a review of cognitive femtocells for green cellular communications
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National Conference on Communications A Review of Cognitive Femtocells for Green Cellular Communications
Edwin Mugume
School of Electrical and Electronic Engineering
University of Manchester
Manchester, United Kingdom
edwin.mugume@gmail.com
Peterson Mwesiga
Department of Electrical and Computer Engineering
Makerere University
Kampala, Uganda
mwesigapeter@gmail.com
Abstract — The ICT industry contributes over 2% of the
world’s greenhouse gas emissions into the atmosphere and
this figure is expected to top 3% by 2020. Mobile cellular
systems are a major contributor to energy consumption
because of their emphasis on capacity and spectral
efficiency rather than energy efficiency. The long term
effects of CO2 emission into the atmosphere have led
service providers and members of the scientific community
to start taking the issue of carbon emissions seriously.
Soaring energy prices coupled with continued demand for
high rate applications means that operators spend a
significant amount of their revenue on energy. It is likely
that governments will implement climate change policies
that will force polluters to reduce their greenhouse gas
emissions such as carbon trading and energy taxation
policies. Femtocells have the potential to significantly
reduce the energy consumption of mobile cellular systems.
However, they may increase interference on the network
which compromises their intended benefits. This paper
discusses the potential of femtocells as a ‘green’ alternative
to the macrocell network. It will also investigate the
potential of spectrum sensing using cognitive radio to
avoid or reduce both homogeneous and heterogeneous
interference on the network. Finally, the potential of
femtocell networks as a locally relevant solution for mobile
operators in Uganda is discussed.
Key words - femtocell, macrocell, interference, spectrum
sensing, energy efficiency, green communications
I. INTRODUCTION
Mobile subscribers continue to demand for high data rate
services and this can be seen in the new cellular standards
being designed in the industry such as WiMAX, HSPA, LTE,
EV-DO, 3G and LTE all of which emphasize high data rates
and spectral efficiency. The challenge is therefore to design
more efficient networks to meet the required quality of service
(QoS). This has made indoor coverage solutions crucial for
operators the world over [1]. The most basic way of increasing
data rates and increase frequency reuse is to reduce the
separation distance between the transmitter and receiver. This
ensures a high signal to interference and noise ratio (SINR)
which results into a high channel capacity according to
Shannon‟s channel capacity theorem1.
In the cellular context, reducing the transmitter-receiver
separation distance basically means reducing cell sizes. Ever
since wireless communications began, data rates have
continued to rise every year and the major reason for this trend
has been reduced cell sizes. Indeed, over the last 50 years,
reducing cell sizes has been responsible for a million-fold
increase in channel capacity. Other major reasons include
designing better modulation and coding schemes and better
digital and analog signal processing techniques that allow
smaller slices of the spectrum to be used [1].
Macrocells cover a significant distance depending on whether
they are in rural, suburban or an urban setting. They give
sufficient outdoor coverage but due to penetration losses,
indoor coverage is generally poor. A typical macrocell has a
cell radius of 200-500m in an urban area. This means that they
provide insufficient signal strength levels to support very high
data rates. On the other hand, microcells have a smaller
footprint than macrocells and are intended for very busy areas
where data and voice traffic is very high. Picocells are even
smaller and are normally installed for indoor coverage
especially in shopping malls, hotels and other business
centers. All these base stations are very expensive to set up
because of the associated initial investment costs of
equipment, site lease and the subsequent maintenance costs.
One solution that enhances data rates without significantly
increasing costs is the new innovation called the femtocell.
II. FEMTOCELLS
Femtocells are low-power, low-cost and short range access
points (also called home base stations) that are installed in
homes or small office buildings to enhance indoor coverage
for both data and voice connections. They use existing DSL or
cable modem for the backhaul connection back to the
switching centre of the parent cellular network. A femtocell
access point (FAP) operates typically as a cellular network
radio base station found on the macrocell network. Femtocells
are simple plug-and-play user-installed devices which do not
1 Shannon’s theorem states that C = Blog10 (1+SINR) where C
is channel capacity and B is the channel bandwidth.
2
require the subscriber to have any technical competence. They
are installed in a totally ad hoc manner independent of any
input from the operator. This renders centralized frequency
planning by the operator inapplicable which results into a high
risk of interference [1].
