technologies
towards current systems.
I. I NTRODUCTION
Multi-access networks are promising due to highly varying
requirements over time and geographically on mobility, quality
of service, capacity, etc., and the inherent tradeoff in all
wireless systems between range and feasible data rates. Hence,
by deploying a heterogeneous wireless network, with multiple
standards and/or hierarchical cell structures, an operator can
adapt capacity to demand and thereby lower their capital and
operational expenditures (CAPEX/OPEX). There are in fact
many wireless standards in the market already today, and
even more are under development. Some systems, like 3G,
are designed to benefit from economy of scope, meaning that
cost efficiency is achieved since a wide range of services can
be provided with the same system over wide areas, potentially serving many users. Other standards are streamlined for
specific services, such as 2G for wide-area mobile voice and
WLAN for local high-speed data connectivity. There are hence
reasons to believe that multi-access networks is a sustainable
deployment strategy for mobile network operators.
This paper treats cost efficient deployment strategies for
networks composed of HSDPA macro cellular base stations
(BS) and IEEE 802.11g access points (AP). These should
represent systems with long range for wide area coverage and
low cost, short range access points suitable for hot spots. More
specifically, two problems will be addressed; (i) the tradeoff
between the number of macro cellular BSs and complementary
WLAN APs needed, and (ii) which parameters that are most
important to improve in each system in order to further
reduce costs. This study complements the results presented
in [3], which evaluates the cost with single and multi-access
deployment for a number of, both commercially available, and
future radio access technologies. In both studies the scope is
limited to the radio access network. Spectrum license fees and
other costs that are common for the whole mobile network
are thus excluded. Likewise are a number of other parameters
that also are of importance for a mobile operator’s deployment
strategy; e.g. topology, previous assets, and (perhaps foremost)
demand and regulatory requirements. Yet, the objective is to
contribute to a better understanding of the role of multi-access
as capacity expansion strategy and for this reason a stochastic
(log-normal) spatial distribution of traffic is assumed [3].
Service allocation principles for multi-access networks have
previously been treated in, e.g., [4] and [7]. These studies,
however, addresses the problem of selecting radio access
network for users that are covered by multiple systems, and not
dimensioning of each subsystem. In addition, EU has recently
initiated the Ambient Networks project which deals with a
number of aspects, mainly technically but also business wise,
of heterogeneous networks [9] and there are hence a number
of ongoing studies in this area. Previous studies considering
the cost structure of mobile systems include, e.g., [6], [8], [11],
and [12]. From these studies it is clear that the cost structure
of mobile networks today is dominated by the radio access
network. Moreover, the studies in [6] and [8] provide a basis
for the techno-economical modelling used in the sequel of this
paper which is outlined as follows.
Section II covers basic modelling and assumptions related to
BS performance and costs, as well as network dimensioning.
Infrastructure cost estimations for a multi-access network is
presented in Section III for incumbent and greenfield operators
respectively, together with an analysis of the elasticity of cost
with respect to the capacity, coverage and cost per BS. In
Section IV we discuss how the studied systems could be
improved technically and economically, and point at a few
gaps that potentially could be filled by future radio access
technologies. The paper is concluded in Section V.
II. S YSTEM M ODELS AND P ERFORMANCE M EASURES
A macroscopic model is used to capture key technical and
economical parameters that influence the infrastructure cost for
a typical (western European) mobile network operator. This is
based on previous work presented in [4] and factors normally
modelled in network dimensioning and capacity analysis; like
interference, propagation, etc., are exogenous to the model.
A. Network dimensioning and traffic modelling
In a multi-access network APs with shorter range and cost
may be used in hot spots, whereas macro BSs with a high
area coverage are used to provide basic coverage and capacity.
In practice radio network planning and dimensioning is an
iterative process [2] and as the network matures it is gradually
adapted to local demand. To model the deployment strategy we
use a heuristically based method which is described in more
detail in [3]. The basic idea is, in our example, to first deploy
HSDPA sites with a given cell radius and load them with as
many transceiver units as needed (up to the maximum capacity
per BS). If this is not sufficient to serve the traffic demand,
802.11g APs are deployed in areas with highest traffic density.
Ideally, each subsystem should be just capacity limited for a
cost effective deployment to avoid excess capacity per AP.
