Dynamic frequency allocation in femtocells-based systems: algorithms and performance analysis

UNIVERSITA DI ROMA “TOR VERGATA”

Degree of Philosophy Doctor in Telecommunication and

Microelectronics

Dynamic frequency allocation infemtocells-based systems:

algorithms and performanceanalysis

by

Remo Pomposini

Supervisor Coordinator

Prof. Francesco Vatalaro Prof. Giuseppe Bianchi

Co-advisor

Prof. Franco Mazzenga

2011

Summary

Femtocells are small domestic low cost and low power cellular-based access points,

also known as Home Nodes B (HNB’s) or “home base stations”’, which are self-

installed by consumers and are remotely managed by operators. HNB’s transmit

with a range of tens of meters in licensed band (e.g., UMTS frequency bands), thus

avoiding the need for dual mode devices, and provide mobile handsets with high

data rate wireless access to the mobile operator network through broadband wired

connection, such as cable, xDSL or optical fiber.

The need for femtocells derives from the consideration that mobile terminals are

predominantly used within closed spaces [1]. Indeed, since most of the mobile radio

traffic is spent in the home and in workplaces, a better indoor coverage is wished

in order to increase the available bit-rate as well as to off-load macrocells [2],[3]. In

such a way, femtocells allow indoor mobile radio users to use advanced data ser-

vices, such as high quality video and audio streaming, downloads, on-line gaming

and other multimedia applications, with a higher efficiency in the use of spectrum

resources.

In the next future a multi-operator scenario is envisaged in which each network op-

erator makes available some portions of spectrum band for femtocells. In respect to

the frequencies allocated to the macrocell network, operators can assign dedicated,

common or partially common channels to femtocells [4]. Depending on the pursued

spectrum planning strategy, each operator has to face different interference scenar-

ios, referred to as cross-layer (i.e. macro-to-femto and femto-to-macro) and co-layer

(i.e. femto-to-femto) interference [5, 6, 7, 8, 9].

II

Even if a dedicated frequency band is allocated to the femtocell network to limit

cross-layer interference, harmful femto-to-femto interference levels can be generated

in a network scenario where a high density of Home Node B (HNB) sharing the same

frequency band is installed. This is due to the self-installation nature of femtocells

with no radio network planning by operators, that could lead to a random and not

coordinated deployment of HNB’s [10].

In this perspective, the Cognitive Radio (CR) concept [11] can be a possible solution

to reduce the mutual interference among femtocells. By introducing smart CR al-

gorithms accounting for different interference mitigation techniques, performance of

femtocells sharing the frequency band of their operator can be increased. However,

such algorithms can result very complicated to implement and can require a review

of the standard specifications to introduce signalling fields which permit to realize

them.

For these reasons, in this work I propose an alternative approach represented by

the opportunity for operators to share its licensed spectrum allowing users of fem-

tocells subscribed to a certain operator to exploit the frequency resources of other

operators. By assuming that operators make arrangements one with each other -

similar to roaming agreements - to allow the mutual exchange of frequency bands,

HNB’s belonging to different network operators could operate in a cognitive manner

in order to dinamically select the best operating frequency based on local interfer-

ence measurements. This solution allows to realize simple distributed algorithms

with minor revisions for the legacy femtocells, that can change operating frequency

considering also the band of other network operators. The proposed algorithms

are named Dynamic Frequency Selection (DFS) algorithms. In such a way, the in-

terference between neighboring femtocells belonging to the same operator can be

considerably reduced. In a multi-operator scenario, this approach offers advantages

to all operators in terms of achievable network capacity and quality of service pro-

vided to customers.

The aim is to evaluate the gain in terms of number of served femtocells per operator

when the channels available from operators increase, I refer to this parameter as the

III

Spectrum Sharing Gain.

In a realistic environment a hybrid scenario can be expected in which only some

HNB’s conform to the proposed DFS algorithms, while the rest of the femtocells are

unable to implement CR techniques.

For this reason in the second part of the work the performance in terms of out-

age probability and average Signal-to-Interference Ratio (SIR) when femtocells are

characterized by different behaviours are evaluated. In particular, the performance

gap with respect to the results obtained in the case in which all femtocells adopt

the proposed algorithms with reference to two situations are analyzed: in the first

one is considered that only a certain percentage of HNB’s implements the DFS algo-

rithms; in the second case, all the femtocells adopts the suggested DFS algorithms

but some of them only partially follow the rules, to the aim of maliciously exploiting

the frequency resources. These HNB’s, referred to as selfish femtocells, continue to

occupy the channel with dummy data even if their QoS is below the required level

in order to force the other DFS-conformed HNB’s to interrupt transmissions.

The work is organized in five chapters. Chapter 1 deals with basic notions con-

cerning femtocell systems. In particular, network architectures, services, standard

and security aspects are briefly discussed. Chapter 2 summarizes main mechanisms

regulating the cognitive radio concept. It aims at highlighting the main cognitive

radio characteristics, essential to understand the subsequent chapters which focus on

issues of cognitive femtocells. Chapter 3 describe the proposed cognitive algorithms

named ”Dynamc Frequency Selection Algorithms”. Along with the identification of

both the main limitation and the proposed solutions to improve femtocells perfor-

mance. A vast gamut of analytical, simulation and experimentation results, coming

from my research activity, are presented in Chapter 4. In Chapter 5 are analyzed

some security aspects concerning the proposed algorithms. Finally, conclusions are

drawn in Chapter 6.

My research activity during the PhD period focus on wireless systems, with par-

ticular interest on Dynamic Spectrum Allocation, Cognitive Radio and Femtocells

Systems. Most of the outcomes of my research activities have been collected and

IV

discussed in the following publications:

JOURNAL

1. A. Detti, P. Loreti, R. Pomposini, On the performance anomaly in WiMAX

networks, Wireless Communications & Mobile Computing, special issue on

Wireless Technologies Advances for Emergency and Rural Communications,

vol. 10, Issue 9, September 2010, pp. 1162-1172.

CONFERENCE & WORKSHOP

1. F. Mazzenga, M. Petracca, R. Pomposini, F. Vatalaro, R. Giuliano, Perfor-

mance Evaluation of Spectrum Sharing Algorithms in Single and Multi Opera-

tor Scenarios, in Proceedings of IEEE 73rd Vehicular Technology Conference:

VTC2011-Spring, 15-18 May 2011, Budapest, Hungary.

2. F. Mazzenga, M. Petracca, R. Pomposini, F. Vatalaro, R. Giuliano, Impact

of Control Channel Design on Cooperative Spectrum Sensing in Opportunis-

tic Spectrum Access Networks, First International Conference on Advances in

Cognitive Radio (COCORA 2011), 17-22 April 2011, Budapest, Hungary.

3. F. Mazzenga, M. Petracca, R. Pomposini, R. Giuliano, M. Vari, An Always

Available Control Channel for Cooperative Sensing in Cognitive Radio Net-

works, in Proceedings IFIP Wireless Days 2010, Venice, Italy, October 20-22

2010.

4. F. Mazzenga, M. Petracca, R. Pomposini, F. Vatalaro, R. Giuliano, Algo-

rithms for Dynamic Frequency Selection for Femto-cells of Different Operators,

21st IEEE International Symposium on Personal, Indoor and Mobile Radio

Communications (PIMRC 2010), September 26-29 2010, Istanbul, Turkey.

5. F. Mazzenga, M. Petracca, R. Pomposini, F. Vatalaro, Impact on QoS of

Femtocells Defecting from Dynamic Frequency Selection Algorithms, 21st In-

ternational Tyrrhenian Workshop on Digital Communications (ITWDC 2010):

Trustworthy Internet, September 2-8 2010, Ponza, Italy.

V

6. R. Giuliano, F. Mazzenga, M. Petracca, R. Pomposini, Wireless Opportunis-

tic Network Based on UWB for Preserving Environment, 19th IEEE Interna-

tional Workshops on Enabling Technologies: Infrastructures for Collaborative

Enterprises (WETICE 2010), June 28-30 2010, TEI of Larissa (Greece).

7. M. Petracca, R. Pomposini, F. Mazzenga, F. Vatalaro, ”Which Control

Channel for Cooperative Sensing in Cognitive Radio Networks?”, 1st Work-

shop of COST Action IC0902: Cognitive Radio and Networking for Coopera-

tive Coexistence of Heterogeneous Wireless Networks, November 23-25, 2010,

Bologna, Italy.

8. F. Ananasso, F. Vatalaro, A. Durantini, M. Petracca, R. Pomposini, The

femtocell: an international standard for a home base station, AGCOM techni-

cal report, April 2009, Egypt.

BOOK

1. Trustworthy Internet, Ed. Springer, F. Mazzenga, M. Petracca, R. Pom-

posini, F. Vatalaro, chapter editors of Chapter 9: Improving QoS of Femto-

cells in Multi-operator Environments. 1

1book in press

VI

Acknowledgements

Per non inserire la solita lista di ringraziamenti, ho deciso di riportare i nomi delle

persone che in qualsiasi modo mi hanno aiutato a raggiungere tale traguardo tramite

una nuvola, come quelle usate per i tag dei siti web o dei blog.

A differenza delle classiche ”tag cloud” dove le parole piu importanti sono a caratteri

piu grandi, in questa nuvola l’unico distinguo tra i nomi e nel colore, che li differenzia

per la provenienza delle persone, in ambito familiare e personale, quelle nere, e le

blu in ambito professionale.

Un grazie a tutti quanti!!!.

VIII

Contents

Summary II

Acknowledgements VIII

1 Femtocells: architectures, services and standards 1

1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.2 A new technology and market paradigm . . . . . . . . . . . . . . . . 3

1.2.1 Benefits expected from femtocells . . . . . . . . . . . . . . . . 3

1.2.2 Femtocells usage scenarios and applications . . . . . . . . . . 5

1.2.3 Market forecasts . . . . . . . . . . . . . . . . . . . . . . . . . 8

1.3 System architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

1.3.1 Femtocells and standardization . . . . . . . . . . . . . . . . . 9

1.3.2 Pre-standard proprietary architectures . . . . . . . . . . . . . 10

1.3.3 3GPP standard architecture . . . . . . . . . . . . . . . . . . . 13

1.4 Femtocell network management . . . . . . . . . . . . . . . . . . . . . 15

1.4.1 Remote control of femtocells . . . . . . . . . . . . . . . . . . . 15

1.4.2 Radio Resource Management . . . . . . . . . . . . . . . . . . 16

1.4.3 Mobility management . . . . . . . . . . . . . . . . . . . . . . . 21

1.5 Some open problems . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

1.6 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

2 Cognitive radio 28

2.1 Cognitive Cycle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

X

2.2 Cognitive Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

2.3 Cognitive Femtocells . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

2.4 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

3 Dynamic Frequency Allocation Algorithms 38

3.1 Problem statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

3.2 Regulatory aspects . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

3.3 Start-Up Procedure in Femtocells . . . . . . . . . . . . . . . . . . . . 42

3.4 Dynamic Frequency Selection Algorithms . . . . . . . . . . . . . . . . 44

3.4.1 DFS algorithm without Power Control mechanism . . . . . . . 44

3.5 DFS algorithm with Power Control mechanism . . . . . . . . . . . . . 47

3.6 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

4 Performance evaluation 50

4.1 Scenarios description . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

4.2 Performance results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

4.2.1 SIR in the regular grid topology . . . . . . . . . . . . . . . . . 53

4.2.2 Outage probability . . . . . . . . . . . . . . . . . . . . . . . . 56

4.2.3 Signal to Interference Ratio (SIR) . . . . . . . . . . . . . . . . 58

4.2.4 Spectrum Sharing Gain SSG . . . . . . . . . . . . . . . . . . . 61

4.3 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

5 DFS algorithm: robustness and resilience against malicious users 66

5.1 Scenario description . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

5.2 Performance analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

5.3 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

6 Conclusions 78

Bibliography 81

XI

List of Tables

1.1 Comparison femtocell market growth. . . . . . . . . . . . . . . . . . . 9

XII

List of Figures

1.1 Cellphone usage during weekdays at home, at work and on the move

(Source: NOKIA, 2006). . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.2 Femtocell development scenarios at home and in the office. . . . . . . 6

1.3 Possible development of femtocells in an outdoor environment. . . . . 7

1.4 Femtocell and WiFi market projections (Source: ABI Research, 2007). 8

1.5 Standard interfaces. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

1.6 Pre-standard femtocell architecture solutions. . . . . . . . . . . . . . 11

1.7 Standard 3GPP architecture. . . . . . . . . . . . . . . . . . . . . . . 14

1.8 Femtocell CWMP monitoring and control. . . . . . . . . . . . . . . . 16

1.9 Dynamic use of macrocell frequencies. . . . . . . . . . . . . . . . . . 17

1.10 Dead zones due to the interference between femto and macro layers. . 19

1.11 Relative mean value of the bit-rate: (a) outdoor area and (b) indoor

area (from [14]) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

1.12 Femtocells aggregator. . . . . . . . . . . . . . . . . . . . . . . . . . . 22

2.1 Environment sensing cognitive radio network. . . . . . . . . . . . . . 29

2.2 Overlay and Underlay spectrum access techniques in CR system. . . . 30

2.3 Cognitive Cycle proposed by Mitola. . . . . . . . . . . . . . . . . . . 31

2.4 User with different channel conditions within the coverage of the same

femtocell. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

3.1 Femtocells deployment in a small office environment. . . . . . . . . . 40

3.2 Femtocell start up procedure. . . . . . . . . . . . . . . . . . . . . . . 45

3.3 Flow chart of the DFS algorithm. . . . . . . . . . . . . . . . . . . . . 46

XIII

3.4 Flow chart of the ODFS algorithm with and without the power control

mechanism. The dashed blocks and lines are only referred to power

control. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

4.1 Interference scenario with HNB and user terminals (indicated with

dot) belonging to different operators. . . . . . . . . . . . . . . . . . . 51

4.2 Optimal frequency arrangement among femtocells in the regular grid

scenario with 2 network operators. . . . . . . . . . . . . . . . . . . . 54

4.3 SIR distribution for femtocells in different network topology without

power control mechanism. . . . . . . . . . . . . . . . . . . . . . . . . 55

4.4 Outage Probability with 2 frequency bands for Random (solid line)

and Perturbed Grid (dashed line) topologies. . . . . . . . . . . . . . . 56

4.5 Outage Probability with 3 frequency bands for Random (solid line)


4.6 Average SIR per femtocell with 2 frequencies for Random (solid line)


4.7 Average SIR per femtocell with 3 frequencies for Random (solid line)


4.8 Average SIR per femtocell with 2 frequencies for Regular Grid with

optimal frequency assignment (solid line), Regular Grid (dashed line)

and Perturbed Grid (dotted line) topologies. . . . . . . . . . . . . . . 60

4.9 Number of active femtocells per operator vs the number of available

frequency bands for Nf = 2. . . . . . . . . . . . . . . . . . . . . . . . 62

4.10 Spectrum Sharing Gain vs the number of operators with Nf = 2. . . . 63

4.11 PDF of the minimum reuse distance for Pout = 2%. . . . . . . . . . . 64

5.1 Building scenario for the interference analysis. HNB’s are indicated

with circle and each colour represents the assignment to a different

operator. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

5.2 Outage probability vs the percentage of non DFSA-conformed fem-

tocells in a low-medium density scenario. . . . . . . . . . . . . . . . . 70

XIV

5.3 Average SIR vs the percentage of non DFSA-conformed femtocells in

a low-medium density scenario. . . . . . . . . . . . . . . . . . . . . . 72

5.4 CDF for various percentage of femtocells that do not implemet the

DFSA algorithm. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

5.5 Outage probability vs the percentage of non DFSA-conformed fem-

tocells in a high density scenario. . . . . . . . . . . . . . . . . . . . . 74

5.6 Average SIR vs the percentage of non DFSA-conformed femtocells in

a high density scenario. . . . . . . . . . . . . . . . . . . . . . . . . . . 75

5.7 Bit Error Rate vs the average SNR on a Rayleigh fading channel for

different modulation schemes assuming a coding gain gc=10 dB. . . . 76

XV

Chapter 1

Femtocells: architectures, services

and standards

1.1 Introduction

The mobile radio service was designed to ensure total freedom from wires, to be

always connected anywhere with full mobility, especially outdoor, even away from

the fixed telecommunications infrastructure. But since long time we witness an

apparent paradox, on which all market analyses agree: the prevalence of the use

of mobile terminals within closed spaces. According to Northstream, the 57% of

minutes of mobile radio traffic is spent in the home and in workplaces in Western

