by Ola Ashour Mohammed...I sincerely want to thank my supervisors Prof. Khalil El-Khatib and Prof. Miguel Vargas Martin for their guidance and encouragement. They were always supporting

SPECTRUM SENSING BASED ON CAPON POWER SPECTRAL DENSITY

ESTIMATION

by

Ola Ashour Mohammed

A thesis submitted in conformity with the requirementsfor the degree of Master of Applied Science

Faculty of Engineering and Applied Science (Electrical and ComputerEngineering)

University of Ontario Institute of Technology

Supervisor(s): Khalil El-Khatib, Miguel Vargas Martin

Copyright c© 2015 by Ola Ashour Mohammed

Abstract

Spectrum Sensing Based on Capon Power Spectral Density Estimation

Ola Ashour Mohammed

Master of Applied Science

Faculty of Engineering and Applied Science

University of Ontario Institute of Technology

2015

Cognitive radio (CR) technology has evolved to solve the spectrum scarcity problem and im-

prove spectrum utilization. Spectrum sensing is a CR function that allows secondary users to

efficiently utilize the spectrum without interfering with primary users. The performance of this

function depends on the efficiency of the used detection method. In this thesis, we propose a

spectrum sensing based on the Capon Power Spectral Density (PSD) estimation method. The

proposed method estimates the received PSD, and uses it to identify free and busy channels.

A cooperative spectrum sensing approach is also introduced. The goal is to solve the common

hidden node problem and help devices without CR capability to identify free channels. Exper-

imental results show that the proposed method outperforms the spectrum sensing based on the

Periodogram method in detecting both busy and free channels. In addition, simulation results

show that the cooperative approach improves the spectrum sensing function.

ii

Dedication

I would like to dedicate my work to my lovely parents for their endless love and whose words

of encouragement were lightening my road to success.

To my adorable son Yusuf, and my husband Mohamed who has been a constant source of

support during the challenges of graduate school and life. I thank Allah for having you in my

life and cannot imagine the long way we passed together.

To my pretty sister Rehab and brother Ahmed who believe on me and were always there to

support me.

iii

Acknowledgements

Thanks to Almighty ALLAH, the most gracious and the most merciful, who gave me the

strength and patience to complete this thesis.

I sincerely want to thank my supervisors Prof. Khalil El-Khatib and Prof. Miguel Vargas

Martin for their guidance and encouragement. They were always supporting me on the hardest

times.

I want to express my gratitude to Prof. Shahram Shahbazpanahi for his time, advice and

guidance to finish this thesis.

I would like to thank Prof. Michael Bennett for his guidance which helped me to obtain the

prestigious Danny Ho Scholarship.

I would also like to thank the Natural Sciences and Engineering Research Council of

Canada for funding part of my research work.

iv

Contents

1 Introduction 1

1.1 Thesis Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.1.1 White Spaces (Spectrum Holes) . . . . . . . . . . . . . . . . . . . . . 3

1.1.2 TV White Spaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

1.1.3 Countries Utilizing White Spaces . . . . . . . . . . . . . . . . . . . . 5

1.2 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

1.3 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

1.4 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2 Dynamic Spectrum Access and Cognitive Radio Technology 9

2.1 Dynamic Spectrum Access . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.1.1 Dynamic Exclusive Use Model . . . . . . . . . . . . . . . . . . . . . . 9

2.1.2 Open Sharing Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2.1.3 Hierarchical Access Model . . . . . . . . . . . . . . . . . . . . . . . . 10

2.2 Cognitive Radio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.2.1 Cognitive Radio Network Architecture . . . . . . . . . . . . . . . . . 11

2.2.2 Cognitive Radio Network Operation . . . . . . . . . . . . . . . . . . . 12

3 Cognitive Radio Management Functions 15

3.1 White Spaces Determination . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

3.1.1 Spectrum Sensing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

v

Primary Transmitter Detection . . . . . . . . . . . . . . . . . . . . . . 16

Hidden Node Problem . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Cooperative Spectrum Sensing . . . . . . . . . . . . . . . . . . . . . . 18

Primary Receiver Detection . . . . . . . . . . . . . . . . . . . . . . . 20

Interference Temperature Metric . . . . . . . . . . . . . . . . . . . . . 20

3.1.2 Geo-Location Database . . . . . . . . . . . . . . . . . . . . . . . . . . 21

3.2 Spectrum Decision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

3.3 Spectrum Sharing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

3.3.1 Spectrum Sharing Architectures . . . . . . . . . . . . . . . . . . . . . 23

3.3.2 Spectrum Allocation Behaviour . . . . . . . . . . . . . . . . . . . . . 23

3.3.3 Spectrum Access Techniques . . . . . . . . . . . . . . . . . . . . . . . 24

3.3.4 Spectrum Sharing Scope . . . . . . . . . . . . . . . . . . . . . . . . . 24

3.3.5 Spectrum Sharing Models . . . . . . . . . . . . . . . . . . . . . . . . 24

3.4 Spectrum Mobility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

4 Spectrum Sensing Based on Capon PSD Estimation 27

4.1 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

4.2 Choosing Threshold Value . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

4.3 Energy Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

4.3.1 Energy Detection in Time Domain . . . . . . . . . . . . . . . . . . . . 31

4.3.2 Energy Detection in Frequency Domain . . . . . . . . . . . . . . . . . 32

4.4 Power Spectral Density Estimation . . . . . . . . . . . . . . . . . . . . . . . . 33

4.4.1 Power Spectral Density . . . . . . . . . . . . . . . . . . . . . . . . . . 34

4.4.2 Periodogram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

4.4.3 Capon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

4.5 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

vi

5 Simulation and Results 40

5.1 Determining the Operating Frequency of Primary Transmitters . . . . . . . . . 41

5.2 Measuring the Detection Performance . . . . . . . . . . . . . . . . . . . . . . 44

5.2.1 Detecting Primary Transmitters . . . . . . . . . . . . . . . . . . . . . 46

5.2.2 Detecting White Spaces . . . . . . . . . . . . . . . . . . . . . . . . . 48

5.3 Cooperative Spectrum Sensing Based on Capon Method . . . . . . . . . . . . 50

5.3.1 Changing Threshold Value . . . . . . . . . . . . . . . . . . . . . . . . 52

5.3.2 Changing Number of Sensors . . . . . . . . . . . . . . . . . . . . . . 53

5.3.3 Changing Search Distance . . . . . . . . . . . . . . . . . . . . . . . . 55

6 Conclusion and Future Work 58

References 59

vii

List of Tables

5.1 Common parameters for the three scenarios. . . . . . . . . . . . . . . . . . . . 40

5.2 Performance detection parameters. . . . . . . . . . . . . . . . . . . . . . . . . 45

viii

List of Figures

Figure 1.1 The electromagnetic spectrum [54] . . . . . . . . . . . . . . . . . . . . 2

Figure 1.2 Spectrum utilization [7] . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Figure 1.3 Concept of spectrum hole [3] . . . . . . . . . . . . . . . . . . . . . . . 4

Figure 1.4 Canadian TV transmitters coverage . . . . . . . . . . . . . . . . . . . . 5

Figure 2.1 Dynamic spectrum access classification [62] . . . . . . . . . . . . . . . 10

Figure 2.2 Cognitive radio concept [32] . . . . . . . . . . . . . . . . . . . . . . . 12

Figure 2.3 Infrastructure-based CRN [10] . . . . . . . . . . . . . . . . . . . . . . 13

Figure 2.4 Ad hoc CRN [10] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Figure 2.5 CRNs operation inside the coverage area of primary networks [55] . . . 14

Figure 3.1 Cognitive radio functions [42] . . . . . . . . . . . . . . . . . . . . . . 15

Figure 3.2 Methods used for white spaces determination [42] . . . . . . . . . . . . 17

Figure 3.3 Hidden node problem [3] . . . . . . . . . . . . . . . . . . . . . . . . . 19

Figure 3.4 Cooperative sensing methods (a) Centeralized (b) Distributed [1] . . . . 20

Figure 3.5 Spectrum sharing categorization [42] . . . . . . . . . . . . . . . . . . . 23

Figure 4.1 Types of error in hypothesis testing . . . . . . . . . . . . . . . . . . . . 29

Figure 4.2 Time domain representation of energy detection . . . . . . . . . . . . . 31

Figure 4.3 Frequency domain representation of energy detection . . . . . . . . . . 32

Figure 4.4 Spectrum sensing steps . . . . . . . . . . . . . . . . . . . . . . . . . . 39