However, due to the proximity between the transmitter and
receiver, link quality is normally very good. This results in a
high signal-to-noise ratio (SNR) that ensures high capacity. In
addition, mobile equipment battery life is maximized due to
low transmitted power on the uplink. The motivation for
femtocells stems partly from the fact that in the near future,
over 50% of all voice calls and at least 70% of data traffic will
originate indoors [1], [2]. Current femtocell designs support 2-
4 active subscribers in a home or 8-16 subscribers in an office
environment. It can be applied to all standards including GSM,
LTE, WiMAX and WCDMA. In 3GPP terminology, a 3G
femtocell is called a Home NodeB (HNB) while a LTE
femtocell is called a Home eNodeB (HeNB).
Currently, it is challenging and expensive for operators to
provide quality indoor coverage mainly due to indoor
penetration loss. 3G and LTE technologies suffer even more
because of operating at higher frequencies which means that
subscribers do not get quality indoor coverage to enjoy the full
capacity of such technologies. In order to provide reliable
indoor coverage, operators would have to increase the number
of macrocells and possibly add microcells and picocells to
patch up any dead spaces left. While this increases frequency
reuse and spectral efficiency, it sends operating costs soaring
because of the involved costs of site lease/acquisition, initial
investment in radio equipment, maintenance and running costs
like electricity, security etc. It is estimated that a typical urban
macrocell costs upwards of $1,000/month in such costs [1].
Femtocells present a cheap and reliable alternative as an
indoor coverage solution. A FAP typically costs around $100
and is normally subsidized by the operator. It consumes very
little power during transmit/receive and can be designed to
“sleep” during periods of inactivity, further increasing energy
savings. Femtocells relieve macrocells of indoor „mobile‟
users enabling the operator to concentrate resources on those
truly mobile outdoor users. It is also cost effective in the sense
that operators reduce on the number of macro/micro/picocells
required to provide the same quality of indoor coverage. It
reduces subscriber turnover (churn rate) because customers
have access to a high quality of service (QoS). Most
importantly, especially towards this paper, femtocells
consume little power by design which enhances energy
efficiency. Estimates show that by 2012, there will be around
150 million office or home femtocell customers being served
by over 70 million FAPs [1], [2].
However, as is normally the case, this technology faces
several challenges that can hamper its wide deployment. The
quality of the backhaul connection is important because it has
the potential to compromise the benefits of the FAP air
interface. The femtocell shares the broadband connection with
other data and this can potentially reduce the QoS. The
femtocell and parent network must be tightly time-
synchronized to avoid a large carrier offset, minimize multi-
access interference and to support handovers [1].
Handovers can be open access, closed access or hybrid. In
open access, any mobile on the same network is allowed to
handover to the femtocell as long as it satisfies the conditions
for handover such as relative received signal strength from the
macrocell and femtocell. While open access gives the best
spectral efficiency and network quality, it can lead to a high
ping-pong rate where handovers happen to and fro between
the macrocell and the femtocell for short periods. It also does
not guarantee access to the femtocell owner in case the all
femtocell capacity is currently taken up by other users. In
closed access, the owner configures the FAP to authorize
which mobile numbers can handover to the femtocell via some
kind of web application. In hybrid, some channels are fixed
and dedicated to the home users while the others are
dynamically used to serve both home and other users [1].
Perhaps, the major challenge facing femtocell deployment is
interference. Femtocells are typically installed in homes or
offices in a totally ad hoc manner without input from the
operator. Installing femtocells over an existing macrocell
network essentially creates two cellular network layers, the
underlaid macrocell and the overlaid femtocell layer.
Assuming that femtocells and macrocells use the same
frequency band, cross-layer interference can happen between
the two layers or co-layer interference can happen between
neighboring femtocells. Significant interference must be
avoided as it has deleterious effects on the performance and
realized QoS [1], [2].
Energy Efficiency in Cellular Systems
Mobile networks contribute a significant amount of the
greenhouse gas emissions attributed to the ICT industry. For
example, compared to digital TV broadcast networks, mobile
networks consume much more power. Due to the increasing
demand for high data rate services, operators are forced to
increase the density of base stations and this, coupled with
increasing energy prices, significantly increases the energy-
related costs without necessarily increasing revenue [10].
In areas where there is no grid power, operators have sought
more efficient ways of running their networks such as using
more efficient battery backup as opposed to using generators.