Europe. According to Ovum from the 30 to 40% of mobile traffic originates in

the home. VisionGain foresees that by 2011 third generation (3G) traffic generated

within buildings will rise up to 75% of total traffic. In 2006, Nokia carried out an

investigation in the UK into the habits of the mobile terminal’s use by a selected users

group, finding the results summarized in Figure 1.1: over 24 hours the examined

customers sample limited within about 20-30% the phone’s use on the move. The

prevalent use of the mobile phone in indoors environment caused the gradual decline

of POTS telephony, initially in second homes and later in many principal homes too,

only partly counteracted by the requirements dictated by the use of PCs for Internet

1

1 – Femtocells: architectures, services and standards

Figure 1.1. Cellphone usage during weekdays at home, at work and on the move(Source: NOKIA, 2006).

access. Given for granted the reduction in revenues caused by VoIP (Voice-over-IP),

the operators rely, at least in part, their expectations of renewed growth of the

market for fixed telephony on the emergence of the so-called ”home networking”,

i.e. the interconnection of appliances in home networks and to the Internet. But,

despite expectations, the market for home networks evolve slowly and according to

still uncertain technological paths. The reasons for this delay are numerous:

• there are many standardization authority that operate independently to carry

out specific techniques that are not always interoperable (e.g. TISPAN/3GPP,

DSL Forum, UPnP Forum, HGI), which creates fragmentation and uncertainty

in the development of the market;

• some domestic appliances are already predisposed to connect to the Internet,

but most homes do not allow to exploit the potential due to the low penetration

both of PCs and of broadband access. Also internal wiring is inappropriate

for homes which are often old;

• most customers are not willing to face extensive domestic wiring, due to the

2

1.2 – A new technology and market paradigm

high cost of civil works, or even just the inconvenience that they entail.

Surely people prefer solutions that bypass the need to wire or rewire property, called

”no-new-wires” (power-line and wireless networks) and among them the new fem-

tocells technology, which is the subject of this article, is an interesting solution

to promote the market for home networking. Therefore it may prove suitable for

helping to build the so-called broadband ecosystem.

1.2 A new technology and market paradigm

1.2.1 Benefits expected from femtocells

The femtocell is a 2G or 3G small, low cost, self-installing radio gateway, and that

does not require maintenance. Designed to operate in licensed bands for use both in

residential areas and in SOHO (small office - home office) areas, the femtocell can

serve simultaneously a small number of terminals (less than 5, or so) and connects

to the network of a mobile operator through broadband lines (DSL, cable modem,

fiber optics).

In the mobile radio system’s evolution chain, the femtocell represents the latest

”ring”. In the past years, the technological path going from macrocells to picocells

was characterized by continuity. It combined progressive equipment miniaturization

with service, always provided by external radio towers, getting closer to the user.

On the contrary, by changing the functionalities distribution between entities, the

advent of femtocells coincides with a change of paradigm in wireless communica-

tions.

With femtocells, in fact, the capabilities of mobile radio network management move

close to the end user and are much more distributed than in traditional network

configurations. This is in line with the tendency of modern telecommunications sys-

tems that provide an increasing share of the ’intelligence’ move toward the network’s

border [12].

Many benefits are expected by the advent of femtocells. Among them:

3


• Use of standard terminals.

A first advantage for a mobile operators in the use of femtocells is the pos-

sibility to use standard mobile terminals, so that they can leverage the wide

dissemination of the terminal and VAS enabled.

• Ensuring QoS using licensed spectrum.

Contrary to what happens for WLAN systems, the use of licensed spectrum

may allow interference control for femtocells that is the premise for guaran-

teeing Quality of service (QoS).

• Better coverage and increased capacity.

Designed to provide good coverage in buildings without the need to increase

the number of expensive outdoor radio installations, another advantage of fem-

tocells lies in the ability to devote high-capacity radio to the customer only

if and when this is required. While the installation of a macrocell is expen-

sive and often faces considerable logistical difficulties, approaches to extending

coverage through femtocells allow to deliver radio capacity exactly where and

when it is needed.

• High bit rate values.

In a small indoor area the femtocell makes available the capacity of one base

station (BS) to a small number of potential users. Therefore, it supports

broadband traffic with values of bit rate always comparable to the peak values

expected from the standard

• Improved service offered by macrocells.

The transfer of substantial traffic to the indoor femtocell layer causes traffic

decongestion in the macrocell layer with the result that macrocells improve

the quality of service provided.

• Cost benefits.

For the operator aggregate traffic transport to the network (backhaul) is expen-

sive too. Allowing to reduce the backhaul cost, the femtocell reduces network

4


CAPEX and OPEX.

• Reduced customers churn.

The femtocell can increase customer loyalty by ensuring higher values of bit

rate and increased radio capacity, allowing the use of a single numbering system

for all applications and lower fares when calling from home.

Therefore, in the broadband access and home networking scenario the femtocell may

represent one of the key subsystems, to be associated with the home gateway with

which in the future it may also be integrated. Thanks to the femtocell the smooth

connection of the users to the home network can be ensured, thus avoiding the civil

works of internal wiring.

Today most manufacturers are producing femtocells interfaced with 3G (UMTS/HSPA).

Femtocells are also foreseen for other systems, such as WiMAX and LTE.

1.2.2 Femtocells usage scenarios and applications

Indoor coverage of a typical isolated femtocell is a few hundred square meters. Usu-

ally the cell radius varies between 30 m and 200 m depending on the propagation

characteristics in the femtocell’s environment. In principle, both isolated femtocell

coverage and multiple femtocell coverage can be implemented. The former is usu-

ally appropriate for residential use, while the latter, possibly integrated with a LAN,

is better suited in office and industrial areas (Figure 1.2). To date, however, the

major manufacturers are focusing on the product for domestic use. In the future

we can also expect femtocell use scenarios in outdoor and mixed indoor/outdoor

environments, with limited mobility conditions but in the presence of large volumes

of aggregated traffic (e.g., in malls, airports, railway stations, pedestrian areas) and

also when the end user requires high-capacity. Femtocell applications in outdoor

environments are still little investigated (although there is interest from some major

players, including Google), but we can already envisage their integration in RoF

(Radio over Fiber) architectures. These architectures are considered among the

options of interest to high capacity next generation radio communications also to

5


Figure 1.2. Femtocell development scenarios at home and in the office.

extend in urban areas the services of ultra broad band NGNs (next generation net-

works). One future scenario that is likely to be enabled by femtocells in dense urban

areas uses small remote units on lamp-posts and walls with backhaul over copper

pairs (DSL) or fiber optics. This may enable the operator an easier and economical

delivery of fixed and mobile services (Figure 1.3). So, the femtocell could stand out

as one of the key components of fixed-mobile convergence in the future.

There are three most promising application scenarios for the femtocell:

• Increased Quality of Service indoors.

This is the original motivation for the femtocell concept. The installation

of a femtocell in closed areas can provide the customer of one UMTS/HSPA

network data rates very close to the peak value in the immediate vicinity of the

femtocell site, so optimizing system capacity. Therefore, where DSL or fiber

optics accesses are already diffuse, femtocells can enhance the QoS provided

by the operator and can be an important enabler for the use of broadband

services on mobile terminals in indoor environments. With femtocells the QoS

6


Figure 1.3. Possible development of femtocells in an outdoor environment.

improvement offered to the customer at his residence can be obtained without

the need of dual-mode mobile phones.

• Increased traffic capacity through multiple coverage areas.

From this point of view, a femtocell network for use in large areas with a low

coverage degree is a substitute to the Distributed Antenna Systems (DAS)

technology. A DAS can extend the GSM or UMTS radio signal in shady areas

or in disadvantaged areas for one BS. Typical use cases may be represented by

shopping malls and large industrial buildings. One advantage of multiple fem-

tocell coverage areas, in addition to better coverage, is given by the increased

traffic capacity that can be permitted.

• Remote monitoring of the intelligent house.

In the future more and more household objects will be equipped with in-

tegrated computing and storage systems and wireless gateways that enable

communication with the outside world and their remote monitoring and con-

trol. The wiring of hundreds of household equipments, some of which also

7


arbitrarily relocatable, is not feasible and, therefore, the femtocell is the el-

ement that allows continuous monitoring without placing any constraints to

the user.

1.2.3 Market forecasts

Some analysts consider the femtocell market among the most important innovations

in mobile communications, although the projections are quite diverse. ABI Research

calculated about 52,000 units delivered in 2007, growing to about one million in 2008,

and 102 million users to be served at the end of 2011 by about 32 million access

points (Figure 1.4) [13]. If IDATE estimates about 10 million femtocells delivered in

2010 and 18 million in 2011, IDC forecasts are more conservative, reporting at least

5.7 million femtocell users of in 2011. Still with reference to 2011, OVUM envisages

17 million femtocells delivered in Western Europe only (Table 1.1).

Some mobile operators started investing in femtocells even before the 3GPP stan-

Figure 1.4. Femtocell and WiFi market projections (Source: ABI Research, 2007).

8

1.3 – System architecture

dard: e.g., the U.S. operator Sprint in September 2007 was the first to launch in

Denver and Indianapolis ”Sprint Airave”, a commercial service based on CDMA

femtocell from Samsung. In several Western countries, some operators planned to

launch commercial offerings based on specific agreements between operators and

vendors. However, the large economies of scale necessary for wide dissemination of

femtocells can be facilitated only now that a complete standard is available.

Femtocell (2011) Units (million) Customers (million)ABI RESEARCH 32 102IDATE 28IDC 5,7OVUM 38 (EU)

Table 1.1. Comparison femtocell market growth.

1.3 System architecture

1.3.1 Femtocells and standardization

An important point for the insertion of femtocells in the network of a mobile opera-

tor concerns standardization. In the tradition of GSM and UMTS mobile networks,

the complete standardization of radio interfaces, known as Um and Uu, respectively,

was essential to enable interoperability with terminals of different vendors. This

allowed achieving the necessary economies of scale in terminals production and cus-

tomers roaming. On the other hand, the need of standardization was not considered

important for the interfaces between the base station (BS) and the base station

controller (i.e. the Abis interface in the GSM and the Iub interface in the UMTS).

Therefore they exhibit proprietary features.

With the advent of femtocells as home base stations the need arises for a complete

standardization of radio interface elements towards the mobile operator network

(Figure 1.5). In order to be successful, the femtocell must be a consumer product

9


to ensure the high volumes of production typical of the market for terminals. Re-

cently, 3GPP completed the process of standardizing the network architecture and

protocols for 3G and for LTE .

Figure 1.5. Standard interfaces.

1.3.2 Pre-standard proprietary architectures

In the past years the industry identified several UMTS femtocell architecture con-

figurations (Figure 1.6). Though none of them was adopted as the standard they

include features which were useful for the standard definition. Every architecture

must provide for the implementation of security functions between the femtocell and

the femtocell aggregator placed on the network side, since the DSL backhaul is out

of the mobile operator’s domain and can include a path in the Internet. Therefore,

the interface must be encapsulated within an IPsec tunnel.

A first draft of architecture is based on the readaptation of the existing control el-

ements of the UMTS radio network, i.e. the RNC (radio network controller). This

is Architecture ”1” in Figure 6, named ”Iub − over − IP”.

10


Figure 1.6. Pre-standard femtocell architecture solutions.

According to this approach, the femtocell is connected throughout the Iub in-

terface, which is is intended to interconnect with the radio resource management

system, RNC. As is known, Iub is the 3GPP standard interface (TS 25 434) used

between the NodeB, i.e. the SRB in the UMTS system, and the RNC but, as al-

ready mentioned, is not full open, as it includes specific manufacturing features. For

this reasons, such architecture does not guarantee the interoperability of a femto-

cell network with different vendors products. In this architecture, the RNC focuses

different Iub flows deriving from different femtocells to the UMTS core network,

or CN (core network). Modern RNC are designed to accommodate at most a few

hundred of NodeBs high traffic load. For this reason the architecture does not scale

in scenarios with thousands of femtocells, or more. The changes required to imple-

ment access control and management reporting solutions in the RNC devices are

significant compared to standard solutions, and can require the re-interpretation of

the NAS (Non Access Stratum) protocols. Therefore, after the first proposal from

some manufacturing, this kind of solutions, based on the rehabilitation of the RNC,

were no more considered.

11


In Architecture ”2” the femtocell includes both the NodeB functions and the

RNC functions. Clearly, in this case the femtocell implements a different interface

solution, known as ”Iu − over − IP”, as is the UMTS Iu interface that is used.

Different integrated femtocell (NodeB + RNC) are connected to an aggregator,

which should be able to manage up to several thousand of them. The aggregator

is connected both with circuit-switched networks and with packet based networks,

through Iu − CS and Iu − PS standard interfaces, respectively. As is well known

Iu − CS is the standard interface between RNC and MSC, the mobile switching

center for services based on circuit switching, while Iu − PS is is teh interface

between RNC and SGSN, the switching center for packet based services.

The use of a truly open standard as Iu is an excellent basis for effective interop-

erability between aggregator and femtocell of different vendors.