Figure 5.1 SUs distribution with respect to primary transmitters . . . . . . . . . . . 41

ix

Figure 5.2 PSD at the location of SU1 for high SNR . . . . . . . . . . . . . . . . . 42

Figure 5.3 PSD at the location of SU1 for low SNR . . . . . . . . . . . . . . . . . 43

Figure 5.4 PSD at the location of SU2 for high SNR . . . . . . . . . . . . . . . . . 43

Figure 5.5 PSD at the location of SU2 for low SNR . . . . . . . . . . . . . . . . . 44

Figure 5.6 Probability of detecting primary transmitters versus threshold . . . . . . 46

Figure 5.7 Probability of false alarm versus threshold . . . . . . . . . . . . . . . . 47

Figure 5.8 Average ROC for both Capon and Periodogram methods . . . . . . . . 48

Figure 5.9 Probability of detecting white spaces versus threshold . . . . . . . . . . 49

Figure 5.10 Probability of miss detection versus threshold . . . . . . . . . . . . . . 49

Figure 5.11 Average probability of detecting white spaces versus probability of miss

detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

Figure 5.12 Distribution of sensors and nodes . . . . . . . . . . . . . . . . . . . . . 51

Figure 5.13 ROC of cooperative spectrum sensing . . . . . . . . . . . . . . . . . . 52

Figure 5.14 PW versus PMiss for cooperative spectrum sensing with different thresh-

old values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

Figure 5.15 PD versus number of sensors for cooperative spectrum sensing . . . . . 54

Figure 5.16 Average PD versus PFA for cooperative spectrum sensing with different

number of sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

Figure 5.17 Average PW versus PMiss for cooperative spectrum sensing with differ-

ent number of sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

Figure 5.18 PD versus different search distances in cooperative spectrum sensing . . 56

Figure 5.19 Average PD versus PFA with different search distances for cooperative

spectrum sensing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

Figure 5.20 Average PW versus PMiss with different search distances for coopera-

tive spectrum sensing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

x

xi

List of Acronyms

CR Cognitive Radio

SUs Secondary Users

PUs Primary Users

PSD Power Spectrum Density

CM Capon Method

FCC Federal Communications Commission

USA United States of America

Ofcom Office of Communications

UK United Kingdom

ITU International Telecommunications Union

FCA Fixed Channel Allocation

ISM Industrial, Scientific, and Medical

DSA Dynamic Spectrum Access

TV Television

VHF Very High Frequency

UHF Ultra High Frequency

TVWS TV White Spaces

CRD Cognitive Radio Device

UWB Ultra Wide Band

SDR Software Defined Radio

WSDs White Space Devices

xii

WiFi Wireless Fidelity

CRNs Cognitive Radio Networks

BSs Base Stations

APs Access Points

QoS Quality of Service

SNR Signal to Noise Ratio

FC Fusion Center

RF Radio Frequency

LO Local Oscillator

L-R Longley-Rice

WSDB White Space Database

WSDs White Space Devices

SAPs Secondary Access Points

H0 Null hypothesis

H1 Alternative hypothesis

PDF probability density function

PFA Probability of False Alarm

PMiss Probability of Miss Detection

NP Neyman Pearson

PD Probability of Detection

γ Threshold

ROC Receiver Operating Characteristic curve

AWGN Additive white Gaussian noise

BPF Band-Pass Filter

ADC Analog to Digital Converter

FFT Fast Fourier Transform

NBS Nash Bargaining Solution

xiii

MA Moving Average

ARMA Auto Regressive Moving Average

MUSIC Multiple Signal Classification

FIR Finite Impulse Response

CCC Common Control Channel

xiv

Chapter 1

Introduction

Wireless communication is an important technology that changed the way we live. Many

things we use every day depends on wireless communication such as our cellular phones, wire-

less keyboards, and remote car starters. Crisis and emergency situations can be handled very

quickly with the help of wireless communication. The fast growth in wireless communication

services and in the number of wireless users, lead to an increasing demand for more spectrum

bands. However, the radio spectrum that can be used for wireless communication is limited by

nature, and most of its spectrum bands have already been allocated using long term licenses.

So, it is hard to find vacant bands.

A chart of the electromagnetic spectrum which contains all types of electromagnetic ra-

diation is shown in Figure 1.1 [54]. Radio spectrum is a part of electromagnetic spectrum

corresponding to radio frequencies lower than 300 GHz. Electromagnetic waves in this fre-

quency range are called radio waves and can be used for wireless communication and other

different applications.

The usage of the radio spectrum is typically regulated by government agencies around

the world, such as the Federal Communications Commission (FCC) in the United States of

America (USA), Industry Canada in Canada, and Office of Communications (Ofcom) in the

United Kingdom (UK). These agencies are supervised by an international standards body called

1

CHAPTER 1. INTRODUCTION 2

Figure 1.1: The electromagnetic spectrum [54]

the International Telecommunications Union (ITU). They divide the radio spectrum range into

non-overlapping frequency bands. The assignment of these frequency bands is made based on

a Fixed Channel Allocation (FCA) policy, in which the usage of each band is exclusively given

to a single provider or to one wireless application. Some bands like the Industrial, Scientific,

and Medical (ISM) bands are unlicensed. They are allocated free of charge and can be used

by several applications like cordless phones, microwave ovens, medical diathermy machines,

and wireless computer networks. There are also licensed bands that can be allocated to various

operators such as cellular telephone operators or broadcast television stations using long term

licenses.


Figure 1.2: Spectrum utilization [7]

1.1 Thesis Motivation

1.1.1 White Spaces (Spectrum Holes)

The need for spectrum bands that can facilitate the implementation of emerging wireless ser-

vices, increase on a daily basis. However, most of the suitable spectrum bands are either sold

or leased using long term licenses. Recent studies show that the allocated spectrum is not effi-

ciently used [21, 5, 27, 38]. Figure 1.2 [7] shows the distribution of the signal strength versus

frequency for a part of the radio spectrum measured at downtown Berkeley. The distribution

indicates that some frequency bands are intensely used, while others are barely used.

Moreover, measurements done by FCC [21] in Atlanta, New Orleans, and San Diego, show

that the FCA policy led to huge temporal and spatial disparities ranging from 15% to 85% in

the allocated spectrum usage, which make the spectrum inefficiently utilized. Recent measure-

ments done in different places like Vietnam [5], Singapore [27], and HULL-UK [38] also show

similar results. Figure 1.3 [3] shows the spectrum parts that are unused at a specific time or


Figure 1.3: Concept of spectrum hole [3]

location, which are called white spaces or spectrum holes. The blocks represent frequencies

in use, while the empty regions represent temporally unused frequencies. Dynamic Spectrum

Access (DSA) techniques that will be covered in Chapter 2, allow the unlicensed use of these

spectrum holes.

1.1.2 TV White Spaces

After switching the TV transmission from analog to digital, big parts of frequencies in the Very

High Frequency (VHF) and Ultra High Frequency (UHF) bands have been freed up. These un-

used frequency portions on the TV broadcasting are referred to as TV White Spaces (TVWS).

Frequencies in the TV broadcast bands benefit from high bandwidth, long transmission ranges

and better building penetration. Figure 1.4 shows an example for the TV transmitters coverage

in Canada. Each circle represents the coverage area of one transmitter located at the center of

the circle. After switching to digital TV a lot of these transmitters are no longer used, resulting

in a large number of TVWS.


Figure 1.4: Canadian TV transmitters coverage

1.1.3 Countries Utilizing White Spaces

Although the USA was not the first country to switch to digital TV, it became a leader in making

the decision to utilize white spaces. The FCC allowed the usage of TVWS by unlicensed radio

transmitters [16]. These unlicensed transmitters have to avoid interfering with licensed users

[19]. The FCC rules state that the unlicensed devices must both consult a database and perform

spectrum sensing to determine white spaces in their locations. The database is queried by pro-

viding a certain location, then it returns a list of the available channels at that location. While,

the spectrum sensing is used to ensure that licensed devices such as wireless microphones, that

may not be registered in the database, are also protected.

The FCC issued a Memorandum Opinion and Order on September 23, 2010 [17], which

states the final regulations that the unlicensed devices should follow for utilizing white spaces.

The mandatory requirement for using spectrum sensing was eliminated. The reason for remov-

ing spectrum sensing is that the FCC assigned two channels for the exclusive use of wireless

microphones that are not guaranteed to be registered on the database. Meanwhile the FCC

encourage the development in spectrum sensing as it believes that it can enhance the spectrum

efficiency.