In the mobile network, over half of the power is consumed by
the radio access network (RAN) as shown in Fig. 1. Within the
RAN itself, the power amplifier, transceiver idling, power
supply system and the cooling system account for most of the
power consumption.
There is high research interest in the area of energy efficiency
because of the significant cost savings and increased revenues
that operators would realize. A lot of emphasis has been put
on designing network architectures that enhance more „green‟
communications as well as designing more intelligent radio
techniques that ensure that base station equipment and mobile
handsets consume less power. Noise and interference
management are very important in most wireless networks.
However, previous noise and interference cancellation
schemes emphasize high data rates and spectral efficiency as
opposed to energy efficiency. In order to realize green
communications, research efforts must be focused on those
schemes that prioritize energy efficiency while achieving
sufficient data rates and spectrum efficiency [10].
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Figure 1. Electricity usage in a mobile network [10]
Energy efficiency studies have also considered the impact that
cell size has on power consumption. With reduced cell size,
more base stations are required and this linearly increases the
power consumption. On the other hand however, power
consumed by the mobile equipment reduces. However,
simulation results show that a four-fold reduction of the cell
size can result into a sixteen-fold increase in energy efficiency
[11].
Femtocells are a more interesting technology to the green
communications community because they provide energy
savings both in hardware at the system level. A femtocell uses
a compact low-power access point and does not need any
cooling system. It also does not suffer cable loss because the
antenna and radio equipment are in one package. On the other
hand, conventional base stations must employ cooling systems
to cool the equipment because it heats up during operation.
Likewise, cable losses are inevitable because antennas are
normally placed in the tower or roof top and the radio
equipment are placed on the ground. A typical TVS 7/8” RF
feeder cable common with GSM networks in Uganda has
losses of about 3.88 dB and 5.75 dB for a cable length of
100m in the 900 MHz and 1800 MHz bands respectively [12].
On the system level, it is well known that transmitted power
reduces exponentially with distance according to the equation
d-
where is the path loss exponent that depends on the
propagation environment. In femtocells, the range is small
(10-20 metres) which reduces the path loss and thus reduces
the required transmitted power. In addition to increasing
battery life, it improves the SINR and leads to high channel
capacity. Conventional base stations must transmit high power
levels to overcome the high path loss involved in long range
propagation which increases their energy consumption.
It was shown in [13] that joint deployment of macrocells and
femtocells can result into energy savings of up to 60% in
urban areas using the technology available today. The
simulation results showed that operators with a large market
share benefit more from this joint deployment and that those
operators with smaller market can derive the same benefits
through RAN sharing agreements for their femtocells. The
paper also discussed and showed that with better equipment
components in future, energy efficiency will be further
improved. For example, the power amplifier consumes up to
60% of the energy required by a macrocell base station. New
devices are being designed that can reduce power
consumption of current power amplifiers by 50% while
maintaining the same performance. Future systems will also
see further energy savings on antenna systems, cables, etc.
Radio Access Techniques for improving energy efficiency
It is quite predictable that future systems will involve a dense
deployment of femtocells alongside the existing macrocell
network. Such a dense deployment means that the aggregate
power consumed by femtocells will become very significant.
Although subscribers meet all the energy costs of the
femtocell, their energy consumption must be reduced to
minimize greenhouse emissions. Two popular techniques
include the implementation of power control and sleep mode
procedures to further improve energy efficiency of femtocells.
Deployment of femtocells alongside macrocells leads to
femto-femto and femto-macrocell interference. In the cell
edge, power transmitted on the uplink is high and interferes
with other receivers. This deteriorates energy efficiency
especially due to high packet retransmission rates. Intelligent
power control schemes can be designed to reduce this
interference problem and enhance their performance such as in
[14].
Sleep mode procedures are intended to reduce the energy
consumption when the femtocell is idle. Sleep mode
procedures allow the femtocell to switch off its radio and
associated processing hardware in a manner similar to
schemes employed in ad hoc and sensor networks to improve
their battery longevity. In [15], a novel sleep mode procedure
is proposed and simulation shows that it results into
approximately 37.5% reduction in the power consumed by the
femtocell. In this technique, the femtocell is always off unless
there is a user that needs to connect to it. Thus, the femtocell
can employ an RF sensor to detect an increase in signal over a
given threshold.