One more alternative, represented in Figure 1.6 as Architecture ”3”, is based on

UMA (Unlicensed Mobile Access) architecture. Today, UMA is a 3GPP standard

to ensure interoperability between cellular/WiFi (TS 43318 v6.7.0 - R6, valid for

any IP access network), through an interworking entity called UNC (UMA Net-

work Controller), which allows dual-mode terminals, provided with an appropriate

client, to access to mobile network services and to perform vertical handover between

2G/3G and WiFi systems. Accprding to the UMA approach, an enabled terminal

search periodically if there is a WiFi gateway (or access point), when a compatible

one is found, it access to the network provider through an IPsec tunnel, established

between the UNC and the terminal. This tunnel ensure the communication secu-

rity, regardless of the security features offered by the technology used in the access

point. The UMA architectural approach for femtocells, is based on the use of an

Up interface toward a modified UNC. In this case, the UMA client functions are

situated in the femtocell instead of in the terminal: so any existing 2G/3G terminal

can connect with a UMA-enabled femtocell. When start-up, the femtocell use the

SIM/USIM standard with whom it is equipped and the EAP-SIM protocol to au-

thenticate within the mobile operator’s network and then to create an IPsec tunnel

12


between the femtocell and the UNC security gateway. Afterwards, the femtocell uses

the UMA standard procedures for the discovery of the appropriate UNC and then

to register with it. The UNC separates the voice traffic from the data traffic that

are delivered through the mobile operator circuit and packet networks, respectively.

As can be noted, in this architecture is completely missing the RNC block, whose

functions are integrated into the femtocell.

The last approach (Architecture ”4”) is based on IMS (IP Multimedia Subsys-

tem). IMS is the architecture designed to provide voice, video and multimedia

services across any kind of access networks. This standard is based on SIP (Session

Initiation Protocol) to manage the start-up, termination and modification in the

course of a multimedia over IP session. Therefore, Architecture ”4” requires femto-

cells enable SIP and connects with a gateway server that SIP IMS convergence.

1.3.3 3GPP standard architecture

As is evident, such a proliferation of architecture solutions would have inevitably

undermined any possibility of technology development and, therefore, the 3GPP

put in charge its RAN WG3 (Radio Access Network Working Group 3) with the

task of developing one single standard interface between the UMTS core network

and a femtocell. The reference configuration was approved in May 2008. Having

examined the four architecture proposals coming from the industry, RAN WG3

selected a compromise solution between Architectures ”2” and ”3” above.

The so-called 3GPP HNB Access Network (HNBAN), shown in Figure 1.7, includes

two new elements of a UMTS network, known as Home NodeB (HNB) and HNB

Gateway (HNB-GW), respectively:

• HNB (femtocell).

Connected to a broadband residential access service, this radio port provides

13


coverage to standard 3G terminals in the house. HNB includes NodeB func-

tions in addition to the radio resource management functionalities of a stan-

dard RNC.

• HNB-GW (femtocells aggregator).

Installed in the operator’s network, this gateway performs the task to aggre-

gate and disaggregate the traffic that comes from a large number of HNBs and

back to the CN by means of the two standard interfaces, Iu−CS and Iu−PS.

To interconnect these two network elements the standard defines the interface

Iu−h, which contains the protocols HNBAP (Home Node B Application Pro-

tocol) and RUA (RANAP User Adaptation). The first protocol is defined to

allow the creation of highly scalable HNBs, the second one provides transpar-

ent transport of RANAP (Radio Access Network Application Part) messages

and error handling functions. The new interface also introduces an efficient

and scalable method of transporting control signaling in the Internet.

Figure 1.7. Standard 3GPP architecture.

14

1.4 – Femtocell network management

1.4 Femtocell network management

The new femtocell paradigm requires rethinking all system aspects, such as those

relating to the management of operations and maintenance (O&M), in addition to

radio resource management (RRM) and mobility management (MM) aspects.

1.4.1 Remote control of femtocells

The approach to managing O&M is one of the innovative elements characterizing

the femtocell network over BS networks in traditional cellular systems. The femto-

cell, in fact, can be remotely controlled by means of mechanisms standardized in a

technical specification of the DSL Forum (TR-069). This specification, also known

as CWMP (CPE WAN Management Protocol), provides an application-level pro-

tocol for user equipment remote management (Figure 1.8). A dedicated software

installed in the O&M centralized system communicates with the home gateway to

which the femtocell is connected. It performs self-configuration, failure control, and

remote software upgrade functions.

The femtocell self-configuration functions include the choice of transmission pa-

rameters, mobility and access control parameters, and management of all cells to be

monitored. The transmission parameters of one femtocell cannot be fixed a priori

and manually configured by the operator, but must be chosen automatically during

the installation that is usually initiated by the customer. The customer simply con-

nects the femtocell to the DSL line and bears no other tasks or responsibility for its

proper functioning under normal conditions.

An UMTS femtocell may include a standard dedicated receiver, called ”macro-mode

receiver” (MMR), to receive the cell information broadcast by the overlay macro-

cells. In analogy to what happens for mobile terminals in sleep mode, the femtocell

is able to measure the level of signal and interference in surrounding cells. With this

feature, appropriate algorithms can be implemented for automatic power adjustment

15


Figure 1.8. Femtocell CWMP monitoring and control.

that can achieve the proper balance between minimizing co-channel interference gen-

erated by the femtocell on the overlay macrocellular network and the appropriate

coverage of the apartment. These algorithms also allow to control the interference

between adjacent cells in the case of high density femtocell installations.

1.4.2 Radio Resource Management

In a cellular system, Radio Resource Management (RRM) includes the control of

radio system characteristics and interference. As is known, strategies and algorithms

to monitor the transmission parameters are part of RRM, such as transmitted power,

choice of channels, handover criteria, and schemes for modulation and error control.

To understand the specificity of the RRM in a femtocell network we must take into

account two service features: on the one hand the user must enjoy the freedom to

place the HNB where he prefers in the house; on the other hand, the operator must

be also free to set different operating conditions for the femtocell. At operator’s

choice, the access to a HNB may be free, i.e. with no barring for any users who is

16


within range, or it can be restricted to a Closed Subscriber Group (CSG) that is, to

a group of customers registered to the femtocell [14]. Then, the operator has several

options for spectral planning of the femtocell network. In general, there are three

cases:

1. A dedicated frequency band can be assigned to femtocells

2. The same frequencies can be used for the femtocells that are allocated to the

macrocells network

3. A frequency band can be used in common between the femtocell layer and

the macrocell layer and, in case of interference, a macrocell user is required to

perform handover to a reserved frequency (Figure 1.9).

Figure 1.9. Dynamic use of macrocell frequencies.

The use of various scenarios that derive from combinations of the operational choices

have numerous implications for both the operator, in terms of network capacity, net-

work management and service management, and for the customer, in terms of quality

and functionality. In the following we discuss interference control, considered a crit-

ical aspect for the RRM of the femtocell system. While the UTRAN was originally

conceived and implemented on the criterion of the development of an ordered and

17


controlled network, the presence of a femtocell layer, which also contains millions of

small HNB not coordinated with each other, can generate harmful interference lev-

els. There are three types of interference: the ”femto-macro” interference produced

by the femtocell layer the macrocell layer, the ”macro-femto” interference in the

opposite direction and, finally, the ”femto-femto” interference, internal to the layer

of femtocells. Among these, at least in the first phase of the femtocell networks de-

velopment, the interference between layers has primary interest (i.e. ”femto-macro”

and ”macro-femto”). In WCDMA cellular networks, as is for UMTS/HSPA, the

”near-far” problem can occur that produces the connections interruption of users

away from the BS due to closest ones. As is well known, in the UTRAN in or-

der to compensate for the path attenuation and the slow and fast fading, which

determine the near-far phenomenon fast power control is used. When, however, a

femtocell layer adds to the existing macrocell network, the power control can create

”dead zones” (Figure 1.10) that determine non-uniform coverage. The phenomenon

is characterized differently in uplink (UL) and downlink (DL) [15]:

• ”macro-femto” interference and dead zone in UL. In the UL connec-

tion one macrocell user located at the cell edge and who, therefore, transmits

at maximum power causes unacceptable interference to close femtocells. Con-

sequently, the femtocells located at edge of coverage experience interference

significantly greater than that which occurs in femtocells placed inside the

macrocells. In other words, under this condition the aggressor is the macrocell

terminal and the victim is the femtocell terminal.

• ”femto-macro” interference and dead zone in DL. In the DL at the

cell edge macrocell users are affected by the HNB transmissions of nearby

femtocells, since they suffer from attenuation values greater than the users who

are inside the macrocells. In other words, under this condition the aggressor

is the HNB of the femtocell and the victim is the BS of the macrocell.

To contrast the interference that may arise between the two cell layers some solu-

tions, operationally simple but not always practical, can be assumed. The use of

18


Figure 1.10. Dead zones due to the interference between femto and macro layers.

different frequencies for the two layers eradicates the dead zone problem but it is

not a solution for UMTS operators with only one carrier. In general, it could force

the rescheduling of the macrocellular network, and can lead to possible capacity

reductions. For operators who have at least two UMTS bands, the dynamic use of

frequencies offers a solution with the transfer of an active communication within

the macrocell on the second carrier in the presence of interference arising inside the

femtocell (Figure 1.9). Finally, for the 2G/3G operators to reduce the dead zone in

the DL limitation of femtocell power can be done, jointly with the adoption of GSM

in place of UMTS in that area.

However, it is useful if each femtocell is able to monitor the RF environment, adapt

itself and change the transmission parameters (power, frequency, code, data rate) in

order to avoid, or at least contain, the interference to the macrocells and adjacent

femtocells [16]. To control the femto-femto interference, when a neighbor installs a

new femtocell, the other femtocells in the same building can be called upon to adapt

their transmission parameters. This may also be necessary to dynamically control

the interference. In principle, if two femtocells interfere, they can detect each other’s

presence and change transmission parameters at any time.

19


A study presented recently at the 3GPP [17] reported results of a simulation system

for UMTS/HSPA under the following conditions:

• HNB taken only with closed access (CSG),

• assumed the case of frequencies shared between HNB and macrocells,

• output power of HNB set at maximum value,

• the femto-macro interference in downlink to evaluate the impact on capacity.

The study examined the effect of interference on the P-CPICH (Primary Common

Pilot Channel). The results show (Fig. 11) that around the HNB significant inter-

ference problems may arise, where the macrocell’s power is small. The interference

could be reduced by limiting the HNB’s output power, but this also reduces the cov-

erage. The conclusion drawn is that in this scenario the HNB has to adopt adaptive

power techniques based on the interference estimate.

Figure 1.11. Relative mean value of the bit-rate: (a) outdoor area and (b) indoorarea (from [14]) .

20


1.4.3 Mobility management

The main objective of Mobility Management (MM) in a radio network is updat-

ing frequently enough the information on clients network location to ensure proper

delivery of services. Mobility is handled through the established procedures of cell

reselection and updating of location area (LA), with the possible option of national

roaming activated in case different PLMN identifiers are used for the macrocell net-

work layer and femtocell network layer. Indeed, the addition of femtocells within

a mobile operator network requires to ask whether it is more convenient to man-

age one stand-alone layer of femtocells or to integrate it with the existing layer of

macrocells.

In a mobile network the main codes used to facilitate the terminal’s localization

are three: the MNC (Mobile Network Code), which identifies the mobile network

(PLMN), the LAC (Location Area Code) which unambiguously identifies the loca-

tion area within the PLMN and, finally, the CID (Cell Id) that identifies the cell

within the LA. The BS periodically broadcasts such identification codes. The mobile

operator can assign to femtocells the same MNC identifier chosen for the macrocell

network. In this case to discriminate if a user is within macrocell or femtocell cov-

erage they must be assigned a unique set of LAC, to be managed by a separate

planning process. Alternatively, the operator can use for femtocells a different MNC

identifier and this allows to implement different service strategies, introducing an

additional degree of flexibility, potentially reducing the complexity at radio level.

In the case of high femtocell density, even with hundreds of femtocells within range

of a single macrocell, the network must be able to discriminate under what type of

coverage the customer is located. Because of the large number of active femtocells,

some mechanisms must be implemented to manage the increased complexity of the

network and report to the management of localization. Indeed identifiers LAC and

CID may not be sufficient to handle mobility in a high number of femtocelle, espe-

cially if you use a unique identification code of the network.

To allow the network to always know the area where you parked you use the upgrade

procedure of LA (Location Area Update). Each LAC corresponding to a femtocell

21


must be distinguished both from that, which is associated with the macrocells over-

lapped that of the LAC femtocelle adjacent. In general the system of remote control

of femtocell’s network plays the role of aggregator and manages the complexity asso-

ciated to the significant number of LAC and CID of femtocells for the core network

(Figure 1.12). However, since the potential number of femtocells in the network is

very large, it is necessary to implement the reuse distance of LAC codes.

In the exclusive access scenario described above the femtocell or the aggregator,

Figure 1.12. Femtocells aggregator.

depending on the particular solution, stores a list of clients authorized to access to

the femtocell, called white list, based on the user’s IMSI. When a location area up-

date is started, the network simultaneously requests the terminal to send its IMSI.

The femtocell or the aggregator intercept the identifier which is then cross checked

with all the IMSIs stored in the white list. Therefore, the location update function

within the femtocell succeeds only if there is compliance, in which case the user is

accepted.

In Release 8 of the 3GPP standard, the handover of ACTIVE UE from UTRA Home

NB to UTRA macro cell is considered [18] and is expected to be the same as the

procedure specified in [19]. Furthermore, if there is a technical need (e.g. to limit

excessive interference) the standard envisage the handover of ACTIVE UE from

22

1.5 – Some open problems

macro UTRA cell to UTRA CSG Home NB in coverage of UTRA Home NB. As

regards the handover between femtocells, in [18] is specified that the handover pro-

cedures between allowed CSG cells with the same or different CSG ID are expected

to be the same as the procedure specified in [19].

1.5 Some open problems

Being the 3GPP standard now available, and the vendors ready with first release of

products in a few months, in the 2009-2010 biennium the first commercial femtocell

installations will presumably begin by the large mobile operators in Europe.

Some minor technological problems still to be solved relate to aspects of the system

ranging from O & M subsystem to radio resource management, to management of

mobility. We recall some of them:

• Synchronization. Synchronization is the basis of good management of pro-

cedures both at RMM and MM levels. In particular, to meet the standard

on the spectral mask for the SRB accuracy on the frequency that they must

generate is typically 50 parts per billion (0.05 ppm) or better, according to the

specification 3GPP 25.104. To meet this specific even on long term should be

used in crystal oscillators stabilized or controlled temperature requiring exter-

nal calibrations to adjust periodically (eg once a month and once a year) the

frequency that is susceptible to drift because of crystal. In conventional BTS

for periodic calibration sometimes use the GPS but more often the timing is

extracted from the physical layer of the TDM transport. In the case of femto-

celle, in many cases may be insufficient to cover the internal GPS and an IP

network is employed, which has an asynchronous different structure from the

TDM network. An alternative calibration strategy is the employment of the

IEEE 1588, Precision Timing Protocol (PTP). According to a typical master-

slave structure, this solution envisage a master clock in the network providing

timing reference to the slave clocks at the femtocells [8]. In the 3GPP Release

6 standard has released the specification of accuracy the value of 0.1 ppm for

23


the BTS located in closed areas and, more aware of the difficulties for fem-

tocells, in release 8 is proposed to relax the frequency error requirement for

Home BS class to 0.25 ppm [20].