The UK was the second country to utilize white spaces , when Ofcom published a docu-


ment in September 2011 expressing the intention to support commercial utilization of white

spaces [44]. In that document, Ofcom mentioned that their approach is based on geo-location

database rather than alternative approaches like sensing or beacons. Ofcom indicated that the

UK supports the development of a harmonized European approach for White Space Devices

(WSDs), as it believes that the consumers can obtain the largest benefits from that approach.

In August 2011, Industry Canada started a consultation to address the challenges and bene-

fits of using TVWS [9]. Depending on the results of this consultation process Industry Canada

made the decision to allow the usage of TVWS devices in Canada in April 2013 [8].

1.2 Problem Statement

A spectrum scarcity problem appeared as a result of the gap between the available and required

radio spectrum bands. As it is not possible to find new spectrum bands, the only way to

overcome the spectrum scarcity problem is to find better ways to use the available spectrum.

A closer look to the allocated spectrum showed that, the FCA scheme causes the spectrum

to be inefficiently utilized over time and space. Hence, it is not suitable to accommodate the

increasing requirements for wireless communication services. Innovative techniques that can

make efficient use of the allocated spectrum need to be developed to facilitate the deployment

of emerging wireless application.

Dynamic spectrum access techniques have arisen to overcome the drawbacks of the FCA

scheme, by making efficient use of the allocated spectrum. DSA techniques enable unlicensed

Secondary Users (SUs) to make use of the licensed spectrum in an opportunist manner, pro-

vided that they do not interfere with the licensed Primary Users (PUs). Since the SUs are

allowed to use the spectrum when PUs are not using it, so they need a special kind of devices

that enable them to identify the used and unused spectrum bands in their location.

Cognitive radio technology is the enabling technology for DSA, which can help SUs to

identify spectrum holes in their location. Thus for a SU to be able to utilize the spectrum holes


in the allocated spectrum, it has to be equipped with a Cognitive Radio Device (CRD), which

is a device that has CR capability. The key function for any CRD is called spectrum sensing,

which allow SUs to determine the presence or absence of PUs signals.

The spectrum sensing function is a very critical and challenging function and can greatly

affect the spectrum utilization. When performing spectrum sensing we have to compromise

between protecting PUs from interference and achieving efficient utilization of the spectrum.

The performance of the spectrum sensing function is measured by the performance of the

detection technique used to detect the spectrum status. A miss detection for a PU signal results

in a SU transmission on the same channel which will cause interference to the PU. Falsely

detecting the presence of a PU signal in an empty channel would result in underutilization

of the spectrum. Thus, it is extremely important to select a reliable detection technique that

can maximize the spectrum utilization and at the same time provide interference protection for

PUs.

1.3 Contribution

The contribution of this thesis are twofold:

1. First, we propose a spectrum sensing approach based on Capon PSD estimation method.

The proposed method efficiently estimates the spectrum from a finite length of received

data samples. The estimated spectral is then used for detecting spectrum holes. The

effect of changing the signal power threshold value on the detection performance of

the proposed method is investigated. A performance comparison is made between the

proposed spectrum sensing based on the CM and that based on the conventional Peri-

odogram method.

2. Second, we propose a cooperative spectrum sensing approach that can be used to over-

come the hidden node problem. It is also used for helping devices with no CR capability

to accurately determine white spaces in their location. Those devices depend on the PSD


estimation of neighboring sensors to detect spectrum holes in their location. The effect

of different key parameters such as threshold value, number of sensors, search distance

on the performance of the proposed approach was examined.

1.4 Thesis Organization

The remainder of this thesis is organized as follows:

Chapter 2, discusses the concept of DSA, which is used to overcome the spectrum scarcity

problem, with a detailed classification of its access models. An overview of the CR technology,

its definition, and the characteristics of the CRD is also provided. Followed by a description of

the CR network architecture, its operation, and priority of users operating on that network.

Chapter 3, covers the four CR management functions with a detailed description of each

function. Followed by the challenges that we may face and the processes or the methods that

can be used to implement these function. The contents of Chapter 3 were published in the

fourth international conference on advances in cognitive radio, COCORA 2014.

Chapter 4, formulates the spectrum sensing problem as a binary hypothesis test problem

and discusses the errors that can happen when we are deciding which hypothesis has occurred.

The performance metrics for any detection technique is also discussed. Then a description

for the conventional energy detection technique and its implementation in time and frequency

domain was covered. After that we give an overview about the PSD estimation techniques

which is the main part of our thesis. A special focus is given to the Periodogram and Capon

PSD estimation methods, which will be used on the simulation.

Chapter 5, contains the implementation of a spectrum sensing function based on Capon

and Periodogram methods using Matlab simulations. Different scenarios are discussed and

illustrated by simulation results. After that a cooperative spectrum sensing scenario based on

the Capon PSD estimation method is also implemented.

Chapter 6, concludes the thesis and provides recommendations for future work.

Chapter 2

Dynamic Spectrum Access and Cognitive

Radio Technology

2.1 Dynamic Spectrum Access

The limited spectrum availability and inefficient spectrum utilization, in addition to the in-

creasing demand for radio spectrum bands causes a spectrum scarcity problem [23, 3]. DSA

techniques have appeared to overcome this problem, by making efficient use of the allocated

spectrum. DSA techniques allow the spectrum to be shared and reused among different wire-

less applications on a negotiable or opportunistic basis. DSA techniques can be classified into

three categories which are shown in Figure 2.1 [62].

2.1.1 Dynamic Exclusive Use Model

The dynamic exclusive use model allows the licensees to exclusively use certain spectrum

bands which is similar to the concept of FCA. However more flexibility is given for the li-

censees, which can use one of the two approaches called spectrum property rights and dynamic

spectrum allocation to efficiently utilize the spectrum. In spectrum property rights, the license

holders can use the spectrum for different kind of services. They can even lease or trade their

9

CHAPTER 2. DYNAMIC SPECTRUM ACCESS AND COGNITIVE RADIO TECHNOLOGY 10

Figure 2.1: Dynamic spectrum access classification [62]

assigned spectrum with various operators. In dynamic spectrum allocation, the spectrum can

be shared among different services based on the observed traffic statistics variation of these

services in time and location.

2.1.2 Open Sharing Model

In this model, the spectrum is shared among users who have equal priorities in accessing the

spectrum. The model is adopted by wireless systems sharing the ISM band like WiFi networks,

microwave ovens and Blue-tooth devices.

2.1.3 Hierarchical Access Model

Unlike the open sharing model, the users in the hierarchical access model have different pri-

orities in accessing the spectrum. There are two types of users PUs and SUs. The PUs or

the licenses owners, have the absolute privilege to use the spectrum. On the other hand, the

SUs who do not have licenses, can only use the spectrum in a non-intrusive manner. Two ap-

proaches can be used by SUs to access the spectrum. The spectrum underlay approach, which

permits PUs and SUs to transmit concurrently with certain restrictions on the amount of power

transmitted by SUs. The SUs transmitted power must be kept below the noise floor allowed

by PUs. This can be achieved by using ultra wide band (UWB) technology, which maintains


low transmission power by spreading the transmitted signal over a wide frequency band. This

approach is suitable for short range, high bandwidth communications. The spectrum overlay

approach does not have restrictions on the amount of transmitted power. However, there is a

limitation on when and where you are allowed to use the spectrum. SUs can utilize the spec-

trum in an opportunistic manner, so they have to determine the presence or absence of PUs and

try to exploit the white spaces for their transmission. As the white spaces differ based on the

location, frequency and sensing time, SUs need devices that have the ability to detect whether

the spectrum is being utilized at a specific location, frequency and at a certain time [3].

2.2 Cognitive Radio

Cognitive radio, first proposed by mitola [41], is the enabling technology for DSA. It is a

wireless communication technology based on Software Defined Radio (SDR), which helps

SUs identify spectrum holes in their locations.

A device that uses the CR technology to determine white spaces on its location is called a

CRD. This device has two main characteristics [3, 23] illustrated in Figure 2.2 [32]. The cog-

nitive capability allows the device to sense (observe) the medium, determine (learn) available

spectrum bands and then make a decision on which channel to use. Re-configurability enables

the CRD to adjust its software (communication protocol) and hardware (radio frequency front

end and antenna) to operate on the selected channel.