Traffic density is modelled as in [3] with a log-normal,
spatially correlated, stochastic variable over the service area
(10x10km) which herein is divided into samples of 20x20m.
A standard deviation of 7dB has been assumed and the correlation distance is 500m, which matches the cell level statistics
presented in [5]. In all numerical examples the average population density is 20 000 inhabitants/km2 , corresponding to a
city center environment. The operator under study is assumed
to have a 30% market share and the service penetration is
90%. Hence, the number of subscribers is in average 5400
users/km2 (locally this is much higher).
B. Path loss models
Although not used explicitly for the network dimensioning,
two standard path loss models will be used to estimate the
cell range of macro cells in Section IV; the COST231-Hata
(valid for 1km < d < 20km) and COST231-Walfisch-Ikegami
(valid for 20m < d < 5km). In general the path loss in small
cells, and in particular indoors, is case specific and not readily
described with a statistical model. However, these commonly
used models provided in [1] should give an indication of
feasible cell ranges and the same methodology has been used
in, e.g., [2] for rudimentary coverage analysis.
COST231-Walfisch-Ikegami - assuming building separation
(30m), street width (15m), building height (25m), and a 90
degree angle of arrival for building reflections:
1.5fc
) log10 fc + 38 log10 d. (1)
925
COST231-Hata - with 3dB metropolitan area correction
factor and an Okamura-Hata B factor as recommended for
large cities:
Lb = 57.9 + (27.5 −
Lb = 28.9 + 33.9 log10 fc + 35.2 log10 d.
(2)
TABLE I
ACCESS POINT CHARACTERISTICS
Radius
Capacity
Cost coefficient
(CAPEX/OPEX)
HSDPA
200-1000m
[3-9] x 2.5Mbps
1 (55%/45%)
+ 0.03 per cell
802.11g
40m
22Mbps
0.13 (3%/97%)
In both models the BS height was 30m and mobile station
height 1.5m. Lb denote path loss in dB, fc is the carrier
frequency in MHz, and d is the distance between BS and
mobile station given in km.
C. Access point performance and cost assumptions
APs are characterized with different cell radii, capacities and
costs; see Table I. Capacity coefficients for 802.11g assumes
no co-channel interference whereas HSDPA does, due to the
cellular deployment and limited frequency spectrum – we
assume 3 carriers x 5Mhz (15MHz in total) for downlink.
Notice that the maximum capacity for HSDPA and 802.11g
is similar, 22.5 and 22Mbps, so the AP with lowest cost per
transmitted bit is essentially determined by the geographical
distribution of traffic.
Cost coefficients include both CAPEX and OPEX and are
henceforth denoted AP cost. For HSDPA we use the cost for a
macro BS derived in [6], which in turn was based on estimates
provided by the Gartner Group and [8]. In the numerical
examples we have assumed that a macro BS costs e300k.
Costs for radio network controllers (RNC) and electrical power
have been added as compared to the estimates in [6]. Table
I also summarizes the cost structure in terms of CAPEX
and OPEX and the additional cost for extra cells (defined
as a carrier frequency and sector) in HSDPA. An incumbent
operator that already has sites for legacy systems installed may
reuse most of these sites and we assume that this lowers the
cost for HSDPA BSs with 25%. For 802.11g new estimates
have been deducted based on [8]. OPEX is calculated in
present value over a 10-year period, using a 10% discount
rate (see further [6]). For the sake of simplicity the network is
dimensioned to carry the same traffic during the whole network
life span.
D. Infrastructure cost measures
The basic measure for cost efficiency used is the infrastructure cost per GB and month. In doing this we assume that 0.6%
of the monthly traffic is carried during each busy hour, which
roughly corresponds to the traffic pattern in current cellular
systems, and that the network is dimensioned according to
average aggregate throughput (per area sample). Hence, the
results and conclusions should hold for all traffic mixes that
fall within the performance parameters given in Table I.
As a sensitivity analysis we estimate the elasticity of infrastructure cost. Elasticity is commonly used in economics
to measure the incremental percentage change in one variable
with respect to an incremental percentage change in another
variable [10]. We define the elasticity of a parameter X (which
Infrastructure Cost per Month and GB [Euro]
TABLE II
S UMMARY OF MONTHLY INFRASTRUCTURE COSTS PER GB AND THE COST
ADVANTAGE FOR INCUMBENTS TOWARDS GREENFIELD OPERATORS .