• Handover. In Release 8, the handover procedure from an UTRA macro cell

to an active HNB is not defined [18].

• Access control. It’s still considering how to operate the femtocell in relation

to incidents such as the access side by a near or equipment placed in domestic

surroundings, or, finally, users of macrocells that are moving in the vicinity.

• Quality of service and boundaries of responsibility between fixed and

mobile operator. In the tradition of radio systems, one for the GSM and

also for UMTS mobile phone network owned by the mobile is fully autonomous

and does not require to rely on fixed telecommunications networks: If this is the

transport capacity of flow is restricted. But when we use the DSL connection

of customer services for the backhaul, the HNB may have to compete in the

band with the other domestic users. In this scenario of sharing the problem

of managing the quality of service. The problem is especially important if the

network is different from fixed to mobile network. Moreover, in this case we

must also ask who is responsible for the management failures.

• Emergency Services. From 2010 will be mandatory for mobile services, the

provision of emergency that involves the need to locate the user. It should be

clarified whether and how it’s possible to locate the femtocell (and if it can be

repositioned at will). Moreover, like as in the case of VoIP telephony, including

the femtocell do not have the guarantee of continuity of power: then will have

to be clarified whether the emergency service can always be assured by the

mobile equip itself with a network of femtocelle and under what conditions.

• Dynamic spectrum management. As aforementioned, the HNB can be

installed (and reinstalled as many times as you wish) by the user in domes-

tic premises without planning, differently from current mobile networks. This

24

1.6 – Chapter Summary

random distribution of HNBs can cause interference that is difficult to elimi-

nate in some configurations.

The following two possible scenarios are envisaged:

1. The Femtocells (dummy) and the Macro BS belong to the same network

operator and are considered at the same hierarchical level. So, when the

macrocell users perceive an excessive interference level caused by femto-

cells, the operator can employ spectrum

2. The Femtocells (CRs) and the Macro BS belong to the different network

operators and are not considered at the same hierarchical level. In this

case, the femtocells are seen as secondary nodes that can transmit and

receive according to constraints on interference level to the primary net-

work nodes, that are the MacroBSs. For this purpose, the HNBs have to

be endowed with Cognitive Radios features. In particular, to dynamically

manage the spectrum in an opportunistic way, the femtocells, in presence

of primary users, should implement some basic functions, which the most

important are: ”spectrum sensing”, i.e. the continuous spectrum mon-

itoring and detection of unused portions; ”spectrum management”, i.e.

the best dynamic usage of the available spectrum; ”mobility spectrum”,

i.e. the timely release of spectrum when a primary user become active.

In the second scenario, the MAC in the femtocells system will enable the

reallocation of frequencies that suffer from interference, for example due to the

presence of macrocellular radio base stations in the close proximity, possibly

even with an exchange of spectrum between different operators.

1.6 Chapter Summary

Femtocell is a new technology with the potential to achieve a paradigm shift in

radio networking. The femtocell, now a 3GPP standard, has been designed to give

solution to the problem of radio coverage in difficult spaces to high frequencies (2

25


GHz and beyond) where services are allocated to mobile broadband. In addition to

the coverage advantage, then, a femtocell network offers the advantage, over clas-

sical macrocell networks, that the improved radio capacity which can be obtained

in any environment, plus the ability to provide access to capacity in peak many

circumstances in which a network of macrocells can not go beyond the offer of an

average capacity of traffic. Thanks to the introduction of femtocells a traffic capac-

ity increase could be achieved that some estimate up to 500 times greater than that

achievable with traditional UMTS coverage.

But besides all of this, as seen in the chapter, there are a lot of benefits that now

generate a widespread expectation that the femtocell could potentially become an

enabler of development of the ecosystem of the home broadband, offering a gateway

to many devices without the constraints of the wired connection. The femtocell

lends itself to being a key enabler of full integration of home and SOHO environ-

ments in telecommunications networks, a major market driver for the promotion of

the broadband ecosystem so that it could be the ”missing link” to acceleration the

’evolution of intelligent technology.

The awareness of the multiple potential of this technology pushed 3GPP to complete

the standardization process in one year, or so. Since beginning of April 2009, with

3GPP UMTS Release 8 the femtocell technology is now an international standard.

As we saw in the article, with femtocell the capabilities of mobile and radio’s net-

work management, moving close to the end user are much more distributed than

in traditional network configurations. This is in line with the tendency of modern

telecommunications systems that provide an increasing proportion of intelligence to

move towards the periphery of the network. But if the femtocell technology opens

new opportunities in the development of 3G and LTE and it is also integrated in the

development of the NGN technological path, it also pose new challenges in relation

to intrinsic highly distributed nature of functionality. Some problems require careful

consideration and the solutions seem not yet fully identified. Some of these were

considered at the conclusion of the article.

26


This work is focus on the problem of interference in a femtocells network, partic-

ularly the femto-to-femto interference. In order to mitigate such interference, the

concept of cognitive radio is applied to femtocells. More in details, two dynamic fre-

quency selection algorithms that permit to the generic femtocell the smart selection

of its operating band are proposed and analyzed by simulation.

27

Chapter 2

Cognitive radio

The Cognitive Radio (CR) [21] concept was introduced by Joseph Mitola III in 1999

[22] to indicate the concept of a wireless system which is aware of the environment

around it and of its internal structure. A CR can exploit its old experiences and its

learning ability in order to improve the standard adaptation capability (i.e. link-

adaptation based on the estimation of the channel), or for spectrum use information

in order to select the Base Station (BS), Access Points (AP) or more in general the

network nodes that are more appropriate. A CR device can even learn from the

information exchanged by the user with the aim to adapt the power consumption

and the network searching techniques to the user behaviour.

In general, a CR device operates according to two primary objectives:

1. high communication reliability;

2. efficient use of spectrum.

Mitola defines the CR as the point in which the wireless Personal Digital Assistants

(PDAs) and the networks are enough intelligent, in terms of spectrum management

and machine-to-machine communications, in order to:

1. identify the users communication needs;

28

2. provide the spectrum resource and services necessary to satisfy that needs.

On this basis, a CR can automatically select the best service for a specific trans-

mission and it can dynamically manage several transmissions based on the available

resources.

The Mitola’s definition of CR is vast, therefore a so defined device is indicated as

a “Full Cognitive Radio”. Recently the term CR is adopted with a more precise

definition. In this respect, the FCC (Federal Communication Commission) states

that every radio device able to adapt itself to the available spectrum should be ref-

erenced as a “Cognitive Radio” [23].

A CR is a device that perceives the environment around it, it collects data, it obtains

information from the collected data, it identify strategies to be adopted from the

information collected and it converts strategies into actions, as depicted in Figure

2.1.

Since cognitive radios are considered lower priority or secondary users of the spec-

Figure 2.1. Environment sensing cognitive radio network.

29

2 – Cognitive radio

trum allocated to primary users, it is necessary that these cognitive users do not

create interference for potential primary users. Different solutions can be used to

underlay, overlay or interweave the secondary user signals with the primary user

signals, in such a way that the primary signals are as little influenced as possible by

the secondary signals.

In the case of spectrum overlay, Figure 2.2 (a), a primary user receives an exclu-

sive right to spectrum access. However, at a particular time or frequency, if the

spectrum is not utilized by a primary user, it can be opportunistically accessed by

a secondary user. Therefore, to access a spectrum band, a secondary user has to

perform spectrum sensing to detect the activity of a primary user in that band. If a

spectrum hole is found, a secondary user may access the spectrum. The decision of

a secondary user whether to access the spectrum or not depends on constraints such

as the collision probability, which is defined as the probability that the transmission

from a secondary user occurs at the same time as that from a primary user.

In the underlay approach, Figure 2.2 (b), the secondary users spread their signal

Figure 2.2. Overlay and Underlay spectrum access techniques in CR system.

over a large bandwidth, minimizing the amount of interference caused to the pri-

mary users.

In the interweave approach, a cognitive radio must be capable of sensing the air

30

2.1 – Cognitive Cycle

interface and opportunistically exploit the unused spectrum by the primary users.

Since underlay techniques are more suitable for spread spectrum technologies, e.g.

Ultra Wide Band (UWB), and because it is difficult for the secondary users to obtain

the a priori knowledge of the primary user signals in order to perform an overlay

access, interweave techniques are attracting most of the attention in cellular network

environments.

2.1 Cognitive Cycle

The above described capabilities of a CR device can be explained using the ”Cog-

nition Cycle” [22], as depicted in Figure 2.3.

The ”Cognition Cycle” is the set of states, actions and interactions that the CR

Figure 2.3. Cognitive Cycle proposed by Mitola.

device makes in order to understand and know the outside world with the aim of

change the status in according to the received directions and incentives.

The first state of the cycle is named ”Observe” in which the CR device observes

31


the environment around it. Clearly, the sensors and data derived from them, will

represent the environment through appropriate observable parameters in order to

be easily processed by the device.

The extracted data are pre-processed in the Orient state in order to take a decision

on the urgency of action to be performed. There are three urgency’s categories: im-

mediate, urgent and normal, which indicates the maximum time that can be waited

before performing the action. The level immediate indicates that there is no time to

perform a strategy and should be immediately taken a decision, so CR node have to

perform an action already performed in the past. The Act state will act to raise the

event. The urgent level indicates that there is no time to perform a new strategy

but it’s possible to choose a note strategy. So the next steps are: in the Decide

status the device decides the strategy to adopt and then it enter in the Act status.

Finally, the level normal provides that the device should also look at new strategies,

beyond those already known, through the state Plan. Afterwards, the device goes

into Decide mode in order to decide the strategy and then in Act state to perform

it.

The output of the states Observe, Plan and Decide, together with the data derived

from the external environment, are processed in the Learn state during which the

CR device learns from its actions. It should be noted that neither the Orient or Act

states provides input to the learning state, so there is no knowledge of the reaction

to the stimuli that occurs in those states. Alternatively, it is also possible that the

reactions to stimuli are included in the observed data in the Observe state, including

them in the Priority Status and New State blocks that indicate the observed status

before and after the action. In this case the Plan and Decide status have no longer

need to send information to the Learn state.

An important aspect that should be taken into account is the feasibility of a CR

devices. In particular, the requirements of each described phase are not trivial:

• the phase Observe is crucial for the CR device, as it allows to know the en-

vironment. A detection error at this stage may lead the device to act wrong

and even harmful for itself;

32

2.2 – Cognitive Networks

• the phase Orient plans to identify the priorities among the different stimuli

from the surrounding environment and the adaptive scheduling of the different

stimuli;

• the step Plan allows the device to determine the different strategies to use, so

it is required that there be a complete knowledge of the internal state and the

consequences of each strategy;

• in the Decide phase it is important that the device select the action to perform

taking into account the user preferences and habits as well as past experience;

• during the Act phase, the device must be able to reconfigure its internal state

in accordance with the adopted strategy;

• in the Learn phase the device must be able to store, identify and classify the

different situations (in order to reuse them in the future) taking into account

the user behavior and the obtained results. Moreover, the device must be able

to adapt the different phases of the cycle according to the past experiences.

2.2 Cognitive Networks

At the beginning the concept of Cognitive Radio was applied only to the user device,

i.e. the mobile terminal. Now, within the research and standardization offices, the

CR concept is extended to all the network devices. There is an increasing interest on

the Cognitive Radio Systems (CRS), i.e. radio systems based on cognitive concept,

both terminals and network.

An important application of the CRS are the Cognitive Networks (CN), where the

concept of CN is exyended to the netwotk domain. Therefore, a CN is a network

able to adapt its behaviour according to the environment cognition.

A CN is a network characterized by the following two entities and features:

• Cognitive Network Management;

• reconfigurable Base Stations.

33


The Cognitive Network Management functionalities concerns the Radio Access Tech-

nologies (RAT), it manage and control the network nodes in order to adapt them

to the optimal configuration for the radio resources exploitation. This feature act

on the base of some input information, such as the available resources, the traffic

demands, the terminal characteristics in each cell (supported RAT, frequency bands,

etc.) and required services from each user (bandwidth, QoS, etc.). Moreover, this

feature may adopt a cooperative radio resource management scheme, where decision

functions are shared between the network nodes.

The reconfigurable Base Stations are the nodes that physically set up the CN. The

main feature of these nodes is to have hardware and computing resources that can

be reconfigured dynamically, in order to be used with other RATs, different frequen-

cies, different channels, etc., they can also operate in multi-RAT mode, based on a

dynamic load management.

An interesting aspect of the CN is the possibility of introducing the so-called ”Cog-

nition Radio Enablers”, i.e. devices that support the observing process of the ter-

minals, in order to know the radio spectrum, such as the Cognitive Pilot Channel.

In conclusion, the availability of reconfigurable radio Base Stations, together with

the Cognitive Network Management capabilities can give to the operators an addi-

tional management tool for radio resources and for processed resources, in order to

achieve a greater efficiency in their use.

2.3 Cognitive Femtocells

Cellular telephone companies are continually under pressure to provide new services

and devices to their users and as result they are using up increasing amounts of

scarce bandwidth.

For network operators is not simple to supply new radio spectrum, for this rea-

son are necessary new technical solutions that provide more effective use and reuse

of bandwidth. Some new technologies like LTE cell standards using MIMO show

significant promise by improving spectrum efficiency. However the viability of this

34

2.3 – Cognitive Femtocells

4G technology to meet the demand over the long term is also questionable. The

infrastructure costs will be even larger than 3G as LTE base stations will need to be

deployed every 500 meters or less and must be capable of supporting multi-megabyte

downloads to video Smartphones at these distances. Alternatives, such as limiting

user capacities, will not make for happy customers nor address the demand issue.

Faced with such concerns cellular systems designers have been discussing other tech-

nologies that offer alternative bandwidth distribution approaches. Martin Cooper,

one of the pioneers of cellular noted that bandwidth use could be multiplied by up

to 1600 times provided that radio cells became sufficiently small.

Femtocells can be used to create such a scenario. Moreover, their ability to support

efficient MIMO technology would be a better bet. There are drawbacks however.

With Femtocells, as their density increases so does the potential for radio interfer-

ence, Figure 2.4.

One solution to these problems could be to use Cognitive Radio technology in

Femtocells.