Resource Allocation
The radio spectrum is very expensive for operators who may
not afford extra spectrum for their femtocells. Further,
spectrum is not readily available and most of the usable
spectrum has already been allocated in a fixed manner to other
services. Therefore, operators must intelligently allocate
spectrum to the femtocells so as to realize high spectral
efficiency and maintain QoS. One approach of spectrum
allocation is to divide the allocated spectrum into two bands
and use one band for macrocells and the other for femtocells.
While this orthogonal channel allocation approach eliminates
all cross-layer interference, it limits spectrum efficiency in
both bands and is clearly not desirable [2]. This paper will
mainly focus on those techniques of resource allocation that
are based on the concept of cognitive radio.
III. COGNITIVE RADIO
The traditional fixed spectrum allocation paradigm where a
fixed band is given exclusively to one licensee is very
inefficient. Extensive measurements carried out in major
urban centers around the world have shown that spectral
efficiency between 300MHz - 3GHz has a high spatiotemporal
variation but is generally poor. Most bands have spectral
occupancy less than 25% which shows that while spectrum is
as a scarce resource, it is also significantly underutilized [3].
This has led regulators and other industry players to consider a
4
different spectrum allocation paradigm where secondary (or
unlicensed) users (SUs) can access spectrum when the primary
(licensed or legacy) users (PUs) are absent. Whatever method
is used to allow SUs to access these spectrum holes (or white
spaces), the PUs must be protected from interference and they
retain legacy rights to the spectrum. Thus, a SU must release
the spectrum when a PU needs to use it. Cognitive radio (CR)
has been proposed as one way of detecting these white spaces
and it is a very attractive option because of its simplicity and
ease of implementation [4]. In [5], CR is defined as “a
context-aware intelligent radio capable of autonomous
reconfiguration by learning from and adapting to the
communication environment”.
CR senses the spectrum and decides whether a PU is present
or not based on a given criteria. There are several techniques
of spectrum sensing including cyclostationary feature
detection, waveform detection, matched filter and compressed
detection. This paper will consider energy detection because it
is cheap and easy to implement and has low computational
overhead. The energy detector is non-coherent as it does not
require any prior knowledge of the PU signal unlike the other
techniques which are all coherent. The CR measures the
energy on a given band and using simple binary hypothesis
testing and a given threshold, it declares a white space if
energy is below the threshold and vice versa [4], [6].
Energy detection has several weaknesses including receiver
uncertainty and its inability to differentiate between PU signal,
noise and interference. Receiver uncertainty is due to the
random nature of noise and the fact that the threshold is set
close to the noise floor. This leads to poor performance at low
SNR. Regardless of these weaknesses, it retains a high
potential for applicability in practical systems. Fig. 2 shows
the steps of energy detection sensing technique [4].
The detected signal at the CR is x(t), n(t) is zero-mean
AWGN, s(t) is the transmitted PU signal and h(t) is the
corresponding channel gain. A decision statistic M is obtained
from the measured signal and compared with the threshold λ. Using binary hypothesis testing, the CR either declares a white
space (H1) or not (H0) with the following probabilities of
detection Pd and false alarm Pf , thus [4]:
Pd = Pr{M > λ |H1} = Pr{decision = H1|H1}
Pf = Pr{M > λ |H0} = Pr{decision = H1|H0}
Pm = 1 - Pd = Pr{decision = H0|H1}
Pm is the probability of miss detection which results into
interference to the PU. Therefore, it is desirable to maximize
probability of detection which ensures that the PU is
maximally protected from SU interference. On the other hand,
probability of false alarm must be minimized because it
wrongly declares a white space as “occupied” thus reducing
throughput. However, both probabilities cannot be improved
simultaneously because they have a contradictory response
when varied with the threshold. Thus, false alarm rate is
normally fixed and the detection rate is maximized. It should
be noted that in practice, a high level of detection is required
even if it comes at the cost of poor false alarm. This is because
the PU must be protected at all times.
Figure 2. Energy detection procedure
A plot of Pd vs. Pf is the receiver operating characteristic
(ROC) of a CR while a plot of Pm vs. Pf is the complementary
ROC. These two plots are commonly used to show the
performance of spectrum sensing in CR. Fig. 3 shows a
complementary ROC of local sensing in a Rayleigh channel at
different average SNRs. Sensing performance improves with
average SNR as expected. Performance in an AWGN channel
at an average SNR of 10dB is shown for comparison.