2.2.1 Cognitive Radio Network Architecture

Cognitive Radio Networks (CRNs) are unlicensed networks formed only from SUs that exploit

spectrum holes for their transmission. These networks can be classified based on their archi-

tecture [10, 37] as infrastructure-based and ad hoc networks. Figure 2.3 [10] shows an example

of infrastructure-based networks. These kind of networks have secondary Base Stations (BSs)

or secondary Access Points (APs) that can coordinate the communication between SUs in their


Figure 2.2: Cognitive radio concept [32]

coverage areas. Secondary APs can be connected together through a wired network (core net-

work) to facilitate the communication between SUs in different coverage areas. Figure 2.4 [10]

displays an example for the ad-hoc or distributed CRNs. In this type of networks there is no

infrastructure, SUs communicate directly with each other using available white spaces in their

locations.

2.2.2 Cognitive Radio Network Operation

CRNs either infrastructure-based or ad hoc networks usually work inside the transmission

range of primary networks as shown in Figure 2.5 [55]. Primary networks or licensed net-

works refer to already existing infrastructure-based wireless networks like mobile networks


Figure 2.3: Infrastructure-based CRN [10]

Figure 2.4: Ad hoc CRN [10]


Figure 2.5: CRNs operation inside the coverage area of primary networks [55]

that are allocated certain frequency bands for their operation. When the CRN operates in the

licensed band, the priority is for PUs operations. SUs that utilize spectrum holes, have to vacate

the channel whenever a PU appears.

Chapter 3

Cognitive Radio Management Functions

The CRD has to perform four CR management functions to manage spectrum holes in its loca-

tion, avoid interference with PUs, coexist with other devices, and efficiently use the spectrum

[3, 55]. These functions are shown in Figure 3.1 [42], which are white space determination,

spectrum decision, spectrum sharing, and spectrum mobility.

Figure 3.1: Cognitive radio functions [42]

The main function for any CRD is to determine white spaces at a certain place and specific

time. After that, the CRD has to select the best channel for its operation, which is called spec-

15

CHAPTER 3. COGNITIVE RADIO MANAGEMENT FUNCTIONS 16

trum decision. The channel selection is usually done based on specific criteria such as avoiding

interference with other CRDs or requiring a certain Quality of Service (QoS). As the spectrum

is shared among multiple SUs, spectrum sharing is required to coordinate how SUs can coexist

and access the same spectrum without interfering or colliding with each other. Spectrum mo-

bility plays an important role in preserving the PUs priority to access the spectrum. A SU has

to stop its transmission and switch to another available channel if a PU appears on the same

channel.

3.1 White Spaces Determination

The key function for any CRD is to determine spectrum holes in its location which vary in time

and space. Figure 3.2 [42] displays the two approaches used for identifying spectrum holes.

3.1.1 Spectrum Sensing

The first approach is called spectrum sensing, which allows the CRD to sense the spectrum and

determine its availability for use. In spectrum sensing, the CRD depends on its own sensing

measurement, to find the spectrum holes in its current location. To overcome the time varying

spectrum holes the device should periodically sense the medium and adapt to any variations in

the sensed spectrum usage. Figure 3.2 displays the different detection techniques that can be

used to perform the spectrum sensing function.

Primary Transmitter Detection

In primary transmitter detection, the medium is considered available if the CRD cannot hear the

signal sent from any primary transmitter. Several methods can be used for primary transmitter

detection such as matched filter detection [36, 51, 49], feature detection [22, 40, 47], and energy

detection [60, 4, 45, 58].


Figure 3.2: Methods used for white spaces determination [42]

• Matched filter detection is the optimal detection technique in the presence of stationary

Gaussian noise. The matched filter operates by correlating the pattern that needs to be

detected (known information about the signal) with the received signal. If the magnitude

of the resulting signal is above a certain threshold; the medium is considered busy other-

wise, the medium is free. The matched filter is a fast detection technique, but it requires

previous knowledge about the signal to be detected.

• Feature detection or cyclostationary feature detection depends on detecting the cyclosta-

tionary (built-in periodicity) feature of the modulated signal for detecting the presence of

a signal. The cyclostationary feature detector can distinguish the PU signal from noise.


This kind of detector is more robust against the uncertainty in the noise power, but re-

quires more observation time and is computationally complex.

• Energy detection or radiometer does not require prior information about the primary

transmitter signal. In this detection technique, the CRD measures the energy of the

received signal and compares it with a threshold. The medium is considered busy if the

measured energy is greater than the threshold , otherwise the medium is considered free

and can be used by SUs. The energy detector is the most popular detection technique

due to its simple implementation and because it does not require prior information about

the PU signal like the matched filter or the feature detection techniques. However, one of

the concerns of using energy detection is that it cannot differentiate whether the detected

signal is from a primary transmitter or from a secondary transmitter. Another concern is

adjusting the threshold value of the detector as this value is affected by the noise level.

Energy detector will be discussed in details in Chapter 4.

Hidden Node Problem

The main problem of the primary transmitter detection techniques is the hidden node problem

[46]. In this problem, the CRD may be shadowed from detecting the signal of a primary

transmitter, due to the presence of an object that block the transmitter signal, as shown in

Figure 3.3 [3]. As a result of the hidden node problem, the CRD will fail to identify the

presence of a the PU in the channel and hence, will interfere with that user. To overcome the

hidden node problem cooperative spectrum sensing or cooperative detection can be used.

Cooperative Spectrum Sensing

When performing spectrum sensing, SUs may suffer from unexpected multipath fading and/or

shadowing. These multipath fading and shadowing issues can significantly affect the detection

performance. Cooperative spectrum sensing is an effective way to mitigate the effect of these

issues, improve the detection performance and can be also used as a solution for the hidden


Figure 3.3: Hidden node problem [3]

node problem. The idea behind cooperative spectrum sensing is to exploit the observations of

CRDs in different locations to obtain better information about the spectrum. Each CRD will

share the sensed information on its location with other CRDs to make a combined decision

which will be more accurate than individual decisions. Cooperative spectrum sensing can be

done in a centralized or in a distributed manner as shown in Figure 3.4 [1]

• In centralized method, CRDs send the sensed information to a central CRD called fusion

center (FC). The FC is responsible for analyzing the collected information and determin-

ing white spaces that can be used by SUs. This method requires a point to point link

between each CRD and the FC.

• In distributed method, each CRD sends the sensed information to other CRDs. Each

device is responsible for combining its own measurement with the information received

from other devices to determine spectrum holes in its location.

Cooperative spectrum sensing, either centralized or distributed, can enhance the detection ca-

pability, but comes with the cost of additional overhead.


Figure 3.4: Cooperative sensing methods (a) Centeralized (b) Distributed [1]

Primary Receiver Detection

As the interference always happens at the receiver, so the most efficient technique in deter-

mining spectrum holes is to detect the presence of the primary receivers. The CRD needs to

detect primary receivers in its communication range and avoid interfering with them. Primary

receiver detection is not an easy process. Usually primary receivers such as televisions or cellu-

lar phones are passive, which makes it harder for the CRD to detect their presence or determine

their location. One way to allow the CRD identify the existence of a primary receiver [59], is

by detecting the leakage power emitted by the RF front end of the the primary receiver’s Local

Oscillator (LO). This method is currently feasible only in TV receiver detection and still has

some problems like the long sensing time and short detection range [25].

Interference Temperature Metric

Interference temperature is a model introduced by the FCC [15] to accurately measure and limit

the amount of interference at the receiver. Interference temperature dictates the cumulative

amount of interference from all the undesired RF energy sources that exist at a receiver at any

point of time. Interference temperature provides a higher protection for the receiver against


harmful interference. A CR user can use the channel at any place and time as long as it does

not surpass the limit on interference temperature which is the concept of underlay spectrum

sharing that we will discuss in Section 4.3.3 .

3.1.2 Geo-Location Database

In the Geo-location database approach, the CRD does not use spectrum sensing to determine

white spaces; instead it depends on an up-to-data database of incumbents. The database stores

information about all primary transmitters and their locations. It also stores terrain information.

The database uses these information to calculate white spaces at the CRD location.

According to FCC regulation [19], the CRD should have the capability to determine its lo-

cation, and a way to connect to the Internet to access the database. The process of determining

white spaces is done through several steps. First, the CRD has to provide identifying infor-

mation to register with the database. Then, the CRD determines its location and send it to the

database, which uses some propagation models to calculate white spaces at that location. After

that, the database will send a list of the available spectrum holes to the user. The database may

also inform the device with the maximum allowable transmit power for its operation. In this

case, the device can use its adaptable power control to ensure that the transmitting power does

not exceed the maximum allowed value. Using a database to calculate spectrum holes over-

comes the problem of false alarm that can happen with spectrum sensing and thus provides

more efficient use of the spectrum.