In a similar way as in the interweave approach, described above, femtocells must be

able to search the radio channel and estimate which resources are free among the

available ones in order to avoid cross-layer and co-layer interference.

Cognitive radio techniques can be implemented in the femtocell device, but they

must not be implemented into femtocell user terminals due to legacy constraints.

Femtocells must operate using legacy mobile terminals that do not depend on new

user equipment.

Despite having different methods of learning about the air interface, what informa-

tion should be used and how it should be combined is still an open issue. Moreover,

different trade-off has to be taken into account. As an example, measurement re-

ports coming from the user terminals will provide accurate information about the

user environment at the expense of raising the overhead information and processing

time.

Another important problem that can be arise with the diffusion of these devices is

the increasing amount of interference.

35


Figure 2.4. User with different channel conditions within the coverage of the samefemtocell.

It is envisioned that Cognitive Femtocells could be installed at the user’s premises

and would interface with the DSL and cable links that already form an internet dis-

tribution infrastructure within cities. How this infrastructure is to be used and what

demands will be made on it by Cognitive wireless systems is both a regulatory and

technical concern requiring careful study and the drafting of new standards. The

internet was not designed to meet the mobility requirements of cellular applications.

The use of Cognitive radio technology will affect our thinking about how we license

spectrum. For example, should we continue to sell spectrum if Cognitive Radio

technology allows us to rent it? The technology could enable incumbent owners to

sub-license their spectrum and retain revenue doing this. Should there be a method

36


of penalizing Femtocells that create excessive interference and deny access to adja-

cent users? Also what roles do independent wireless services providers have in these

models? In Europe for example, some wireless service providers have no spectrum

licenses but are providing services based on license-exempt spectrum alone.

2.4 Chapter Summary

Cognitive radio is viewed as a novel approach for improving the utilization of a

precious resource: the radio spectrum.

The cognitive radio, is defined as an intelligent wireless communication system that

is aware of its environment and uses the methodology of understanding- by-building

to learn from the environment and adapt to statistical variations in the input stimuli,

with two primary objectives in mind:

• high communication reliability;

• efficient use of spectrum.

The immediate interest to regulators in fielding cognitive radios is to provide new

capabilities that support new methods and mechanisms for spectrum access and

utilization now under consideration by international spectrum regulatory bodies.

Considering that for network operators is not simple to supply new radio spectrum

and that cellular telephone companies are continually under pressure to provide new

services and devices to their users and as result they are using up increasing amounts

of scarce bandwidth. The adoption of Cognitive Radio technology in Femtocells can

be a solution to these problems.

Unfortunately, with the diffusion of femtocells we have to face the problem of in-

terference, that can be arise between femtocells. This problem can be addressed

adopting appropriate cognitive algorithms.

37

Chapter 3

Dynamic Frequency Allocation

Algorithms

In this chapter are described the devised algorithms for dinamically selecting fre-

quency based on local interference measurements.

Two different approaches for femtocells that differ in the preference or not to use

the frequency band of their operators are proposed. In the first case, each femtocell

takes into account its subscription to the specific operator and attempts to use its

own band until the Signal-to-Interference Ratio (SIR) is above the required thresh-

old ρ0. In the second case, the femtocells are absolutely greedy and aim to maximize

their SIR careless of which operator offers the less interfered channel. It means that

also if the femtocell measures a SIR enough to guarantee the desired QoS level on its

frequency, it searches for another band that can maximize the throughput. The two

algorithms are named Greedy Dynamic Frequency Selection (GDFS) algorithm and

Operator-oriented Dynamic Frequency Selection (ODFS) algorithm, respectively.

As aforementioned, in the analysis are considered both the scenario where all the

femtocells transmit at the maximum power and the scenario in which the power con-

trol mechanisms are implemented. In general, according to the spectrum planning

an operator could allocate one or more frequency channels to femtocells. Here it is

38

3.1 – Problem statement

assumed that each operator provides its femtocells with a maximum of two dedi-

cated shared channels. The proposed algorithms can be easily extended to the case

in which the number of channels per operator assigned to femtocells is increased.

In the following sections we focus on the motivation that lead us to introduce the pro-

posed algorithms for cognitive femtocells, i.e. the problem statement, after which we

detail how the two algorithms work for both maximum power and power-controlled

transmissions.

3.1 Problem statement

The “home base stations”, known as femtocells, are the cellular-based access points.

They give the possibility to connect standard mobile devices to the network of a

mobile operator through a broadband wired connection (e.g. ADSL, cable broad-

band connections, optical fibres) or dedicated wireless point-to-point link [5].

Femtocells generally transmit in licensed bands (e.g. those for UMTS), thus avoid-

ing the usage of dual mode devices. In the next future a multi-operator scenario is

envisaged in which each network operator makes available some portions of spec-

trum band for femtocell installations. In respect to the frequencies allocated to the

macrocell network, operators can assign dedicated, common or partially common

channels to femtocells [24]. Depending on the applied spectrum planning strategy,

each operator has to face different interference scenarios, characterized by cross-

layer (i.e. macro-to-femto and femto-to-macro) and co-layer (i.e. femto-to-femto)

interference [6], [9].

In a first phase for femtocell deployment in an area, the mobile operator can assign

two dedicated bands taken from its licensed spectrum depending on the required ser-

vices by subscribers. In this way it is possible to avoid cross-layer interference and

to reduce the co-layer interference among femtocells. Nevertheless, to limit the cost

and time of frequency planning phase and maintaining the self-installation nature of

femtocells, a non-coordinated deployment of femtocells can be favoured [10]. Unfor-

tunately, when subscriber installations of femtocells become more dense, the mutual

39

3 – Dynamic Frequency Allocation Algorithms

interference could be harmful for offered services, limiting the femtocell capacity, as

visible in Figure 3.1 where is depicted a possible future office scenario. The use of

Cognitive Radio (CR) techniques is a viable solution to solve the interference prob-

lem by implementing and installing on femto Home Node B (HNB) complicated CR

algorithms possibly requiring a deep revision of the standard specification. As an

Figure 3.1. Femtocells deployment in a small office environment.

example the implementation of CR may require the introduction of signalling fields

which allow interference measurements that are required to enable CR procedures.

For this reason, simple but effective distributed algorithms aimed to dynamically

redistribute the available spectrum belonging to different network operators among

femtocells just based on local interference measurements are proposed.

The algorithms are based on the assumption that operators share their licensed

spectrum allowing users of femtocells subscribed to a certain operator to exploit

the frequency resources of other operators. Each femtocell is able to select one of

the possible channels available from all the operators in order to experience the

40

3.2 – Regulatory aspects

best Signal-to-Interference Ratio (SIR). This approach realizes a simple distributed

algorithm requiring minor revisions for the legacy femtocells.

3.2 Regulatory aspects

The proposed solution to limit co-tier interference in femtocell networks is based

on the assumption that network operators make arrangements one with each other

(similar to roaming agreements) to allow the reciprocal exchange of operating fre-

quency channels. However, while the infrastructure sharing among telecom service

providers is a mandatory policy by the European Commission (EC) [25], the si-

multaneous mutual interchange of spectrum bands among network operators is not

currently permitted. Nevertheless, the guidelines proposed by some regulatory bod-

ies open interesting perspectives in this regard.

The proliferation of wireless services and devices for uses such as mobile communica-

tions, public safety, WiFi, and TV broadcast serve as the most indisputable example

of how much modern society has become dependent on radio spectrum. While land

and energy constituted the most precious wealth creation resource during the agri-

cultural and industrial eras, respectively, the radio spectrum has become the most

valuable resource of the modern era [26].

Access to the radio spectrum is a key requirement for continuous wireless growth

and deployment of new mobile services. Given the fast-growing demand for radio

spectrum, regulators around the world (e.g., the Federal Communications Commis-

sion, FCC) are analyzing the way the spectrum is currently used and, if appropriate,

make recommendations on how to improve radio resource usage.

In particular they are implementing much more flexible and liberal forms of spec-

trum management, often referred to as dynamic spectrum management. This new

model dynamically redistributes and reassigns spectrum within and across different

wireless systems, adapting spectrum usage to actual demands and achieving much

more efficient use of the precious spectrum resource. Within the new model, two

prominent approaches are being considered by the regulators: spectrum trading and

41


cognitive spectrum access.

Spectrum trading is a market-based approach for spectrum redistribution that en-

ables a spectrum license holder (for example, a cellular operator) to sell or lease all

or a portion of its spectrum to a third party [27].

Note that this is an important departure from the command and control manage-

ment model, where spectrum licenses are granted by regulators for the provision of

a specific service using a predefined technology, and license holders were not allowed

to reallocate their spectrum to different technologies or other users.

Even the policy programme for the use of the European Union’s radio spectrum

foresees that spectrum should be managed on the basis of principles including spec-

trum efficiency and flexibility, technology and service neutrality and competition.

In addition, collective use of spectrum and spectrum trading would be promoted,

also encouraging convergence of authorization conditions and procedures for bands

tradable across Europe [28].

Moreover, the Radio Spectrum Policy Programme (RSPP) encourages the develop-

ment of standards able to avoid harmful interference or disturbance by other radio or

non-radio devices by means of efficient spectrum usage techniques, especially when

high density of radio devices occurs [29].

In this perspective, the demonstration of the benefits deriving from the sharing of

licensed frequency bands among operators can contribute to review the communi-

cations regulatory framework.

3.3 Start-Up Procedure in Femtocells

After acquiring the femtocell, the customer only needs to plug the femtocell into

a power source and Internet connection to start using it. The customer cannot be

assumed to have the knowledge to install or configure the femtocell, hence these

processes need to be automatic. Therefore, after power on, femtocells begin the

start-up procedure as described in [30].

This procedure envisages that when powered on the femtocell connects to the own

42

3.3 – Start-Up Procedure in Femtocells

operator network through the backhaul connection to carry out operations such as

registration, authentication, software updating.

In this phase, femtocells have also to set their RF parameters to a default config-

uration. This should be done with the information provided by operators through

the backhaul link, such as:

• frequency for DL and UL,

• scrambling code list, or

• radio channel bandwidth,

• location, routing and service area code information,

• neighbouring list,

• physical cell ID,

• RF parameters (pilot and maximum data power . . .)

The last four parameters can be be automatically calculated by femtocell from in-

formation on the macrocell layer provided by the operator (OSS data), and from

information on the femtocell layer provided by the users (registration data).

However, when the core network can not support this configuration phase, in partic-

ular for the first parameters of the list, femtocells can perform an auto-configuration

procedure based on sensing of the radio environment.

In the core network assisted configuration, HNB receive from the operator the in-

formation about the possible radio frequencies to be used for data transmission and

transmit it on the HNB control channel to the Mobile Terminals (MTs). Then, in

order to determine the operative frequency, the MTs perform power measurements

on the indicated radio channels and report results to the HNB.

In the second case, due to the lack of assistance from the operator via backhaul con-

nection, femtocells execute the network configuration by self-setting RF parameters

based on monitoring of the radio channels. Hence, the information needed for initial

43


configuration are transmitted on the less interfered frequency.

Once the operative frequency is determined according to one of the described start-

up procedures, femtocells enter into the self-optimization phase [30], since the femto-

cell needs dynamically to adapt its parameters to the changing environment condi-

tions. Using the network listening mode and other inputs, e.g. broadcast messages,

measurement reports, cognitive radio (Section citesec:CognitiveFemtocells) the fem-

tocell will collect statistics to optimize its performance dynamically (coverage and

capacity).

The proposed Dynamic Frequency Selection algorithms refer to this ”loop” phase of

the femtocell cycle. In particular, if the interference conditions cause a QoS degra-

dation on the operator channel (i.e. SIR ≤ ρ0), femtocell can select the frequency

band of another operator for data transmission.

However, in this case a problem of femtocell identification can arise when MTs be-

longing to the Closed Subscriber Group (CSG) of the HNB are temporarily not

linked to the femtocell (e.g. a mobile user coming back home with his terminal in

idle-state). As a result MTs can not recognize the presence of femtocell and remain

on the macrocell network without exploiting the better indoor coverage provided by

their HNB. For this reason HNBs should periodically transmit the control informa-

tion containing its physical cell ID on the beacon frequency of the operator. This

can be done either by temporarily interrupting transmissions on the new selected

channel to switch to the original frequency or by endowing HNB with an additional

dedicated beacon transmitter, as proposed in [31].

3.4 Dynamic Frequency Selection Algorithms

3.4.1 DFS algorithm without Power Control mechanism

In a multi-operator scenario with N network operators, the flow chart for modeling

the ODFS algorithm by femtocells transmitting at the maximum power level is re-

ported in Figure 3.3.

44

3.4 – Dynamic Frequency Selection Algorithms

Figure 3.2. Femtocell start up procedure.

Once femtocells are deployed in the subscriber premises and after the start-up pro-

cedure, they search for the less interfered channel (or bandwidth) among those

activated by its mobile operator. In particular, they measure SIR on all the Nf

available operating frequencies, expressed as:

SIRfop =Cfi

ITOTfi

(3.1)

where Cfi is the received power at the i-th frequency band (1 ≤ i ≤ Nf) and ITOTfi

is

the total amount of interference power on that channel. Then femtocells select the

frequency corresponding to the highest experienced SIR for their MTs. Note that

in our analysis the measured SIR is related to the downlink transmission. It means

that each MT uses the pilot signal received from its HNB to calculate the initial SIR.

Since MT can not measure the signal power level on the other available frequency

bands, it can just report to its HNB the interference power ITOTfi

sensed on such

channels. However, as the proposed algorithms consider SIRmeasurements to select

the operating frequency, a correction factor βλ can be introduced accounting for the

signal propagation at different bands. By assuming that the path loss exponent is

the same on the considered channels, with respect to the current operating frequency

45


fi the SIR on the j-th channel can be calculated as:

SIRfj = SIRfi · αi · βλ (3.2)

where βλ = (λi/λj)2 is the propagation correction factor and αi = ITOT

fi/ITOT

fjis the

ratio between the interference power levels measured on the considered frequency

bands. In (3.2) λi and λj are the wavelenghts of the i-th and j-th channels, respec-

tively. Then femtocell determines which channel maximizes the throughput of the

MT based on the received measurement reports.

Since in a realistic scenario multiple operators are present in the area (Mop oper-

SIRfop

< ρ0start

SIRfi = max{

SIRf1,SIRf2 ,...,SIRfMop·Nf

}

SIRfi

≥ ρ0

Turn OFF(sensingmode)

select fc = fi end

yes

no

yes

no

Figure 3.3. Flow chart of the DFS algorithm.

ators in Figure 3.3), femtocells periodically measure SIR not only on their mobile

operator frequency bands but also on the channels of other operators, i.e. SIRfi

with 1 ≤ i ≤ Nf ×Mop (we assume that all the mobile operators allocate an equal

number Nf of channels). Femtocell selects one of the channels of its operator until

the SIRfop level is above the required threshold ρ0. It means that femtocells con-

tinue to transmit on their own operator frequency bands even when the channels of

46

3.5 – DFS algorithm with Power Control mechanism

other mobile operators are less interfered (i.e. they provide a higher SIR). Only

if the SIRfop becomes less than ρ0, the femtocell is enabled to select the frequency

band of another operator. In particular, each femtocell selects the channel that

maximize its SIR.

