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

[email protected]

Peterson Mwesiga

Department of Electrical and Computer Engineering

Makerere University

Kampala, Uganda

[email protected]

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.

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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

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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





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

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100

10-4

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10-1

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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

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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

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20

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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.

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A Review of Cognitive Femtocells for Green Cellular Communications

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