200m
400m
800m
1000m
2
10
Traffic density
HSDPA radius
HSDPA BS density
HSDPA cells/BS
WLAN AP density
Incumbent operator
Greenfield operator
Cost advantage
for incumbent
1
10
Voice
1Mbps/km2
1000m
0.33BSs/km2
1.4cells
0APs/km2
e6.8
e8.8
24%
10 x voice
10Mbps/km2
800m
0.56BSs/km2
5.7cells
2.1APs/km2
e2.9
e3.4
15%
50 x voice
50Mbps/km2
400m
2.2BSs/km2
6.8cells
19APs/km2
e1.7
e1.9
10%
0
10
Voice
0
10
10 x voice
1
10
Average Traffic Density [Mbps/km2]
100 x voice
2
10
Fig. 1. Infrastructure cost per GB and month for an incumbent operator with
a multi-access network consisting of HSDPA macro BSs and IEEE 802.11g
APs. The curves depict different cell radii in the macro cells.
herein is either cost, coverage or capacity per AP) on the total
infrastructure cost C as:
∆C/C
.
(3)
EC,X =
|∆X| /X
Thus, a negative EC,X corresponds to a decreased cost and
if EC,X is positive the infrastructure cost increases (independently of if the changed variable X is increased or decreased).
Thus, the higher absolute elasticity, the greater impact X has
on C. Notice that elasticity quite often is calculated in absolute
value. A 50% change in X has been used in all studied cases so
that |∆X|/X = 0.5. As an example, assume that we want to
estimate the elasticity with respect to AP coverage in 802.11g.
EC,X = −1 would then mean that the total infrastructure cost
C decreases with 50%
√ if the cell area is doubled. I.e., if the
AP range were 40 · 1.5 = 49m instead of 40m.
III. N UMERICAL R ESULTS
In this section the tradeoff between HSDPA cell radius (site
density) and the number of 802.11g APs will first be quantified
through simulations using the models outlined above, which
are described more thoroughly in [3]. Then the elasticity of
infrastructure cost is derived for a few base line configurations.
A. Traffic density and HSDPA site distance
The macro cellular site density that minimizes cost with
single access deployment is not necessarily optimum with a
multi-access deployment. It depends on the traffic demand,
and how that varies over the total service area (as discussed in
[3]). If the macro BSs are deployed too sparsely, the remaining
areas that have to be covered with, in this case, 802.11g APs
may be too large with a resulting overcapacity per access point.
This tradeoff is illustrated in Figure 1, where the infrastructure
cost per GB and month is shown for an incumbent operator
with different cell radius in the macro cell layer and 90% of
the offered traffic supported. As expected the cost varies with
traffic density and deployed macro cell radius. The results are
summarized in Table II for 1, 10 and 50 times the traffic of
typical private voice telephony users (20mErl, 10kbps, see [3]).
At 10 x voice, in this example equal to a traffic density
of 10Mbps/km2 , an HSDPA cell radius of 800m yields the
lowest cost. In average six cells (two carriers in three sectors)
are used per HSDPA BS and there are approximately two
802.11g APs per km2 deployed in hot spots. The lowest cost
for another tenfold traffic increment is achieved with 400m
cell radius. The average number of cells per HSDPA BS
is approximately the same. However, the number of WLAN
APs is now 19/km2 . Notice also that the incremental cost
per transmitted GB flattens when the macro cellular network
becomes capacity limited which is due to poor coverage in
802.11g.
Results for Greenfield operators have the same shape and
the difference in infrastructure cost per GB and month is given
Table II. The cost advantage of incumbents vanishes as traffic
increase and 802.11g has to be deployed to a greater extent.
This highlights how important it is for incumbents to acquire
more spectrum to remain competitive if traffic surges in the
long run.
These results also explain how operators could exploit
WLAN in the short run instead of building a denser macro
network if traffic suddenly increases. In particular, considering
the relatively small sunk costs in WLAN, see Table I, this
could be economically justified during transition periods.