Microsoft presented an approach for a geo-location database called SenseLess: A Database

Driven White Spaces Network [43]. The SenseLess architecture consisted of a logically cen-

tralized entity called SenseLess Service. Base stations and CRDs are connected to this central

entity, which is responsible for determining white spaces for any given location. Two compo-

nents mainly constitute the SenseLess service, the back-end store and the SenseLess engine.

The back-end store consisted of a terrain server and a database of incumbents, such as TV trans-

mitters (their location, antenna height, transmission power) and wireless microphones. The


database can also cache the computed white spaces for different locations. The terrain server

store high resolution terrain elevation data, which can be obtained from one of the publicly

available terrain maps. Sophisticated signal propagation modeling is used by the SenseLess

engine to compute spectrum holes at any given location. Results showed that the Longley-Rice

(L-R) with terrain propagation model [14] provides accurate results when determining white

spaces at any given location.

3.2 Spectrum Decision

Spectrum decision is the capability of a CRD to choose the most appropriate channel for its

operation. Channel selection should satisfy the QoS requirements of SUs and at the same time

ensures that they do not interference with PUs. The spectrum decision function consists of

three processes called spectrum characterization, spectrum selection and CR re-configurability

[37, 2].

• Spectrum characterization is the first step to be performed after determining spectrum

holes. These spectrum holes are characterized based on the PUs activities and the condi-

tions of the radio environment. Modeling PUs activities based on the historical informa-

tion of previous spectrum usage, can help in predicting the future usage of the spectrum.

The condition of the radio environment is another factor that can be used to character-

ize the channel based on the number of users utilizing the same channel, the amount of

interference, and the strength of the received signal.

• Spectrum selection is used to select the best characterized channel that satisfies the QoS

requirements of the SU.

• CR re-configurability adjusts the transceiver parameters of the CRD to communicate on

the selected channel.


3.3 Spectrum Sharing

The spectrum sharing function is used to coordinate the coexistence of CRDs in a shared

medium without interfering with each other. Figure 3.5 [42] displays a classification for spec-

trum sharing according to architecture, scope, spectrum allocation behaviour, spectrum sharing

models and spectrum access techniques [3] .

Figure 3.5: Spectrum sharing categorization [42]

3.3.1 Spectrum Sharing Architectures

The spectrum sharing architecture can be centralized or distributed. In centralized spectrum

sharing, a central unit is responsible for allocating the spectrum and controlling access to it. In

distributed spectrum sharing, spectrum allocation and access is done by each SU independently.

3.3.2 Spectrum Allocation Behaviour

The spectrum allocation behaviour can be cooperative or non-cooperative spectrum sharing. In

the cooperative spectrum sharing, the CRDs cooperate together to avoid interference with each

other. Each CRD adjusts its transmission power taking into account other devices transmission.

In the non-cooperative spectrum sharing, each CRD behaves in a selfish manner. The CRD will

transmit without considering if its transmission will affect other devices transmission. Thus,

in the non-cooperative case there will be high interference between CRDs, which in turn will

reduce the spectrum utilization.


3.3.3 Spectrum Access Techniques

The spectrum access techniques can be classified as overlay spectrum sharing and underlay

spectrum sharing. In the overlay spectrum sharing, SUs can opportunistically use the spectrum

when it is not used by PUs. In the underlay spectrum sharing, SUs can transmit at the same

time with PUs as long as their transmission is below the noise floor of PUs. In this case, SUs

use spread spectrum techniques and can only transmit over short range.

3.3.4 Spectrum Sharing Scope

The spectrum sharing scope in infrastructure-based CRNs can be classified to intra-cell spec-

trum sharing and inter-cell spectrum sharing [33]. The intra-cell spectrum sharing is concerned

with spectrum sharing between SUs in the same cell. The inter-cell spectrum sharing is con-

cerned with spectrum sharing between different cells.

3.3.5 Spectrum Sharing Models

There are two spectrum sharing models called, exclusive allocation model and common use

model [33]. In the exclusive allocation model, each SU is allocated a different channel to

mitigate the interference between SUs. This model is optimum in maximizing the capacity of

the network, but it provides unfair resource allocation in networks that have limited spectrum

availability. Although the focus of this approach is on spectrum allocation, power allocation

has to be considered to avoid interfering with PUs. In the common use model, several SUs

can simultaneously use the same channel by adjusting their transmission power to minimize

interference. This model is preferred in networks with limited spectrum holes; as it can provide

fairness in the allocation of the available spectrum. However, the achieved capacity is lower

than that of the exclusive model.

Several research papers have been proposed to address the spectrum sharing problem in

CRNs [28, 26]. These papers usually have two main differences which are the objective of the


network and the used spectrum sharing model.

In [28], the authors presented a combined power/channel allocation method in a WiFi-like

spectrum sharing scenario in TVWS. Three types of SUs were defined which are protected,

interfered and out of range SUs. The network objective is to increase the number of supported

SUs and at the same time minimize the interference between the different WiFi CRNs. The

Nash Bargaining Solution (NBS) was used to allocate transmit power for each Secondary Ac-

cess Point (SAP). SAPs cooperate by exchanging information through relaying nodes. These

relaying nodes are the interfered SUs that can hear from more than one SAP. SAPs compete on

their transmission powers to maximize the number of supported SUs. SAPs have to decrease

their power until there is no overlapping between their coverage areas. Two stage of cooper-

ation were made. In the first stage, only neighboring SAPs cooperate and compete for power

control, while in the second stage, all the next hop neighboring SAPs can cooperate. When the

SAPs are highly overlapped, the algorithm can switch to channel allocation instead of power

allocation to improve the performance of the network. The switch can be done if the number

of interfered SUs is above a certain number. Simulation results showed that the number of

iteration required to reach optimality is decreased by the cooperation of SAPs. It also showed

that the proposed sharing algorithm succeded in increasing the number of supported SUs, but

it comes with the cost of decreasing the average throughput per SAP.

A downlink channel assignment and power control for an infrastructure-based CRN was

implemented in [26]. The opportunistic spectrum access problem was formulated as a non-

cooperative game in which the game players are the base stations. Each base station bargain

to increase the number of supported SUs. Channel allocation was done by the base stations,

which randomly assign channels to users. A distributed power allocation is then applied us-

ing the Iterative Water Filling algorithm. Results showed that the pure non cooperative game

might have multiple Nash equilibrium points [20] that are undesirable and may lead to non-

convergence. To obtain better results, the NBS which requires the cooperation of base stations

was applied. Simulation results showed that a unique optimal solution was achieved by using


the NBS.

3.4 Spectrum Mobility

Spectrum mobility is the process of performing seamless transition from one channel to another

available channel. After a SU selects the channel and starts transmitting on it, a PU may appear

on the same channel; in this case the SU has to move to another empty channel and vacate the

channel to avoid causing interference to the PU. The SU may also change its channel to access

another spectrum hole with better QoS. Spectrum mobility consists of two processes [12],

spectrum handoff and connection management.

• In the spectrum handoff process, the SU transfers its transmission from the current chan-

nel to another empty channel. Three events can trigger the spectrum handoff process.

The first event is the arrival of a PU in a channel occupied by a SU. The second is the

spatial movement of SUs to a place where their coverage overlap with PUs already uti-

lizing the channel. The third is the degradation of the link quality.

The spectrum handoff process consists of two phases, evaluation phase and link mainte-

nance phase. In the evaluation phase, the SU keep monitoring the environment to deter-

mine if an event that trigger spectrum handoff occurred, then the SU moves to the next

phase (link maintenance). In the Link maintenance phase, the SU pauses its transmission

on the current channel and continues the transmission on another available channel.

• Connection management process is used to compensate for the unavoidable handoff de-

lay, which happens when the SU transmission is transferred from a channel to another,

by adjusting the parameters of the protocol stack according to the existing situation.

Chapter 4

Spectrum Sensing Based on Capon PSD

Estimation

In this chapter we describe the proposed Capon PSD estimation method. We start by explaining

the spectrum sensing problem and the metrics used for measuring its performance. Then we

cover the popular energy detection technique. After that we explain how we can use the CM to

find an estimate for the PSD of the received signal, which is used for detecting spectrum holes.

We also cover the advantage of using the CM over the conventional energy detection method.

4.1 Problem Formulation

Spectrum sensing is the fundamental task of any CRD, which detect the presence or absence

of a radio frequency signal. The spectrum sensing problem can be considered as a binary

hypotheses testing problem with the following hypotheses:

H0 : Y (n) = W (n)

H1 : Y (n) = X(n) +W (n)

27

CHAPTER 4. SPECTRUM SENSING BASED ON CAPON PSD ESTIMATION 28

where the null hypothesis H0 indicates that there is no signal transmitted, only noise exist, and

the alternative hypothesis H1 indicates that there is a signal transmitted. Y (n) are the received

signal samples, W (n) are the noise samples, X(n) are the transmitted signal samples, and

n = 0, 1, 2, .., N − 1 is the sample index.