However, if also the SIR on the selected channel does not match the QoS require-

ments (i.e. SIRfi < ρ0 ∀i), the femtocell sets its status to sensing mode, i.e. it

just continues to perform spectrum sensing and interference measurements with-

out accessing the channels. Sensing mode operation can be envisaged in order not

to damage the transmission of other femtocells: since the unrecoverable interfer-

ence conditions prevent the femtocell from transmitting, it temporarily disables its

transmission and switches into sensing mode. Since each HNB accounts for its sub-

scription to the specific operator, femtocells attempt to use its own spectrum band

until the measured SIRfop is above the required threshold ρ0. It means that during

the sensing mode period, femtocells first and foremost schedule their channels for

interference measurements. This is also valid when a channel of another operator is

selected and femtocells periodically check other frequency bands.

The GDFS algorithm can be seen as a special case of the ODFS algorithm, since

the only difference is that each femtocell immediately selects the channel that maxi-

mizes its SIR regardless of which operator has license for that frequency band. This

means that the first conditional block is skipped in Figure 3.3.

The described cognitive algorithm is very simple and easy to implement. As high-

lighted by the flow chart in Figure 3.3, with respect to the ODFS, the GDFS algo-

rithm allows the femtocell to maximize SIR.

3.5 DFS algorithm with Power Control mecha-

nism

The behaviour of femtocells implementing the ODFS algorithm with power-controlled

transmissions is depicted in the flow chart of Figure 3.4, considering all the blocks

47


and lines. The algorithm differs from the previous one in the following aspects.

Given the SIR on the frequency band of the home operator the single femtocell

adjusts its transmission power in order to reach the target quality level ρ0. The

transmission power is decreased if the SIR is greater than ρ0 otherwise it is in-

creased up to the maximum transmission power level, Pmax, if necessary to reach

ρ0. If the target ρ0 is reached the algorithm stops.

Otherwise, the femtocell searches for the operating band providing the greatest

SIR and it tries to adjust its transmission power following a procedure similar to

that used for the home operator (see previous steps). Finally, if the transmission

power required to reach ρ0 is greater than Pmax than the femtocell switches into

sensing mode, since it can not reach quality. Otherwise, the femtocell selects the

new operating frequency belonging to one of the other operators.

3.6 Chapter Summary

The self-installation nature of femtocells sharing the same frequency band can lead

to harmful femto-to-femto interference levels.

The possibility for operators to share its licensed spectrum allows femtocells of one

operator to exploit the frequency resources of other operators.

In this chapter is described the devised algorithms for dinamically selecting fre-

quency, among those available from every operator, based on local interference mea-

surements. Two different approaches for femtocells that differ in the preference or

not to use the frequency band of their operators are proposed.

With the Greedy Dynamic Frequency Selection (GDFS) algorithm femtocells aim

to maximize their SIR careless of which operator offers the less interfered channel.

Adopting the Operator-oriented Dynamic Frequency Selection (ODFS) algorithm,

the femtocell takes into account its subscription to the specific operator and at-

tempts to use its own band until the Signal-to-Interference Ratio (SIR) is above the

required threshold ρ0.

48


SIRop

< ρ0start Decrease Tx Power

Increase Tx power

Ptx ≤Pmax

SIRi = max {SIRf1,SIRf2 ,...,SIRfN}

SIRi

> ρ0

Increase Tx power

Decrease Tx Power

Ptx ≤Pmax

select fc = fi

Turn OFF (sensing mode) end

yes

no

yes

no

yes

yes

no

no

Figure 3.4. Flow chart of the ODFS algorithm with and without the power controlmechanism. The dashed blocks and lines are only referred to power control.

49

Chapter 4

Performance evaluation

4.1 Scenarios description

In order to evaluate the performance of the proposed algorithms, three different

network topologies are considered, obtained as:

• random positioning of femtocells in the area;

• positioning of femtocells over a regular grid;

• positioning of femtocells over a regular grid with random 2D displacement

around their original point of the grid.

The last topology, referred to as perturbed grid, is a trade-off between the other ones

and it can represent a typical case of femtocells installed within adjacent apartments.

The interference scenario for the random network topology is shown in Figure 4.1.

A multi-operator scenario where femtocells are randomly distributed in an area of

100 × 100m2 is considered, in accordance to an uniform spatial distribution. This

also reflects a typical case of HNB’s randomly positioned by subscribers in a resi-

dential area. User terminals are assumed to be located at the femtocell border.

HNB’s can or can not implement power control mechanisms. In the second case,

femtocells are sources transmitting at the maximum power level set according to the

50

4.1 – Scenarios description

Figure 4.1. Interference scenario with HNB and user terminals (indicated withdot) belonging to different operators.

standard specification, i.e. Ptx = Pmax = 20 dBm [32]. This assumption is aimed

to evaluate an operating boundary condition in which power control algorithms are

not adopted. To take into account for the distance between HNBs and subscriber

terminals (namely r), the indoor segment and the outdoor segment are considered

separated and different propagation models depending on the type of link are used.

The ITU-R P.1238 model [32] is assumed for pathloss. It is expressed as:

LIN (r)[dB] = L50(r) + LFM + LW (4.1)

51

4 – Performance evaluation

where LFM is the additional shadow fade margin and LW is the penetration loss

due to the outer wall of the building. From [32], LFM = 7.7 dB and LW = 0 dB,

since we are only interested in indoor propagation. L50(r) is the median path loss

at a distance r assumed equal to 10 m (femtocell border), i.e. the loss exceeded at

50 % of positions at that distance, given by the following expression:

L50(r)[dB] = 20 · Log10fc + 10γ · Log10r + Lf(nf )− 28 (4.2)

In equation (4.2) fc is the operating frequency, γ is the indoor path loss exponent

and Lf(nf ) is the floor penetration loss, which varies with the number of pene-

trated floors nf . As recommended by the ITU-R [32], both γ and Lf(nf ) depend

on the frequency and the environment. We assume that femtocells are in residen-

tial environment with nf = 0 (ground level apartments) and operate at frequencies

fc around 1800 MHz, with a channel spacing between different network operators

∆fc = 10 MHz. Based on this assumption, from [32] we set γ = 2.8 and Lf(nf ) = 0.

For the outdoor attenuation model, the following expression for path loss is consid-

ered:

LOUT (d)[dB] = MCL[dB] + 10γout · Log10

(

d

d0

)

+ 2 · LW [dB] (4.3)

where d > d0 is the distance in m from the considered femtocell, MCL[dB] > 0 is

the minimum coupling loss in decibel for d0 = 1 m, γout is the outdoor path loss

exponent assumed to be equal to 3.2. For sake of simplicity we considered just two

external walls crossed by the interfering signal and we set LW = 10 dB.

4.2 Performance results

Performance are evaluated in terms of outage probability and average SIR, which

is directly related to the maximum achievable throughput. When the received SIR

52

4.2 – Performance results

from a subscriber terminal (downlink case) experiences a value lower than the re-

quired threshold, ρ0, an outage event occurs as defined in (4.4):

Pout = Pr {SIR < ρ0} = Pr

{

C

I< ρ0

}

(4.4)

where C is the received power on the reference link, which is equal to the product

between the transmitted power, PT , and the propagation loss, LIN (r); I is the

interference term which takes into account for the overall power transmitted by

other femtocells operating in the same bandwidth (i.e. at the same frequency of the

reference femtocell k) and its expression at frequency fi:

Ik = Ik(fi) =

Nfemto,fi∑

j 6=k

PT

Ltot(rj,fi)(4.5)

where Nfemto,fi is the number of transmitting femtocells in the area at the frequency

fi, Ltot > 1 is the overall pathloss at frequency fi accounting for both indoor and

outdoor losses and rj is the distance between the k-th reference femtocell and the

j-th femtocell. Note that in (4.4) we do not consider the thermal noise η since in gen-

eral for mature cellular systems it can be neglected with respect to the interference I.

4.2.1 SIR in the regular grid topology

In the regular grid topology the optimal frequency arrangement among femtocells in

terms of throughput and outage probability is depicted in Figure 4.2. The achievable

SIR for each femtocell is unique and can be calculated in a closed form as follows:

SIR =LIN(r)

4 ·MCL · L2W

(

α

(d√2)γ

+ β

2dγ

) (4.6)

where α and β are defined as:

α =

∞∑

k=1

1

kγ(4.7)

β =∞∑

k=1

1

(2k)γ. (4.8)

53


and LIN (r), MCL and LW are smaller than 1.

To obtain the summations in (4.7) and (4.8) we considered an infinite number of

interferers over an infinite grid. In the absence of shadowing the critical density of

Figure 4.2. Optimal frequency arrangement among femtocells in the regular gridscenario with 2 network operators.

femtocells leading to the condition SIR < ρ0 for every femtocell in the area can

be easily calculated from (4.6). In this case (regular grid scenario with optimal

frequency assignment) femtocells simultaneously fall below the threshold like a sort

of avalanche effect (break of equilibrium). The last consideration is valid if we do

not consider the border effects, i.e. the femtocells located in the close proximity

of the border of the area. According to this assumption, in the regular grid case

54


with optimal frequency assignment the SIR distribution could be approximated as

a delta function. However, the femtocells located at the borders and the vertices

of the scenario lead to a spread of the SIR distribution with respect to the average

value, as shown in Figure 4.3, where the empirical probability density functions of

SIR for the other network topologies are also reported. As we can note, the pdf’s

show a more and more larger variance passing from the regular grid up to the ran-

dom network topology.

The regular grid topology with optimal frequency allocation represents the baseline

Figure 4.3. SIR distribution for femtocells in different network topology withoutpower control mechanism.

for comparing the behavior and the performance of the proposed frequency adjust-

ment techniques. In this scenario the frequency assignment remains unchanged after

the application of the proposed algorithms, proving their stability. For this reason,

shadowing is not considered in our analysis in order to compare results with this ref-

erence case. However, the application of the proposed algorithms does not allow to

reach the optimal frequency/power adjustment when it is applied by starting from

a random frequency assignment on the regular grid case due to their decentralized

55


nature. This means that a local minimum of the outage probability as well as a local

maximum of the SIR are reached (i.e. the femtocell system has several equilibria

points).

4.2.2 Outage probability

In Figure 4.4 and Figure 4.5 is reported the outage probability obtained without the

chance to exchange frequency channels among operators compared with the curves

related to GDFS and ODFS algorithms for 2 and 3 available channels, respectively.

We assume ρ0 = 9.4 dB. In the random network topology the GDFS and ODFS

16 25 36 49 64 81 100 121 14410

−3

10−2

10−1

100

Number of femtocells

Out

age

Pro

babi

lity

Initial random assignment

Greedy algorithm

Operator algorithm

Greedy PC algorithm

Figure 4.4. Outage Probability with 2 frequency bands for Random (solid line)and Perturbed Grid (dashed line) topologies.

algorithms show similar performances, whereas in the case of perturbed grid the

56


81 100 121 144 169 196 22510

−3

10−2

10−1

100

Number of femtocells

Out

age

Pro

babi

lity

Initial random assignment

Greedy algorithm

Operator algorithm

Greedy PC algorithm

Figure 4.5. Outage Probability with 3 frequency bands for Random (solid line)and Perturbed Grid (dashed line) topologies.

GDFS slightly outperforms the ODFS. As we expected, the power control imple-

mentation leads to the best performance. Considering an outage probability equal

to 5 %, with respect to a network capacity of 14 and 66 femtocells with the initial

frequency assignment in the random and perturbed grid topology, respectively, the

algorithms allow to reach a network capacity of about 68 and 120 femtocells. As

shown in Figure 4.4 and Figure 4.5, an increase of the number of available frequency

bands results in an improvement in the number of active femtocells per frequency.

As an example, given an outage probability of 5 %, in the case of random net-

work topology with 2 frequency bands, we obtained about 34 served femtocells per

frequency, while in the same scenario with 3 network operators we had 47 active

femtocells per channel. In the regular grid topology, the instability effects due to

the aforementioned break of equilibrium for the optimal frequency assignment case

57


are also present: a sort of smooth on-off behavior of the outage is observed with

respect to the random network topology, i.e. a relatively small change in the fem-

tocell density leads to a sudden increase in the outage probability. However, the

instability effects are more gradual than those related to the optimal frequency as-

signment case, that means that it can not be said that all femtocells collapse at the

same moment when equilibrium is reached using the proposed algorithms.

4.2.3 Signal to Interference Ratio (SIR)

In Figure 4.6 and Figure 4.7 are reported the obtained results for the average achiev-

able SIR per femtocell. The results show that random topology always provide the

16 25 36 49 64 81 100 121 144

10

12

14

16

18

20

22

24

26

28

Number of active femtocells

Ave

rage

SIR

per

fem

toce

ll [d

B]

Initial random assignmentGreedy algorithmOperator algorithmGreedy PC algorithm

Figure 4.6. Average SIR per femtocell with 2 frequencies for Random (solid line)and Perturbed Grid (dashed line) topologies.

worst achievable performances both in terms of outage and SIR distribution for low

58


81 100 121 144 169 196 2259

10

11

12

13

14

15

16

17

18


Ave

rage

SIR

per

fem

toce

ll [d

B]

Initial random assignmentGreedy algorithmOperator algorithmGreedy PC algorithm

Figure 4.7. Average SIR per femtocell with 3 frequencies for Random (solid line)and Perturbed Grid (dashed line) topologies.

density of femtocells within the area. Conversely, when the number of femtocells

increase, the achievable average SIR in the random topology is slightly better than

the SIR obtained in the perturbed grid case. This is due to the fact that the outage

probability of the perturbed grid scenario becomes not null but smaller than the

outage obtained for the random topology, so there are more femtocells in sensing

mode. As a consequence, the average SIR per femtocell is higher until the two val-

ues of the outage probability become comparable. As we expected, the achievable

throughput with the implementation of the power control mechanism is constant,

since each femtocell continuosly adjusts its trasmission power level in order to reach

the required quality threshold ρ0. As regards the other curves, an increase of the

number of femtocells in the area corresponds to a reduced achievable SIR since,

contrary to the power control case, the transmission power is constant and so the

59


interference level outgrows. We can observe that the proposed algorithms perform

considerably better than the initial random frequency assignment. The GDFS algo-

rithm shows an improvement with respect to the ODFS algorithm due to the greedy

nature of femtocells aiming at maximizing their throughput.