Performance is much better in an AWGN channel because it is
essentially flat as opposed to a Rayleigh channel which is
characterized by a rapidly fluctuating signal due to multipath
fading and shadowing. The simulation was carried out using
the Monte Carlo simulation technique with 50,000 trials. The
theoretical simulation is also shown and it matches with the
simulated response.
Cooperative Spectrum Sensing
The performance of spectrum sensing is limited by the
deleterious effects of multipath fading and shadowing. The PU
channel may be in a deep fade or shadowed by an obstacle
which causes the CR to make a wrong decision. Shadowing
effects are generally manifested over longer distances and
time intervals than fading effects. Shadowing also causes the
well-known hidden node problem which affects system
performance. Cooperative (or collaborative) spectrum sensing
has been proposed to tackle the problems of shadowing and
fading. Several CRs cooperate by sharing their local sensing
information since it is unlikely that all CRs simultaneously
suffer from shadowing and fading. This cooperation provides
cooperative gain and by taking advantage of the spatial
diversity of collaborating CRs to improve the required
individual CR receiver sensitivity, solving the receiver
uncertainty problem and mitigating shadowing and multipath
effects [6].
10-3
10-2
10-1
100
10-3
10-2
10-1
100
Local Sensing Simulation using Energy Detection in Rayleigh Channel
False Alarm Probability, Pf
Mis
s D
ete
ction P
robability,
Pm
Simulated, SNR = 5dB
Analytical, SNR = 5dB
Simulated, SNR = 10dB
Analytical, SNR = 10dB
Simulated, SNR = 15dB
Analytical, SNR = 15dB
Analytical (AWGN), 10dB
Simulated (AWGN), 10dB
Figure 3. Complementary ROC showing local sensing performance in a
Rayleigh channel at different average SNR
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In centralized cooperative sensing, CRs send their locally
sensed information via a dedicated control channel to one CR
which acts as the fusion centre (FC). The FC determines
which channel should be sensed by the CRs and after
collecting the sensed information, it combines it and makes a
decision which it communicates to the CRs via the control
channel. The FC uses either soft or hard combining to process
the received information. In soft combining, CRs send all the
sensed information to the FC which increases the required
bandwidth of the control channel. In hard combining,
individual CRs make local decisions and send 1-bit (“1” for
H1 and “0” for H0) to the FC. This has the desired effect of
reducing the required control channel bandwidth [6].
The FC uses the linear fusion rules OR, AND or Majority rule
to reach a decision. In the OR rule, at least one CR must
declare H1 for the FC to declare H1. In AND rule, all
collaborating CRs must declare H1 for the FC to declare H1. In
the majority rule, at least half the collaborating CRs must
declare H1 for the FC to declare H1. The majority rule can be
expressed more generally as k-out-of-N rule where at least k of
the N collaborating CRs must declare H1 for the FC to declare
H1. For most matters of practical interest, the OR rule provides
the best performance as it minimizes the chance of causing
interference to the PU [7]. Fig. 4 shows improved detection
performance due to collaboration. It can be seen that sufficient
collaboration makes performance in a Rayleigh channel better
than local sensing in an AWGN channel. This is also a Monte
Carlo simulation with 50,000 trials.
Interference Management in Femtocell Networks
It has been discussed in Section I that femtocell deployment
creates a two-layer structure which creates resource allocation
challenges. OFDMA has been proposed as a solution to
manage the resulting homogeneous and heterogeneous
interference because of its inherent ability to use non-
contiguous channels. In OFDMA, the frequency band is
divided into orthogonal subcarriers that are in turn grouped
into subchannels. The challenge is then to allocate orthogonal
subchannels to users on the same or different layers in a bid to
avoid any of the two kinds of interference.
10-3
10-2
10-1
100
10-4
10-3
10-2
10-1
100
Cooperative Spectrum Sensing in Rayleigh Channel Using OR Rule
Cooperative Probability of False Alarm, Qf
Coopera
tive P
robabili
ty o
f M
iss D
ete
ction,
Qm
Analytical, N = 1
Simulated, N = 1
Analytical, N = 3
Simulated, N = 3
Analytical, N = 5
Analytical, N = 5
Analytical (AWGN)
Simulated (AWGN)
Figure 4. Cooperative sensing performance in a Rayleigh channel with
average SNR = 5dB for different number of collaborating CRs. AWGN case
is shown for comparison.