For example, increasing capacity from 5 to 10 Mbps/km2
with WLAN instead of deploying more macro sites increase
costs with less than 100% per GB calculated over 10 years
(comparing the results for 1000m and 800m HSDPA radii).
In the long run increasing capacity in the macro cell layer is,
however, more cost efficient as we will discuss further next.
B. Elasticity of infrastructure cost
Two reference systems adapted for approximately 10 and
50Mbps per km2 will be used as examples to analyze what key
parameters that would lower infrastructure costs the most. For
these systems, with 800m and 400m cell radius respectively,
the elasticity of infrastructure cost EC,X is plotted Figure 2
with the following variables changed (one per curve):
• decreased HSDPA cost coeffiecient,
1
HSDPA cost
HSDPA capacity
802.11g cost
802.11g coverage
0.5
Elasticity of Infrastructure Cost
Elasticity of Infrastructure Cost
1
0
0.5
1
HSDPA cost
HSDPA capacity
802.11g cost
802.11g coverage
0.5
0
0.5
1
Voice
0
10
10 x voice
1
10
Average Traffic Density [Mbps/km2]
100 x voice
Voice
2
10
(a) HSDPA cell radius 400m
0
10
10 x voice
1
10
Average Traffic Density [Mbps/km2]
100 x voice
2
10
(b) HSDPA cell radius 800m
Fig. 2. Elasticity of infrastructure cost for an HSDPA and 802.11g multi-access network with respect to different changes in design parameters. The reference
system is adapted for approximately 50 x voice traffic (left graph) and 10 x voice traffic (right graph). All but the changed variables are kept constant.
decreased 802.11g cost coeffiecient,
increased HSDPA BS capacity, and
• increased 802.11g AP coverage.
Elasticity of infrastructure cost with respect to the cost coefficients show how large share of the infrastructure cost
that stems from each system. With 400m cell radius each
subsystem stands for 50% of the cost at 90Mbps/km2 , while
the crossover occurs at around 15Mbps/km2 with 800m cell
radius. At 1Mbps/km2 only HSDPA base stations are deployed
and there is hence over-capacity in the macro cell layer. We can
also see that HSDPA capacity is more important to improve
than 802.11g coverage at all studied traffic densities with 400m
HSDPA cell radius. With more sparsely deployed HSDPA
sites, however, infrastructure cost is more elastic to 802.11g
AP coverage than HSDPA BS capacity for traffic densities
above 50Mbps/km2 .
Although not included in the graphs, simulations also
show that the cost is perfectly inelastic to the capacity for
802.11g up to approximately 200Mbps/km2 . Note, though,
that building operator deployed WLAN networks with full
coverage probably is not feasible considering the great number
of APs required so such a network is a bit hypothetical. Higher
traffic densities than, e.g., 50Mbps/km2 are not likely to be
implemented using the studied set of radio access technologies
only. Instead the results for higher traffic densities suggest
how cellular and WLAN like solutions should be improved
if traffic increase in the future. In particular, we can see how
important existing assets (i.e. previous deployment) of mobile
network operators and requirements on traffic density are for
the selection of radio access technology.
•
•
IV. P OTENTIAL IMPROVEMENTS OF CURRENT SYSTEMS
Following this analysis of key parameters to improve in
HSDPA and 802.11g if traffic increases we will discuss briefly
how such improvements could be materialized.
A. Capacity and coverage
To start with, capacity (aggregate throughput) per site is of
course a major issue for macro cells if traffic demand increases
significantly. In the long run this is perhaps easiest solved via
more spectrum bandwidth which, however, usually comes with
a considerable cost. Advanced transmitter and receivers techniques, like Multiple-Input-Multiple-Output (MIMO) systems,
are also promising. Naturally it is also beneficial to smoothen
out traffic load over time, e.g. using inter-temporal pricing
schemes or caching and pre-fetching solutions.
Merely increasing capacity per site is not sufficient though.