The goal of the binary hypothesis test is to decide which of the two hypotheses has occurred

by comparing the received data with a certain threshold. If we have only one received sample,

the decision can be easily taken by comparing the received sample with the threshold and

declare H1 if the received sample is greater than the threshold and H0 otherwise. However, we

usually have a sequence of data samples, and in this case we have to find the most appropriate

way to process these samples. Several detection techniques can be used to solve the binary

hypothesis testing problem. Each technique has a different strategy in processing the data

samples to obtain a test statistics. This test statistics is then compared with a threshold to

decide whether a signal exist or not. The performance of any detection technique depends

on the amount of information known about the signal and noise characteristics. If we have

less information about the signal and noise, the performance of the detection technique will

decrease. The optimal detection technique can be obtained if we have a perfect knowledge

about the signal and noise probability density functions (PDFs).

Figure 4.1 shows the two types of errors that can happen when we are deciding which

hypothesis has occurred [30]. If we decide H1 while H0 is true this is called TypeI error or

probability of false alarm PFA, and if we decideH0 whileH1 is true this is called TypeII error

or probability of miss detection PMiss. The PFA is the probability of detecting the presence of a

PU while it is actually absent. A high PFA value leads to inefficient utilization of the spectrum,

because even though the spectrum is free in reality, the SU assumes it is busy and hence will

not use it. The PMiss is the probability of declaring that the channel is free while it is actually

occupied by a PU. A high PMiss value leads to higher interference, as the SU assumes that

the channel is free, and once he starts using the channel, he will interfere with the PU already

transmitting on that channel.


Figure 4.1: Types of error in hypothesis testing

The performance of any detection technique is measured by its probability of detection PD

and PFA. The PD is the probability of detecting a PU while it is actually present. A high PD

value leads to less interference, as the SU will detect the presence of a PU transmission on

the channel, so he will not transmit on that channel. In mathematics the PD and PFA can be

expressed as:

PD = P (H1;H1)

= Pr{T > γ;H1}

= 1− PMiss

PFA = P (H1;H0)

= Pr{T > γ;H0}

where P (Hi;Hj) is the probability of deciding Hi when Hj is true, γ is the threshold, and

the plot of PD versus PFA is called the Receiver Operating Characteristic (ROC) curve, which

is the most important performance measure for the hypothesis testing problem. An efficient

detector should achieve a low PFA and high PD. As an example the IEEE 802.22 WRAN

standard requires a PFA of 0.1 and a PD of 0.9 [52].


4.2 Choosing Threshold Value

In the binary hypothesis test problem, the test statistics is compared with a certain threshold

to decide which hypothesis has occurred. Choosing the threshold value is a very critical and

challenging issue, and it can greatly affect the PFA and PMiss values. The threshold value that

is suitable for one transmitter may not be suitable for another transmitter. Since each trans-

mitter has a different signal power and transmission range. Increasing the threshold value will

decrease the PFA, but on the other hand will increase the PMiss and vice versa, so we cannot

minimize both errors simultaneously. Different criteria can be used to determine the threshold

value such as Bayesian, NeymanPearson (NP) and Minimax [56]. Most of the detectors depend

on the NP lemma for determining threshold value as it tries to maximize the PD while keeping

the PFA fixed at a constant value.

4.3 Energy Detection

Energy detection is the simplest and most popular signal detection technique used for spectrum

sensing. It can be easily implemented even if there is no information available about the pri-

mary user’s signal. The idea of energy detection is to measure the energy of the received signal

within a certain bandwidth and time period and compare it with a predefined threshold that

depends on the noise floor. If the energy exceeds the threshold the detector decides that there

is a signal transmission on the channel, otherwise there is no signal transmission and the chan-

nel can be used for SUs operation. As the signal energy is maintained in time and frequency

domain, the implementation of energy detector can be made in time and frequency domain as

well. Although the energy detector is simple and has low implementation complexity there are

some challenges associated with energy detection. These challenges include determining the

threshold value, the poor performance under low Signal to Noise Ration (SNR), and inability

to differentiate between the PU and SU signals.


Figure 4.2: Time domain representation of energy detection

4.3.1 Energy Detection in Time Domain

The first model for a time domain energy detector was presented by Urkowitz in [57]. Urkowitz

focused on the detection of a deterministic signal in the presence of band-limited additive white

Gaussian noise (AWGN). Urkowitz assumed that the structure of the signal is unknown, but

the noise variance is known. The designed energy detector consisted of three main blocks as

shown in Figure 4.2. The received signal is first pre-filtered by an ideal Band-Pass Filter (BPF)

to limit the noise bandwidth. Then in order to calculate the energy content of the signal, the

signal is passed through a squaring device followed by a finite time integrator. The integrator

integrates the squared signal over the observation period T to produce a test statistic. The

produced test statistics, which is actually the energy of the signal, is compared with a threshold

and if it exceeds the threshold the detector decides that a signal has been detected.

Mills and Prescott [39], presented six different detection models for the common wide

band energy detector. They tried to find a way to calculate the required SNR that will achieve

certain detection performance. Comparing results obtained from the derived models with ex-

act results showed that results from the models agreed with the exact results for very large

time-bandwidth product. In [13], Ciftci and Torlak analyzed the performance of the detec-

tion models presented in [39] in both AWGN and Rayleigh channels and compare the result

obtained with exact solution results. Simulation results showed that the performance of the

models is nearly equal in Rayleigh channel, and any of the models can be used as a suitable

approximation for energy detection instead of complicated exact solution. Lee and Akyildiz

[34], used the maximum a posteriori (MAP) based energy detection model to develop an op-

timal spectrum sensing framework. The developed framework works on optimizing sensing


parameters to maximize the spectrum efficiency and on the same time avoid causing interfer-

ence to PUs. In [31] Kostylev investigated the detection of a signal with random amplitude

over different fading channels such as Rayleigh, Rice, and Nakagami. The decision statistics

of the energy detector and the probability of detection expression for each fading channel has

been derived. Chen [11], also investigated the detection of a random signal in Gaussian noise,

but he made a modification to the conventional energy detector. In conventional energy de-

tector the amplitude of the signal is squared to obtain the energy. Chen replaces the squaring

operation with a positive power operation, then he made a derivation for the performance of

the improved energy detector.

4.3.2 Energy Detection in Frequency Domain

The implementation of energy detection in time domain usually suppose that the frequency

and bandwidth of the PU signal are known. However this is not the case in reality, which

encourage investigating the frequency domain implementation of energy detection. A block

diagram for measuring the energy of the signal in frequency domain is shown in Figure 4.3

[1]. First the received signal is passed through a BPF to select the frequency band of interest,

then an analog to digital converter (ADC) is used to sample the filtered signal to discrete time

samples. After that the fast Fourier transform (FFT) is taken to convert the sampled signal

from time to frequency domain. Once the signal is converted to frequency domain the energy

is calculated by squaring the coefficients and taking the average. The calculated energy is

used as the decision statistics that will be compared with a predefined threshold to decide the

presence or absence of the PU signal.

Figure 4.3: Frequency domain representation of energy detection


In the frequency domain implementation we are not just measuring energy, we are measur-

ing the energy spectral density, which shows the distribution of energy versus frequency, so we

can easily determine the frequency location of the PU signal [61]. We can calculate the energy

spectral density of a finite energy signal from the Fourier transform of its autocorrelation se-

quence [29]. However, most of the signals used in communication systems are random signals

that can be modeled by probabilistic terms. Such signals do not have finite energy, but they

have finite average power, so they can be described by their average PSD [53]. The problem is

that we cannot find the true PSD of the signal when all what we have about the signal is just

a finite set of measurements. However we can depend on estimating the PSD of the random

signal using one of the PSD estimation techniques.

4.4 Power Spectral Density Estimation

PSD estimation techniques are used to estimate the distribution of the total power over fre-

quency from a finite length of stationary data sequence. Two major methods can be used for

spectral estimation: [53] parametric and nonparametric methods. The parametric methods as-

sume that the data follows a certain structure or model such as Moving Average (MA) models

and AutoRegressive Moving Average (ARMA) models, which can be described by a number

of parameters. Then the spectral estimation problem is reduced to estimation of the parameters

in the assumed model. One example of the parametric methods is called MUltiple SIgnal Clas-

sification (MUSIC) method. The nonparametric methods do not need to have prior information

or make any assumption about the signal. The Periodogram and Capon methods are examples

of the nonparametric methods.