Although orthogonal channel allocation removes cross-layer
interference entirely, it is not preferred due to its spectral
inefficiency. Thus, co-channel assignment is preferred where
both femtocells and macrocells use the same band of
frequencies. Using OFDMA femtocells with cognitive radio
capabilities, interference can be minimized. The macrocell
users are the PUs and have legacy rights to the channel while
the femtocell users are SUs and must access the channel only
opportunistically.
In [8], a method based on fractional frequency reuse (FFR) is
proposed. The whole frequency band is divided into frequency
assignments (FAs) and adjacent cells are allocated different
FAs; each FA has several sub-channels. The femtocell senses
the uplink (UL) channel since it is likely that the PU will have
a higher power in this channel than the downlink (DL)
especially closer to cell edge. Both UL and DL transmissions
take place in the same FA for a particular cell. The cognitive
radio receiver senses the sub-channels and if a FA has some
occupied sub-channels, the femtocell leaves that FA in order
to protect PU DL and UL transmissions. If there is no free FA,
the femtocell uses the FA with the smallest interference
signature.
The authors in [9] propose that CRs should be self-
configurable and self-optimizing so as to easily fit into the
network without degrading performance. At start up, the
cognitive femtocell autonomously senses the channel and from
this knowledge of the environment, it arranges the sub-
channels in their order of increasing interference signature.
The femtocell then starts by allocating sub-channels with the
lowest interference signature. In Fig. 5, the red spots show
high interference channels while white spaces are sub-
channels which are free and can be used by SUs. The channels
that show intermediate levels of interference can also be used
when there are no free channels as long as they satisfy some
criteria.
Simulations on the DL of a typical urban macrocell show that
a significant number of reusable subchannels would be
available to the femtocell users. As expected, the available
channels increase with distance of separation between the
femtocell (fAP) and the macrocell base station (mBS). The
simulations were based on the assumption that all macro users
have a similar target SNR such that the mBS performs power
control on the downlink.
Figure 5: Subchannels in 2D space are sensed and arranged in order of increasing reuse priority
6
Fig. 6 shows a typical cell in an urban area with a cell radius
of 1km. The location of the fAP is random within the cell but
the femtocell users (fUsers) are fixed randomly within a radius
of 10 metres from the fAP. The mBS is fixed at the centre but
the macrocell users (mUsers) are also randomly located within
the cell. The indoor channel model considered for the fAP and
its users was the ITU-R P.1238 and the indoor penetration loss
was assumed to be 12dB. The simulation considers 64 users,
512 subcarriers and a bandwidth of 20 MHz. It was based on
the Monte Carlo simulation technique using 10,000 trials.
The simulation calculates the number of reusable subchannels
at a fixed location from the mBS, calculates the SINR in these
subchannels and computes the total capacity of the femtocell.
It is assumed that all reusable channels are allocated to four
femtocell users. Fig. 7 shows the variation of total femtocell
user capacity with distance on the DL considering the four
users. The threshold level is assumed to be -105dBm. The
threshold can be changed to obtain a desired detection
performance. Note that there is perfect orthogonality between
subcarriers and there is no interference from neighboring cells.
IV. CASE STUDY: UGANDA
In Uganda, operators are forced to run most of their sites on
generators and battery banks because of a lack of grid power
in most rural and sub-urban areas. Where the grid exists, it still
becomes expensive to connect some sites which may be
located far away from the grid lines. Also, grid power is not
reliable due to load shedding. Rural areas are characterized by
macrocells with a large footprint to provide wide coverage due
to sparse distribution of subscribers and low traffic potential.
This renders coverage poor in some areas especially indoors.
However, operators are faced with high costs of site lease
which, together with increasing diesel prices, ensure high
operating costs (OPEX). Also, the low incomes of most
subscribers mean that they are not willing to spend large
amounts of money on mobile services. With the stiff
competition that characterizes the mobile industry in Uganda,
this limits the profit margin of operators which in turn reduces
potential for further investment. However, subscribers demand
and expect a high network even indoors. Due to low revenues,
some operators have struggled to invest in their networks to
improve the quality. Many dead spots can be found in hotel
lobbies, conference halls, underground car parks, underground
office spaces, large shopping malls and arcades, etc.