In Figure 4.8 we report the comparison between the average achievable SIR of femto-

cells in the regular grid scenario with optimal frequency assignment and the regular

and perturbed grid topologies where the proposed algorithms are applied. The re-

16 25 36 49 64

10

12

14

16

18

20

22

24

26

28


Ave

rage

SIR

per

fem

toce

ll [d

B]

Optimal frequency assignmenton Regular Grid topology

Figure 4.8. Average SIR per femtocell with 2 frequencies for Regular Grid with op-timal frequency assignment (solid line), Regular Grid (dashed line) and Perturbed

Grid (dotted line) topologies.

sults show that if only the initial random frequency assignment is considered in

the regular grid topology, we obtain a difference of about 3 dB with respect to the

optimal case, but with the implemetation of our GDFS algorithm we improve the

performance, leading the achievable throughput very near to the optimal curve. In

general, the obtained results show that when the density of femtocells increases the

60


cases related to the different network topologies tend to converge to the same per-

formance in terms of SIR and outage probability.

4.2.4 Spectrum Sharing Gain SSG

Given a certain value of the outage probability Pout and maintaining the number of

femtocells per frequency band constant, we define the Spectrum Sharing Gain (SSG)

as:

SSG|Pout=

Tf∈F

Tf∈Fop

(4.9)

where Tf∈F and Tf∈Fopare the numbers of active femtocells after the implementa-

tion of the proposed DFS algorithm considering the set of channels available from

all the operators F ={

f1,...,fNf×Mop

}

and just the set of own operator frequency

bands Fop ={

f1,...,fNf

}

, respectively. This parameter represents the increase in

the number of active femtocells for a fixed outage probability, obtained by means of

DFS algorithm and when operators permit the mutual exchange of their frequency

bands with respect to the case in which each femtocell can dinamically select only

the channels available from its own operator. In the following sub-section we provide

results concerning the SSG. To the aim of evaluating the SSG, in our simulations

we mantain the ratio between the density of femtocells in the area and the number

of available channels constant.

As for the simulations described in the previous section, the interference measure-

ments and the status update is randomly scheduled by each femtocells. The simula-

tion time is long enough to ensure that the equilibrium in the allocation of frequency

bands has been reached, i.e. each femtocell remains on the last selected operating

channel.

In order to evaluate an operating boundary condition, it is assumed that HNBs

do not implement power control mechanisms, i.e. femtocells are sources transmit-

ting at the maximum power level set according to the standard specification, i.e.

Ptx = Pmax = 20 dBm [32]. Moreover, we assume ρ0 = 16.4 dB for a 16-QAM

61


connection.

In Figure 4.10 and Figure 4.9 are reported the SSG and the number of active fem-

tocells per operator, respectively, for different values of the outage probability. An

2 3 4 5 6 7 810

15

20

25

30

35

40

45

50

Number of frequencies

Num

ber

of fe

mto

cells

per

ope

rato

r

Pout

= 2%

Pout

= 5%

Figure 4.9. Number of active femtocells per operator vs the number of availablefrequency bands for Nf = 2.

increase in the number of available frequency bands results in an improvement in

the allowed number of active femtocells. As an example, for an outage probability

of 2 % the DFS algorithm allows to obtain about 31, 37 and 42 active femtocells

per single operator when 4, 6 and 8 frequency bands are allocated, respectively,

while we experiment 14 served femtocells in the case of 2 available channels. As

shown in Figure 4.10, the lower the outage probability the more evident the SSG.

In general, the achieved results show that a marked gain of the active HNBs per

operator is obtained with the sharing of the spectrum bands among operators. The

SSG increases up to likely reach a saturation point for higher values of the num-

ber of available channels. This is due to the fact that in our analysis the number

62


1 1.5 2 2.5 3 3.5 40

20

40

60

80

100

120

140

160

180

200

Number of operators

SS

G [%

]

Pout

= 2%

Pout

= 5%

Figure 4.10. Spectrum Sharing Gain vs the number of operators with Nf = 2.

of femtocells in the considered scenario is proportional to the number of spectrum

bands. This leads to a very high density of femtocells in the considered scenario

which prevents DFS algorithm from overcoming the achieved performance.

The obtained gain can be explained looking at the formula in (4.5). Indeed, the in-

terference caused by the other femtocells transmitting on the same frequency band

mainly depends on the distance between the considered HNB and the nearest inter-

ferer(s). With the possibility to exploit the sharing of spectrum bands of different

operators, the proposed DFS algorithm performs an autonomous redistribution of

the minimum frequency reuse distance among femtocells, as highlighted by Fig-

ure 4.11. In particular, with the increase of the available frequency bands, the DFS

algorithm tends to shrink the variance of the obtained distributions. This allows

to reduce or completely eliminate the lower values of the distributions, i.e. the tail

on the left side (see results from 4 to 8 available frequencies in Figure 4.11) which

63


correspond to the most harmful interferers for the considered femtocell. Hence, the

required outage probability is reached with a higher allowed number of served fem-

tocells per operator, which explains the SSG, especially for small outage probability.

Figure 4.11. PDF of the minimum reuse distance for Pout = 2%.

4.3 Chapter Summary

The proposed algorithms provide marked improved performance with respect to the

random frequency assignment resulting from the self-installation nature of femto-

cells. In particular, the suggested GDFS algorithm performs better than the ODFS

algorithm in terms of achievable SIR, while we obtained similar results for the out-

age probability. Moreover, the results show that the GDFS algorithm allows to

reach performance very close to the optimal case in terms of achievable throughput

also in a typical residential scenario with femtocells randomly installed in adjacent

apartments.

64


In addiction, the algorithm also shows a marked gain in terms of active femtocells per

operator with the increase of the number of shared frequency bands. The obtained

results lead to a significant consideration from the operator point of view: spectrum

sharing among operators is advantageous to ensure QoS to their subscribers.

65

Chapter 5

DFS algorithm: robustness and

resilience against malicious users

In this chapter is analyzed the performance gap with respect to the results obtained

in Chapter 4 with reference to two situations:

1. in the first one it is considered that only a certain percentage of HNB’s imple-

ments the DFS algorithms;

2. in the second case, all the femtocells adopts the suggested DFS algorithms

but some of them only partially follow the rules, to the aim of maliciously

exploiting the frequency resources. These HNB’s, referred to as selfish femto-

cells, continue to occupy the channel with dummy data even if their QoS is

below the required level in order to force the other DFS-conformed HNB’s to

interrupt transmissions.

The first case is merely aimed at evaluating the impact on QoS of a percentage of

HNB’s defecting from the DFS algorithm. This can be expected in a more realistic

scenario where failures or a transient state towards the complete adoption of DFS

algorithm can occur. For this purpose we consider femtocells installed in a residen-

tial/offices building, in order to obtain a realistic environment.

In the second situation, the selfish behaviour assumed by femtocells is aimed to

66

5.1 – Scenario description

decrease the interference level by forcing the other DFS-conformed HNB’s to turn

into sensing mode, which means to interrupt their transmissions.

In the previous chapters the proposed DFS alhorithms are analyzed considering

both maximum power and power-controlled transmissions. Since in this chapter

we are just interested in evaluating the impact of different behaviours assumed

by HNB’s on the network performance in terms of outage probability and average

achievable throughput, we assume that HNB’s do not implement power control

mechanisms. It means that all the femtocells transmit at the maximum power level

allowed by the standard specification, i.e. Ptx = Pmax = 20 dBm [32], but each HNB

can or can not conform to the DFS algorithm.

In addition, in this chapter we do not refer to a specific DFS algorithm, for this

reason, we refer to them as Dynamic Frequency Selection Algorithm (DFSA).

5.1 Scenario description

In Figure 5.1 the interference scenario is depicted. Femtocells installed in a six floor

residential building are considered. As regards the number of femtocells within the

building, we consider two cases corresponding to different levels of HNB’s density. By

assuming one HNB for each apartment, in the first case each floor has two apartments

(low-medium density scenario), whereas in the second case four apartments are

considered for each floor (high density scenario). According to the typical self-

installation by users, HNB’s are randomly located inside the apartments. We assume

that the floor area is 200 m2 and each floor is 3 m heigth. Equal-area apartments

are assumed in which user terminals are located at a distance of 4 m from the HNB.

A typical multi-operator scenario is considered where each femtocell is subscribed

to one of the N network operators. In the analysis is assumed both N = 2 and

N = 3 operators providing services in the considered building area. The initial

assignment of each femtocell to an operator (and therefore to an operating frequency)

is randomly performed in accordance to an uniform distribution. We assume that

each operator allocates one dedicated frequency band for femtocell communications.

67

5 – DFS algorithm: robustness and resilience against malicious users

Residential building

3 m

floor area = 200 m2

femtocell of operator A

femtocell of operator B

Figure 5.1. Building scenario for the interference analysis. HNB’s are indicatedwith circle and each colour represents the assignment to a different operator.

Hence HNB’s subscribed to a same operator do not suffer from cross-tier interference,

whereas they interfere one with each other due to the sharing of a single radio

channel.

In this analysis we are just interested in evaluating the impact of different behaviours

assumed by HNB’s on the network performance in terms of outage probability and

average achievable throughput, we assume that HNB’s do not implement power

control mechanisms. It means that all the femtocells transmit at the maximum

power level allowed by the standard specification, i.e. Ptx = Pmax = 20 dBm [32],

but each HNB can or can not conform to the DFSA. In particular, we analyze the

following two different cases:

1. femtocells can or can not adopt the DFSA;

2. all the femtocells implement DFSA, but some of them are selfish and do not

turn into sensing mode, i.e. even if the condition SIR < ρ0 is verified they

access the channel with dummy data instead of setting their transmission

status to off and just performing interference measurements.

68

5.2 – Performance analysis

The selfish behaviour assumed by femtocells is aimed to decrease the interference

level by forcing the other DFSA-conformed HNB’s to turn into sensing mode, which

means to interrupt their transmissions.

As for the signal propagation, the path loss model described in section 4.1 is con-

sidered. As recommended by the ITU-R [32], in equation (4.2) both γ and Lf(nf )

depend on the operating frequency and the environment. In this analysis is as-

sumed that femtocells are in residential environment with nf = 6 and operate at

frequencies fc around 1800 MHz, with a channel spacing between different network

operators ∆fc = 10 MHz. Based on this assumption, from [32] we set γ = 2.8 and

Lf(nf ) = 15 + 4 · (nf − 1).

5.2 Performance analysis

We run simulations to evaluate the impact of non DFSA-conformed femtocells on

the QoS performance. The outage probability and the average SIR per femtocell

are assessed as a function of the percentage of non DFSA-conformed HNB’s. As

describe in the previous chapter, each femtocell is randomly scheduled for the inter-

ference measurements and the status update. Furthermore, the simulation time is

appropriately set to guarantee the convergence of the DFSA, i.e. at the end of the

test each femtocell has selected the best operating frequency channel. Simulation

results are obtained using a Monte Carlo based approach.

As regards the low-medium density scenario (i.e. 2 femtocells per floor), in Figure

5.2 and Figure 5.3 are reported the outage probability and the average SIR per fem-

tocell, respectively, as a function of the percentage of femtocells whose behaviour is

not to conform to the proposed DFSA, for different number of available frequency

channels. When 2 operators provide services in the considered residential area,

the complete defection from the DFSA causes an increase of the outage probability,

which ranges from about 8,4 % (which is equivalent on average to 1 outage femtocell

in the building) when all the femtocells adopt the proposed algorithm up to about

69


0 20 40 60 80 10010

−3

10−2

10−1

100

Non "DFS−conformed" femtocells [%]

Out

age

prob

abili

ty

Femtocells without DFS, 2 fc


Selfish femtocells, 2 fc


Figure 5.2. Outage probability vs the percentage of non DFSA-conformed femto-cells in a low-medium density scenario.

50 % in the all non DFSA-conformed HNB’s case. This outstanding degradation

is due to the impossibility to perform an efficient allocation of frequency channels

by means of the proposed algorithm. Conversely, the selfish behaviour of femtocells

does not cause a marked decrease of performance. This is due to the fact that in

a low density scenario as long as less than about 85 % of HNB’s are selfish there

is on average always one DFSA-compliant femtocell which experiments a SIR less

than the required threshold ρ0 and turns into sensing mode, i.e. it sacrifices itself

in behalf of network capacity. When all the femtocells are selfish we note a little

decrease of performance due to the increase of the average interference level caused

by the continuos trasmission by each femtocell. The obtained results show similar

trends also when 3 operators are considered. However, in this case the gap between

an all DFSA-compliant scenario and the case of 100 % non DFSA-conformed HNB’s

is larger than the 2 operators scenario. Indeed, when 3 operating frequencies are

70


available for femtocells, a smart cognitive selection of the channel lead to a marked

improvement of outage probability with respect to the initial random assignment.

In this case, the selfish behaviour of femtocells is completeley negligible due to the

very low values of outage probability.

The performance related to the outage probability are reflected in the average SIR

experimented by femtocells which are not in outage, as shown in Figure 5.3. As

expected, for 2 available frequency bands with respect to the all DFSA-conformed

HNB’s case a slight worsening of average SIR is observed when both all femtocells do

not implement the DFSA and all the femtocells are selfish. This trend is reflected

in Figure 5.4, which reports the cumulative distribution functions (CDFs) of the

SIR for different percentage of HNB’s defecting from the DFSA. This results are

obtained considering the SIR of all the femtocells in the area. We can note that in

general it is better to adopt the DFSA, but with the increase of the percentage of

non DFSA-conformed femtocells the probability of obtaining higher values of SIR

increases. Anyway, it is worthwhile to note that by considering only the femtocells

whose SIR ≥ ρ0 the achievable average SIR is almost the same in both situations

with a very slight difference in favour of the selfish case when the percentage of

non DFSA-conformed HNB’s increases. This can be explaned by considering the

low-medium density scenario which permits a better redistribution of frequency re-

sources by adopting the DFSA compared with the random channel assignment. This

is more evident when 3 operators provide services in the considered area, resulting

in an average SIR difference of more than 2 dB between the selfish behaviour and

the defection from DFSA. As shown in Figure 5.3, in this scenario it’s much better

for femtocells to conform to the proposed DFSA since the average SIR per HNB’s

increases of more than 2 dB. Conversely, when HNB’s implement the DFSA with-

out sensing mode the same performance are obtained regardless of the percentage

of femtocells taking the selfish attitude.

As regards the high density scenario (i.e. 4 femtocells per floor), the obtained

results in terms of outage probability and average SIR per femtocell are shown in

71


0 20 40 60 80 10019.5

20

20.5

21

21.5

22

22.5

23

23.5


aver

age

SIR

per

fem

toce

ll [d

B]





Figure 5.3. Average SIR vs the percentage of non DFSA-conformed femtocells ina low-medium density scenario.