Parametric methods can provide more accurate PSD estimation than the nonparametric

methods, if the assumed model correctly reflects the behaviour of the data. Otherwise, which

is the more likely case the estimated PSD might not be reliable and the nonparametric methods

outperforms the parametric ones. Nonparametric methods are more suitable for the implemen-


tation of energy detector, as they do not require any information about the transmitted signal,

which matches the concept of energy detection.

4.4.1 Power Spectral Density

Consider a discrete time signal {y(t); t = 0,±1,±2, ......} consists of a stationary sequence of

random variables with zero mean E{y(t)} = 0 for all values of t. The auto-covariance function

r(k) of y(t) can be determined by:

r(k) = E{y(t)y∗(t− k)} (4.1)

where k is the time lag between two samples and * represents complex conjugate. The PSD

of the signal y(t) can be defined in two ways:

• First it can be defined as the DTFT of the auto-covariance sequence of the signal:

ϕ(ω) =∞∑

k=−∞

r(k)e−iωk (4.2)

• Or it can be defined according to the equation:

ϕ(ω) = limN→∞

E

1

N

∣∣∣∣∣N∑t=1

y(t)e−iωt

∣∣∣∣∣2 (4.3)

If we assume that the auto-covariance sequence of y(t) decays so fast, The two PSD definition

will be equivalent.

4.4.2 Periodogram

The Periodogram is the most widely used method for energy detection. The PSD estimate

using Periodogram method depends on equation (4.3) of the PSD definition. The difference is


that the expectation and limit operation in equation (4.3) were neglected because they cannot

be computed when the only known information about the signal is its samples.

∧ϕp(ω) =

1

N

∣∣∣∣∣N∑t=1

y(t)e−iωt

∣∣∣∣∣2

(4.4)

The performance or the quality of an estimator is usually measured based on two parameters

named, the bias and variance of the estimator [48]. The difference between the expected

value and the true value of the estimated parameter represents the bias of the estimator. The

variance of the estimator is a measure of how the estimated values varies around their mean.

A good estimator should have the value of its bias and variance fade when the number of data

sequences increase to be infinite.

The Periodogram is considered as a bad estimator. Since it is unbiased estimator and its

variance does not decrease even if we increase the data length. Moreover, it has a poor reso-

lution, which means that its ability to identify frequency components that are closely spaced

is not good [29]. However the resolution of the Periodogram method can be improved by

increasing the length of the observed data samples.

4.4.3 Capon

The drawbacks of the Periodogram method encourage the investigation of other spectral esti-

mation methods that can overcome the high variance and poor resolution such as the Capon

Method (CM). The CM, also known as minimum variance method [29] uses a Filter Bank

Approach (FBA) based on a data dependent BPF to estimate the PSD of the signal. The idea

behind the FBA is that if the input signal is applied to an array of BPFs, each filter is tuned to

pass certain frequency band without distortion while minimizing the power of other frequency

components in the signal. Thus, the signal can be divided into multiple components with each

component representing a single frequency sub-band of the input signal.

In the CM the signal of interest is introduced to only one BPF that has a narrow bandwidth


[53, 24]. The filter band-width can be swept over the frequency band of interest to calculate the

value of the estimated spectrum. The filter should be designed to pass the required frequency

band undistorted while minimize the power outside that band. In order to do that consider a

Finite Impulse Response (FIR) filter whose impulse response h is given by:

h = [h0 h1 ...... hm]∗ (4.5)

where m is a positive integer representing the length of the impulse response of the filter.

If the data sequence y(t) is introduced as an input to the filter, then the filter output in

response to the input data at time t can be given by:

yF (t) =∑k=0

hky(t− k)

= h∗

y(t)

y(t− 1)

...

y(t−m)

(4.6)

The power of the filtered signal yF (t) can be written as:

E{|yF (t)|2

}= h∗Rh (4.7)

where R is the covariance matrix of the input data vector and is equivalent to:

R = E

y(t)

y(t− 1)

...

y(t−m)

[y∗(t).....y∗(t−m)]

(4.8)

Our goal is to determine how the output power calculated above is distributed over frequency.


In order to do that, we need to look at the frequency response of the filter, which shows how

the filter will react to a sinusoidal input of the form e−jω, where ω represents the frequency.

The frequency response of the filter is given by:

H(ω) =m∑k=0

hke−iωk = h∗a(ω) (4.9)

where

a(ω) = [1 e−iω ..... e−imω]T (4.10)

We want to design the filter to be as selective as possible for a certain frequency band centered

around frequency ω. So, we can think of minimize the total power at the filter output sub-

ject to the constraint of passing the frequency band centered around ω without distortion and

suppressing other frequencies. This is equivalent to the optimization problem [53]:

minhh∗Rh subject to h∗a(ω) = 1 (4.11)

The solution to this optimization problem is given by [53]:

h = R−1a(ω)/a∗(ω)R−1a(ω) (4.12)

If we substitute for the value of h in equation (4.7) we can find that the PSD of the input

sequence y(t) at the output of the BPF with center frequency ω can be written as:

∧ϕc(ω) ' 1/a∗(ω)R−1a(ω) (4.13)

4.5 System Model

Consider a number of primary transmitters (tx) with fixed locations operating inside an area

(A). Each transmitter can transmit on a number of channels (ch), and each channel operates


on a different frequency (fc). The transmitters can randomly transmit in all or some of the

channels. In order to efficiently utilize the spectrum, a number of SUs with CR capability are

distributed in the area. Our goal is to allow SUs to perform spectrum sensing based on the

Capon and Periodogram PSD estimation methods and see which method has a better detection

performance.

We assume that the transmitters are transmitting a continuous time signal in the form:

s(t) = A(t)eiωt + n(t) (4.14)

where A(t) represents the signal amplitude, which is a complex Gaussian random variable

with zero mean and variance σ2s , n(t) is the noise which is also a complex Gaussian random

variables with zero mean and variance σ2n. The received signal strength at any location can

be calculated using the free space path loss model. According to this model, the path loss

is proportional to the separation distance between the transmitter and receiver raised to some

power. The relation between the transmitted and received power can be given by Friis free

space equation [50]:

Pr(d) =PtGtGrλ

2

(4π)2d2L(4.15)

where Pt is the transmitted power, Pr(d) is the received power as a function of distance, Gt

is the transmitter antenna gain, Gr is the receiver antenna gain, Gt and Gr are dimensionless

quantities, d is the separation distance in meters between the transmitter and receiver, L is the

system loss factor not related to propagation (L >= 1), and λ is the wavelength in meters

which is related to the carrier frequency according to the equation:

λ =c

f=

2πc

ωc

(4.16)

where c is the speed of light given in m/s, f is the carrier frequency in Hz, ωc is the carrier

frequency in rad/s.


Figure 4.4: Spectrum sensing steps

In order to perform spectrum sensing the CRD has to perform some steps illustrated by

Figure 4.4. First the device senses the medium to determine the strength of the received signal

on its location, which can be calculated by making use of equation 4.15 as follow:

Sr(d) = St

√GtGrλ2

(4π)2d2L(4.17)

where St is the transmitted signal, Sr(d) is the received signal, and d is the distance between

the transmitter and receiver. The distance can be calculated using the equation of the euclidean

distance between two points [18], which is given by:

d =√

(xt − xr)2 + (yt − yr)2 (4.18)

where (xt, yt) are the transmitter coordinates and (xr, yr) are the receiver coordinates.

After sensing the medium the device needs to process the received signal samples (sensed

signal) to obtain a test statistics. In our model we use the Capon and Periodogram methods to

process the received signal samples. These methods provide an estimation for the received PSD

at the SU location, calculated using equation 4.13 for Capon and equation 4.4 for Periodogram.

The estimated PSD is used as the test statistics for the hypothesis test, which is compared with

a predefined threshold to identify busy channels and white spaces at the SU location.

Chapter 5

Simulation and Results

In this chapter, the performance of the Capon and Periodogram methods introduced in Chapter

4 are evaluated through simulations. Three different scenarios are considered: 1) determining

the operating frequency of primary transmitters, 2) measuring the performance of the Capon

and Periodogram methods, 3) cooperative spectrum sensing based on the Capon method. All

the simulations are conducted using the MATLAB software. For all the three scenarios, the

values of the parameters listed in the table below are set to the following default values:

Parameter name Parameter valueSimulation area A = 1 km2

Number of transmitters tx = 3Transmitter antenna gain Gt = 1Receiver antenna gain Gr = 1system loss L = 1propagation speed c = 3 ∗ 108m/sNumber of samples N = 1000

Table 5.1: Common parameters for the three scenarios.