-1000 -800 -600 -400 -200 0 200 400 600 800 1000-1000
-800
-600
-400
-200
0
200
400
600
800
1000Locations of mBS, mUsers, fAP and fUsers (Downlink Channel)
X Position (Meters)
Y P
ositio
n (
Mete
rs)
mBS
mUsers
fAP
fUsers
Figure 6: Typical urban cell used for the simulation
0 0.5 1 1.50
5
10
15
20
25
30
35
40
45
Variation of Total Downlink Capacity with Distance from mBS
(Cooperative Spectrum Sensing involving fAP and fUsers)
mBS-fAP Separation Distance [km]
Tota
l C
apacity o
f F
em
tocell
Users
[M
bps]
Figure 7: Variation of capacity of femtocell users with mBS-fAP separation
distance
In residential areas, many subscribers continue to have poor
coverage inside their homes. This clearly shows that macrocell
base stations are few and far between to provide quality indoor
coverage that the subscribers expect. Femtocells have the
potential to solve these issues without significant need for new
investment by the operators. They can be used to provide
focused high quality coverage in business centers and homes
with coverage gaps. The operator does not suffer any running
costs since the acquisition, operation and safety of the
femtocell is the sole responsibility of the subscriber.
Depending on the business plan, the operator can subsidize or
even offer the femtocells free of charge especially to home
users and business enterprises that spend significant amounts
of money on the operator‟s services. Femtocells reduce
subscriber turn over, help operators to maintain or improve
their market share and enhance the QoS experienced by their
subscribers. On the other hand, subscribers get access to a
high quality service and enjoy data and voice services that
give value for money.
Femtocells have the potential for high usage in Uganda
especially in urban and semi-urban areas. However, Uganda
may not be prepared for dense deployment of femtocells
because they DSL or cable modem for their backhaul. This is
because the fixed line network in Uganda is very sparse and
has few subscribers. The percentages of fixed line active users
in comparison to the total number of registered users are
approximately 78% and 65% for fixed copper-based and fixed
wireless respectively2. However, the proportion of registered
fixed line users is very small (with a growth rate estimated at
about 1%) compared to the number and growth rate of active
mobile subscribers. Not only do fixed lines provide a cheaper
service but they are also an alternative to mobile telephony
especially if the network is “down” or the coverage is poor
inside a home. Thus, a significant effort is required to expand
the fixed line network so as to take advantage of these
advantages as well as enhance the potential of future femtocell
deployments.
2 Obtained from Uganda Telecom Ltd
7
V. CONCLUSION
The complementary technologies of femtocell and cognitive
radio have a huge potential to revolutionize future mobile
communications around the world by improving spectrum
utilization and enhancing data rates beyond what is possible
today. Femtocells are already popular in North America and
there is huge interest developing in most of Europe. As the
technologies continue to evolve, researchers hope to tap into
the unique capabilities of cognitive radio to combat potential
interference in femtocell networks. Femtocells have a huge
potential to enhance the green communications effort. Green
communications has become an area of major interest in the
research community because of the need to reduce the total
contribution of communications to the total greenhouse gas
emissions into the atmosphere.
In Uganda‟s case, femtocells can go a long way in enhancing
indoor coverage that is insufficient in many homes, offices,
hotels, places of entertainment, etc. Such a solution will save
operators a lot of money that would otherwise have been used
to set up more macrocell and microcell base stations to
provide the required indoor coverage signal levels. However,
because femtocells use DSL or cable modems for backhaul,
only those premises that are connected with fixed lines can
benefit from this technology. Therefore, operators will target
hotels, malls and arcades, government and business offices,
and other such establishments.
Fixed lines must be encouraged as they are crucial for future
dense deployment of femtocells. Rural areas may not benefit
significantly from femtocells because of the backhaul issue.
The fixed line network is almost nonexistent in rural areas in
Uganda. Thus, there would be a backhaul problem to contend
with before any rural femtocell deployments can take place.
While a dedicated RF channel can also be used for backhaul, it
is undesirable because of the associated costs of transmission
antennas, installation, etc and the need for line of site (LOS) to
a nearby base station tower. In addition, the operator may have
to buy an extra microwave band for transmission which is
very expensive and may not be readily available. Since the
purpose is to enhance network coverage and quality without
significantly increasing costs of implementation, the DSL
backhaul option is the most desirable.