Figure 5.5 and Figure 5.6, respectively. As in the previous scenario, the implemen-

tation of DFSA always allows to maximize the network capacity and the achievable

average SIR. In particular, starting from higher values of outage probability due to

the increased number of femtocells per area, the consideration related to the trends

of the outage curves are the same of the low-medium density scenario, with the

exception that the higher is the percentage of selfish femtocells the more evident is

the decrease of network capacity. For example, even when 3 carrier frequencies are

available, the selfish behaviour by all the femtocells causes an increase of the outage

probability of about 3 % with respect to the all DFSA-conformed HNB’s case.

As for the average SIR of femtocells in quality, interesting results are shown in Fig-

ure 5.6, where we can observe worst performance in the case of selfish femtocells with

respect to the case of defection from the DFSA. This is due to the high density of

femtocells in the area, which in the random frequency assignment for the considered

72


0 10 20 30 40 500

20

40

60

80

100

SIR [dB]

Pro

b(S

IR<

absc

issa

) [%

]

25%

0 10 20 30 40 500

20

40

60

80

100

SIR [dB]

Pro

b(S

IR<

absc

issa

) [%

]

50%

0 10 20 30 40 500

20

40

60

80

100

SIR [dB]

Pro

b(S

IR<

absc

issa

) [%

]

75%

0 10 20 30 40 500

20

40

60

80

100

SIR [dB]

Pro

b(S

IR<

absc

issa

) [%

]

100%

Figure 5.4. CDF for various percentage of femtocells that do not implemet theDFSA algorithm.

scenario statistically lead to a greater value of the average SIR of a few femtocells

that are not in outage (see Figure 5.5) since with the random channel distribution

single femtocells of one operator neighbour to cluster of femtocells belonging to the

other operator can occur. This implies that a lot of femtocells measures SIR < ρ0,

while a few “lucky” femtocells can experiment high values of SIR, as visible in the

last curve of Figure 5.4. Conversely, with the increase of HNB’s density the smart

allocation of the available operating channels still permits to preserve the outage

performance when the percentage of selfish femtocells increases, but on average the

achievable SIR is lower than that obtained when the same percentage of HNB’s

defects from the proposed algorithm.

In general, we can argue that when the percentage of selfish femtocells increases the

performance in terms of network capacity are preserved against a little reduction in

73


0 20 40 60 80 10010

−2

10−1

100


Out

age

prob

abili

ty





Figure 5.5. Outage probability vs the percentage of non DFSA-conformed femto-cells in a high density scenario.

the average SIR measured by those femtocells that are in quality; to the contrary,

in the case of femtocells defecting from DFSA the average SIR of femtocells which

are not in outage shows a very slight decrease with respect to the best performance

(i.e. the all DFSA-conformed HNB’s case) to the detriment of marked worse outage

probability. Furthermore, the obtained results point out that for low percentage of

HNB’s that do not turn into sensing mode the selfish behaviour is advantageous

for them and at the same time, even if an unfair situation occurs, it does not sub-

stantially debase the performance in terms of network capacity as well as average

SIR per femtocell, even in high density scenario. However, this is valid only when

less than about 25 % of femtocells are selfish. Hence, the best situation is verified

when all the femtocells implement the proposed DFSA without assuming a selfish

behaviour.

As an example, with reference to the Bit Error Rate (BER) curves shown in Figure

74


0 10 20 30 40 50 60 70 80 90 10017.5

18

18.5

19

19.5

20


aver

age

SIR

per

fem

toce

ll [d

B]





Figure 5.6. Average SIR vs the percentage of non DFSA-conformed femtocells ina high density scenario.

5.7 for different modulation schemes, we give an indication of the possible benefits

in terms of achievable throughput deriving from the implementation of the DFSA.

By assuming a symbol rate of 1 Msymbol/s we can obtain a bit rate of 2, 4 and

6 Mbit/s for QPSK, 16QAM and 64QAM, respectively, based on the available av-

erage SNR on a Rayleigh fading channel. Considering a required BER of 10−3, in

the considered low-medium density scenario a bit-rate of 6 Mbit/s is always avail-

able for active femtocells regardless of their behaviour. Conversely, in the described

high density scenario we can note a decrease of the achievable throughput from 6

to 4 Mbit/s both passing from 3 to 2 available carrier frequencies and considering

3 channels when all the femtocells are selfish.

Hence, the higher is the density of femtocells in the area the greater is the number of

75


−5 0 5 10 15 20 2510

−4

10−3

10−2

10−1

100

average SNR [dB]

BE

R

QPSK16QAM64QAM

Figure 5.7. Bit Error Rate vs the average SNR on a Rayleigh fading channel fordifferent modulation schemes assuming a coding gain gc=10 dB.

operating channels needed for enjoying high data rate services and the more conve-

nient is to entirely implement the DFSA, i.e. without assuming selfish behaviours.

5.3 Chapter Summary

In this chapter is analyzed the impact on the QoS performance of different behaviour

of HNB’s with respect to the case in which all the femtocells adopt the proposed

algorithm. The outage probability and the average SIR per femtocell is evaluated

by simulations as a function of the percentage of femtocells that defect from the

proposed DFSA or behave like selfish, i.e. they maliciously adopt the DFSA without

turning into sensing mode. Both cases of 2 and 3 operators is assumed providing

services in the considered indoor scenario. Each operator allocates one dedicated

76


frequency channel for femtocell communications. Results show that in order to

maximize the network capacity and the average throughput it is better for femtocells

to fully conform to the proposed DFSA, i.e. by also implementing the sensing mode.

Moreover, it is observed that with the increase of the available carrier frequencies

and the decrease of the density of HNB’s in the area the selfish behaviour tends not

to affect the QoS performance.

77

Chapter 6

Conclusions

In this work the problem of co-tier interference for femtocells is faced. Different net-

work topologies are considered, ranging from a pre-planned deployment to a random

placement of femtocells.

Based on the assumption that operators agree on the sharing of their spectrum

bands, two simple distributed algorithms are proposed aimed to dynamically redis-

tribute the available frequency resources belonging to different network operators

among femtocells just based on local interference measurements.

The proposed algorithms provide marked improved performance with respect to

the random frequency assignment resulting from the self-installation nature of fem-

tocells. In particular, the suggested GDFS algorithm performs better than the

ODFS in terms of achievable SIR, while we obtained similar results for the outage

probability. Moreover, the results show that the GDFS algorithm allows to reach

performance very close to the optimal case in terms of achievable throughput also in

a typical residential scenario with femtocells randomly installed in adjacent apart-

ments.

In order to assess the real performance of the proposed algorithms I define the Spec-

trum Sharing Gain (SSG), as the number of served femtocells per operator when

the channels available from operators increase.

78

The performance analysis carried out to obtain this parameter show that the pro-

posed algorithm achieves a marked gain in terms of active femtocells per operator

with the increase of the number of shared frequency bands.

The obtained results lead to a significant consideration from the operator point of

view: spectrum sharing among operators is advantageous to ensure QoS to their

subscribers.

In the second part of this thesis I have analyzed the robustness of the Dynamic

frequency Selection Algorithms (DFSA) and the impact on network capacity of

Femtocells defecting from the DFSA. For this scope, the impact on the QoS per-

formance of different behaviour of HNB’s with respect to the case in which all the

femtocells adopt the proposed algorithm are analyzed.

The outage probability and the average SIR per femtocell as a function of the per-

centage of femtocells that defect from the proposed DFSA or behave like selfish, i.e.

they maliciously adopt the DFSA without turning into sensing mode, are evaluated

by simulations.

In order to analyze a more realistic scenario a residential/offices building is consid-

ered. Both cases of 2 and 3 operators providing services in the considered indoor

scenario are assumed. Each operator allocates one dedicated frequency channel for

femtocell communications.

Results show that in order to maximize the network capacity and the average

throughput it is better for femtocells to fully conform to the proposed DFSA, i.e. by

also implementing the sensing mode. Moreover, it is observed that with the increase

of the available carrier frequencies and the decrease of the density of HNB’s in the

area the selfish behaviour tends not to affect the QoS performance.

Summarizing, the obtained results highlights that:

• adopting the proposed DFSA we obtain a marked improvement on QoS per-

formance;

• for operators is better to adopt the proposed algorithms and to share spectrum

each other;

79

6 – Conclusions

• it’s not necessary that all femtocells in an area adopt the DFSA in order to

obtain a performance improvement;

• DFSA performs a natural resilience against malicious users.

80

Bibliography

[1] Chandrasekhar V, Andrews J, Gatherer A (2008) Femtocell networks: a survey.

In: IEEE Communications Magazine, Vol. 46, Issue 9, page(s): 59 - 67.

[2] Claussen H (2007) Performance of Macro- and Co-Channel Femtocells in a Hi-

erarchical Cell Structure. In: IEEE 18th International Symposium on Personal,

Indoor and Mobile Radio Communications.

[3] Calin D, Claussen H, Uzunalioglu H (2010) On femto deployment architectures

and macrocell offloading benefits in joint macro-femto deployments. In: IEEE

Communications Magazine, Vol. 48, Issue 1, page(s): 26 - 32.

[4] J. D. Hobby and H. Claussen, “Deployment options for femtocells and their

impact on existing macrocellular networks”, Bell Labs Technical Journal, vol.

13, no. 4, page(s): 145-160, 2009.

[5] J. Zhang, G. de la Roche, Femtocells: Technologies and Deployment, Ed. John

Wiley & Sons Ltd., 2010.

[6] Claussen, H., “Performance of Macro- and Co-Channel Femtocells in a Hierar-

chical Cell Structure”, IEEE 18th International Symposium on Personal, Indoor

and Mobile Radio Communications (PIMRC 2007), page(s): 1-5, Sep. 2007.

[7] V. Chandrasekhar and J. Andrews, “Uplink Capacity and Interference Avoidance

for Two-Tier Cellular Networks”, IEEE Global Telecommunications Conference,

(GLOBECOM 2007), page(s):3322-3326, Nov. 2007.

[8] S. ping Yeh, S. Talwar, S. choon Lee, and H. Kim, “WiMAX femtocells: a

perspective on network architecture, capacity, and coverage”, IEEE Commun.

81

Bibliography

Mag., vol. 46, no. 10, page(s): 58-65, Oct. 2008.

[9] Ho, L.T.W., Claussen, H. ; “Effects of User-Deployed, Co-Channel Femtocells

on the Call Drop Probability in a Residential Scenario”, IEEE 18th Interna-

tional Symposium on Personal, Indoor and Mobile Radio Communications, 2007

(PIMRC 2007).

[10] Lopez-Perez, D., Valcarce, A., de la Roche, G., Jie Zhang ; “OFDMA femtocells:

A roadmap on interference avoidance”, IEEE Communications Magazine, Vol.

47, Issue 9, page(s): 41 - 48, Sep. 2009.

[11] Mitola, J., III; “Cognitive Radio for Flexible Mobile Multimedia Communica-

tions”, IEEE International Workshop on Mobile Multimedia Communications

(MoMuC ’99), San Diego CA, USA, Nov. 1999.

[12] D. Isenberg, ”The Rise of the Stupid Network”, Computer Telephony, August

1997. Web: http://www.hyperorg.com/misc/stupidnet.html

[13] ABI Research, ”Femtocell Market Challenges and Opportunities - Cellular-

Based Fixed Mobile Convergence for Consumers, SMEs and Enterprises”, 2Q

2007.

[14] 3GPP Doc. TR 25.820 V8.0.0 (2008-03), Technical Report, 3rd Generation

Partnership Project; Technical Specification Group Radio Access Networks; 3G

Home NodeB Study Item Technical Report (Release 8), 2008.

[15] V. Chandrasekhar, J.G. Andrews, and A. Gatherer, ”Femtocell Networks: A

Survey”, June 23, 2008.

[16] H. Claussen, L.T. W. Ho, L. G. Samuel, ”Self-optimization of Coverage for Fem-

tocell Deployments”, 24-26 April 2008, Wireless Telecomunications Symposium

(WTS 2008), pp. 278 285.

[17] 3GPP Doc. R4-071660 - Ericsson, ”Impact of HNB with fixed output power on

macro HSDPA capacity” 3GPP TSG-RAN Working Group 4 (Radio), Shanghai,

October, 2007.

[18] 3GPP TS 25.367 v8.1.0, ”Mobility procedures for Home Node B (HNB); Overall

82

Bibliography

description; Stage 2”, www.3gpp.org

[19] 3GPP TS 25.331: ”Radio Resource Control (RRC) protocol specification”,

www.3gpp.org

[20] 3GPP TS 25.967 v8.1.0, ”Home Node B Radio Frequency (RF) Requirements

(FDD)”, www.3gpp.org.

[21] Alexander Wyglinski, Maziar Nekovee, Thomas Hou, “Cognitive Radio Com-

munications and Networks Principles and Practice”, Academic Press, 2009

[22] J. Mitola et al., “Cognitive radio: Making Software Defined Radio more per-

sonal”, IEEE Personal Communications., vol. 6, no. 4, pp. 13-18, 1999.

[23] FCC. Et docket no. 03-322. Notice of Proposed Rule Making and Order, De-

cember 2003.

[24] Mahmoud, A. Hisham, I. Guvenc, ”A comparative study of different deployment

modes for femtocell networks”, IEEE Int. Symp. on Pers., Ind. Mob. Rad. Com.

(PIMRC 2009), Sept. 2009, Toyko Japan, p.1-5.

[25] European Commission (2009) Directive 2009/140/EC of the European Parlia-

ment and of the Council. In: Official Journal of the European Union, L 337, Vol.

52, page(s): 37 - 68.

[26] IEEE 802.22 Working Group on Wireless Regional Area Networks. [Online]:

www.iee802. org/22, Feb. 2009.

[27] S. Olafsson, B. Glower, and M. Nekovee, “Future management of spectrum”,

BT Technology Journal, vol. 25, no. 2, pp. 52-63, 2007.

[28] European Commission (2010) Spectrum: Commission proposes to ensure

availability of radio frequencies for new and faster wireless services. In:

MEMO/10/425.

[29] European Commission (2010) Radio spectrum policy: first programme. In:

COM/2010/0471 final - COD 2010/0252.

[30] J. Zhang, G. de la Roche, Femtocells: Technologies and Deployment, Ed. John

Wiley & Sons Ltd., 2010.

83

Bibliography

[31] Humblet, P. Raghothaman, B. Srinivas, A. Balasubramanian, S. Patel, C.

Yavuz, M., “System design of CDMA2000 femtocells”, IEEE Comm. Mag., Sept.

2009.

[32] Simon Saunders, Stuart Carlaw, Andrea Giustina, Ravi Rai Bhat, V. Srinivasa

Rao, Rasa Siegberg, “Femtocells: Opportunities and Challenges for Business

and Technology”, Wiley 2009.

84

Dynamic frequency allocation in femtocells-based systems: algorithms and performance analysis

Documents

Transcript of Dynamic frequency allocation in femtocells-based systems: algorithms and performance analysis