Also the cell range needs to be maintained to provide coverage
also for higher peak data rates. This requires an improved
link budget which, e.g., can be achieved through MIMO,
higher masts, frequency spectrum in lower bands, or multihop relaying. Above all the link budget is very critical if
broadband data services should be provided indoors using
outdoor BSs. This is illustrated in Figure 3, where we have
plotted the theoretical cell range in uplink for a conventional
WCDMA macro cell. A few typical services are depicted in
the graph according to standard link budgets presented in
[2]. Already at 144kbps the maximum cell range is in the
order 700m for indoor users. Hence, increasing peak data
rate to 1Mbps reduces the nominal cell range from almost
700m to approximately 350m since the link budget is linearly
proportional to the data rate (all else equal). To support even
higher rates in a macro cellular network the link budget
therefore needs to be improved.
B. Cost per access point
Continuing to the cost per AP, we have in Section III-B
considered a reduction of the total cost, including investments
and running costs. In Table I the main cost coefficients for the
studied systems were listed. The base value has been e300k
V. C ONCLUSIONS
2200
2000
1800
Cell range [m]
1600
Incar 12.2kbps
speech user
Outdoor 384kbps
non realtime data user
1400
1200
1000
Indoor 144kbps
realtime data user
800
600
COST231-Walfisch-Ikegami
COST231-Hata
400
200
120
125
130
135
140
Allowed path loss [dB]
145
150
Fig. 3. Uplink range as a function of allowed path loss for urban WCDMA
macro cells. A few typical services [2] are depicted in thegraph.
for a single carrier HSDPA BS with an additional e10k per
cell and e39k for 802.11g APs.
As discussed in [6], the cost structure for new macro
cells is today dominated by costs for site acquisition, buildout, installation and rental. In 802.11g however, ’last mile’transmission is a key contributor to the total infrastructure
cost (followed by site rental). This is based on an assumption
that a leased line is required per AP. Low-cost transmission
alternatives, e.g. wireless (meshed) networks, could reduce
OPEX to some extent. But it does not change the conclusion
that coverage per 802.11g AP needs to be improved (with
retained capacity) in order to lower the cost for operator
deployed WLAN solutions. An alternative method to provide
indoor coverage and capacity at a low cost could be to let users
install APs in their own premises that are open for access to
all the operator’s subscribers and roaming partners, e.g. using
a Network Franchise business model. This solution seems
promising in particular for operators with a strong position
also in fixed access.
C. Positioning of next generation radio access technologies
The results also points at how a future 4G radio interface
targeted for urban environments could be differentiated with
respect to current main stream technologies for wireless data
connectivity. Examples of niches not covered well by today’s
systems for urban deployment are
• high capacity micro cells, and
• long range WLAN access points.
In both cases we assume that the data rate at the cell border
is significantly higher than in 3G. Without making explicit
assumptions on required range and data rates, we can note
that concepts similar to the gaps indicated by this study already
have been proposed in different contexts; for example in the
research program WINNER (recently initiated by the EU) and
in similar initiatives. Numerical results on the infrastructure
cost for a different 4G concepts are presented in [3].
Multi-access networks are useful in order to lower infrastructure costs for operators in the long run if geographical
traffic density varies strongly. Also in the short run, it can be
beneficial as a temporary solution before improved macro cell
networks and more frequency spectrum are available.
As an example, we have looked in more detail into an
operator deployed network with macro cellular HSDPA base
stations and IEEE 802.11g access points. For this system
the total infrastructure cost was quantified for a city center
environment using a stochastic (log-normal) model for heterogeneous traffic density. It was shown that an HSDPA cell
radius between 400m and 800m minimize cost for average
traffic densities during busy hour of 10-50Mbps/km2 . This
approximately correspond to 10-50 times the traffic of private
voice users today. For higher traffic densities, either a very
dense macro cell layer, or a large amount of WLAN access
points are needed and this is probably not feasible.
We have also illustrated how elasticity of infrastructure cost
can be used to effectively analyze what design parameters
that are most important to improve in a multi-access wireless
network. In the example with HSDPA and 802.11 the capacity
per macro base station is more important to improve with
400m cell radius than 802.11g coverage up to 100Mbps/km2 .
However, if HSDPA base stations are more sparsely deployed
(800m radius) the same cost savings can be achieved through
increasing the range of 802.11g already at 50Mbps/km2 .
ACKNOWLEDGMENT
This work has partly been sponsored by the Swedish
Foundation for Strategic Research via the Affordable Wireless
Services and Infrastructure Project.
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