VI. FUTURE WORK
The huge interest in mobile telephony has greatly limited the
proliferation of fixed telephone lines in homes and offices in
Uganda. However, operators who decide to roll out femtocells
would mainly target home and office users. Therefore, there is
a need to ascertain exactly how many fixed lines (active and
inactive) exist in Uganda and the trend of subscription over
the last few years. It is also necessary to identify any
bottlenecks hampering the deployment and expansion of the
fixed network and to identify new opportunities and services
for fixed line subscribers. After such a study, it will be
beneficial to suggest suitable policy interventions that can be
implemented by the Government of Uganda, through Uganda
Communications Commission (UCC), in conjunction with
other major industry players such as operators, vendors,
researchers and customers.
REFERENCES
[1] V. Chandrasekhar, J. G. Andrews and A. Gatherer, Femtocell
Networks: A Survey, IEEE Communications Magazine,
46(9), September 2008, pp. 59-67.
[2] D. Lopez-Perez, A. Valcarce, G. de la Roche and J. Zhang,
OFDMA Femtocells: A Roadmap on Interference Avoidance,
IEEE Communications Magazine, 47(9), June 2009, pp. 41-
48.
[3] Shared Spectrum Company, Spectrum Reports [Online].
Available from:
http://www.sharedspectrum.com/papers/spectrum-reports/
[Accessed on15th April 2011]
[4] Tevfik Yucek and Huseyin Arslan, A survey of Spectrum
Sensing Algorithms for Cognitive Radio Applications, IEEE
Communications Surveys Tutorials, 11(1), 2009, pp. 116-130.
[5] Q. Zhao, A. Swami, A Survey of Dynamic Spectrum Access:
Signal Processing and Networking Perspectives, IEEE Int.
Conference on Acoustics, Speech and Signal Processing, Vol
4, 2007.
[6] Ian A. Akyildiz, Brandon. F. Lo, and R. Balakrishnan,
Cooperative Spectrum Sensing in Cognitive Radio Networks,
Physical Communication Journal, Volume 4, 2011, pp. 40-62.
[7] Khaled B. Letaief and Wei Zhang, Cooperative
Communication for Cognitive Radio Networks, Proc. IEEE
97(5), May 2009, pp. 878-893.
[8] D. Chan, H. C. Lee and Y. H. Lee, Cognitive Radio Based
Femtocell Resource Allocation, IEEE Int. Conference on
Information and Communication Technology Convergence
(ICTC), 2010, pp. 274 – 279.
[9] Y. Y. Li, M. Macuha, E. S. Sousa, T. Sato and M. Nanri,
Cognitive Interference Management in 3G Femtocells, IEEE
International Symposium on Personal, Indoor and Mobile
Radio Communications, September 2009, pp. 1118-1122.
[10] J. He, P. Loskot, T. O‟Farrell, V. Friderikos, S. Armour and J.
Thompson, Energy Efficient Architectures and Techniques
for Green Radio Access Networks, IEEE Int. Conference on
Communications and Networking in China, 2010, pp. 1-6.
[11] B. Badic, T. O‟Farrell, P. Loskot and J. He, Energy Efficient
Radio Access Architectures for Green Radio: Large versus
Small Cell Size Deployment, IEEE Vehicular Technology
Conference, 2009.
[12] TVS Interconnect Systems Ltd [Online]. Available at
http://www.tvsics.com/dataSheet/RFCABLES.pdf [Accessed
on 22nd June 2011]
[13] H. Claussen, L. T. Ho, and F. Pivit, Effects of Joint Macrocell
and Residential Deployment on the Network Energy
Efficiency, IEEE International Symposium on Personal,
Indoor and Mobile Radio Communications, 2008, pp.1-6.
[14] I. Guvenc, M. R. Jeong, F. Watanabe, H. Inamura, Femtocell
Channel Assignment and Power Control for Improved
Femtocell Coverage and Efficient Cell Search, US Patent
0291690 A1, November 2009.
[15] I. Ashraf, L. Ho and H. Claussen, Improving Energy
Efficiency of Femtocell Base Stations via User Activity
Detection, IEEE Wireless Communications and Networking
Conference, 2010, pp. 1-5.
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