40

CHAPTER 5. SIMULATION AND RESULTS 41

5.1 Determining the Operating Frequency of Primary Trans-

mitters

In the first scenario, we investigate the ability of the Capon and Periodogram methods to de-

termine the operating frequency of primary transmitters. Assume that we have three primary

TV transmitters distributed as shown in Figure 5.1, and each transmitter transmits only on one

channel. The TV transmitters are transmitting on frequencies fc = 57, 69, 79MHz.

Figure 5.1: SUs distribution with respect to primary transmitters

To be able to estimate the PSD at the locations of the two SUs, we start by calculating the

strength of the received signal from each transmitter using Equation 4.17. The total received

signal at the SU location is actually the summation of the received signals from the three

transmitters given by:

Sr(SU) =3∑

tx=1

Sr(tx) (5.1)

where Sr(SU) is the total received signal at the SU location and Sr(tx) is the received signal


Figure 5.2: PSD at the location of SU1 for high SNR

from the tx transmitter calculated using Equation 4.17. The total received signal is the signal

sensed by the CRD which is used as the input for both the Capon and Periodogram methods.

The PSD estimation using the Periodogram method is calculated using Equation 4.4 which

relies on the Fast Fourier Transform (FFT) of the input data sequence. On the other hand, the

CM estimates the PSD using Equation 4.13.

Figure 5.2 plots the estimated PSD versus frequency for both the Capon and Periodogram

methods measured at SU1. The figure illustrates the ability of the Capon method to determine

the operating frequency of the three primary transmitters, while it is hard to identify them in

case of Periodogram due to the excessive amount of noise. Figure 5.3 displays the effect of

decreasing the SNR on the estimated PSD at SU1. It is clear from the figure that as the noise

level increase, the CM is still able to determine the operating frequency of the three primary

transmitters, while it becomes harder in case of Periodogram. Similar results are obtained for

SU2 which are shown in Figures 5.4 and 5.5.


Figure 5.3: PSD at the location of SU1 for low SNR

Figure 5.4: PSD at the location of SU2 for high SNR


Figure 5.5: PSD at the location of SU2 for low SNR

5.2 Measuring the Detection Performance

In this scenario, we measure the ability of the Capon and Periodogram methods to detect busy

channels and white spaces at the SUs locations. In order to have white spaces in the simula-

tion area, we assume that each transmitter has a fixed number of channels, and each channel

operates on a different frequency. The transmitters can randomly transmit in some or all of

the channels. When the transmitter is only transmitting on some of its channels, the remaining

channels are available for use by SUs. If the transmitter transmits in all of the channels, this

means that no white spaces are available for use at that time. The SU in this case have to wait

a period of time and then repeat the sensing process to see if a channel becomes available.

In order to detect the status of the channel, the CRD starts by sensing the medium to mea-

sure the received signal on its location, which is calculated based on Equation 4.17. The sam-

ples of the received signal is then used as an input for our detection methods. The capon method

estimates the PSD at the SU location using Equation 4.13, while the Periodogram method uses


Equation 4.4. After that, the estimated PSD is compared with a threshold and if it is greater

than the threshold, the channel is considered busy, otherwise the channel is considered free.

In our simulation we use the following parameters:

Parameter name Parameter value

No of channels in each transmitterchtx1 = 3chtx2 = 3chtx3 = 1

Operating frequencies of tx1fch1 = 57MHzfch2 = 85MHzfch3 = 69MHz

Operating frequencies of tx2fch1 = 79MHzfch2 = 183MHzfch3 = 177MHz

Operating frequency of tx3 fch1 = 63MHzThreshold range γ = 0 : 10−13 W

Table 5.2: Performance detection parameters.

In order to calculate the detection probability of each method we made the following steps.

1. We generate a random vector of 0′s and 1′s indicating the original state of the channels

in the three primary transmitters.

2. We perform a hypothesis test based on our detection methods to determine the status of

the transmitter channels.

3. We compare the result obtained from the hypothesis test with the original state of each

channel to determine correctly and incorrectly detected channels.

4. We count the number of correctly detected busy channels and divide it by the total num-

ber of busy channels to calculate the probability of detecting primary transmitters PD for

each iteration.

5. We count the number of correctly detected free channels and divide it by the total num-

ber of free channels to calculate the probability of detecting white spaces PW for each

iteration.


6. We count the number of incorrectly detected busy channels and divide it by the total

number of busy channels to calculate the probability of miss detection PMiss for each

iteration.

7. We count the number of incorrectly detected free channels and divide it by the total

number of free channels to calculate the probability of false alarm PFA for each iteration.

8. The PD of each method is calculated as the sum of PD of each iteration divided by the

number of iterations, we did the same for PW , PMiss, and PFA.

5.2.1 Detecting Primary Transmitters

Figure 5.6 plots the PD versus different threshold values. The figure illustrates that the PD can

be greatly affected by changing the threshold value. As we mentioned earlier in Chapter 4, the

performance of any detection technique is measured by the value of its PD and PFA. An ideal

detector should maximize the PD and minimize PFA, which cannot happen in reality at the

Figure 5.6: Probability of detecting primary transmitters versus threshold


Figure 5.7: Probability of false alarm versus threshold

same time. Selecting a zero threshold value will achieve a PD equal to one, but on the other

hand will increase the PFA to one. If we start increasing the threshold value we can see that

the PD will decrease.

Figure 5.7 displays the PFA versus different threshold values. The figure illustrates that

when the threshold value equal to zero any noise in the channel no matter how small it is,

cause the detector to falsely assume that there is a signal transmitted in the channel. That is

the reason why when the threshold value is zero, the PFA equals one. Increasing the threshold

value will cause the PFA to decrease. Thus, choosing the threshold value is a very critical issue

when designing a detector.

The performance of any detector can be measured from its ROC curve, which is a plot for

the PD versus PFA for different threshold values. The ROC curves for both the Capon and

Periodogram methods are shown in Figure 5.8. The figures shows that for a given PFA the PD

of the Capon method is better than that of the Periodogram method, which means that the CM

has a better performance.


Figure 5.8: Average ROC for both Capon and Periodogram methods

5.2.2 Detecting White Spaces

Till now, we saw the effect of changing threshold on PD, as we want to have a good PD to

protect PUs from interference. Meanwhile, our major goal is to efficiently utilize the spectrum

by making use of the white spaces. Figure 5.9 plots the PW versus threshold. From the figure

we can see that having a zero threshold value will cause PW to be zero, which means that we

are not efficiently utilizing the spectrum. Increasing the threshold value will achieve a better

PW , but on the other hand will increase the PMiss as we can see in Figure 5.10. A high PMiss

value means that the detector fails in detecting most of the primary transmitters, hence those

primary transmitter are susceptible to be interfered by SUs. Thus, we should carefully select a

threshold value that can achieve a good PW with low PMiss value. Figure 5.11 displays a plot

for the PW versus PMiss. The figure illustrates that at any given PMiss value, the PW for the

CM is better than that of the Periodogram method. This indicates that the CM outperforms the

Periodogram method in detecting white spaces.


Figure 5.9: Probability of detecting white spaces versus threshold

Figure 5.10: Probability of miss detection versus threshold


Figure 5.11: Average probability of detecting white spaces versus probability of miss detection

5.3 Cooperative Spectrum Sensing Based on Capon Method

As we saw from previous simulation results that the CM outperforms the Periodogram method

in detecting both primary transmitters and white spaces. In this scenario, we are implementing

a cooperative spectrum sensing based on the CM. The goal of the cooperative spectrum sensing

is to help SUs that do not have CRDs to identify white spaces in their location. The cooperative

spectrum sensing can be also used to overcome the hidden node problem that usually happen

in primary transmitter detection, as discussed in Section 3.1.1.

We consider the same simulation area and primary transmitters used in the second scenario.

In addition, we assume two types of SUs, sensors and nodes, which are shown in Figure 5.12.

Sensors are SUs equipped with CRDs and are uniformly distributed in the simulation area.

Nodes are SUs that do not have CRDs, so they cannot determine white spaces on their own.

Those nodes use cooperative spectrum sensing to determine white spaces on their location.

Each sensor use Equation 4.13 of the CM to estimate the PSD on its location. In order for

the node to find white spaces on its location, it depends on the estimated PSD of the sensors


Figure 5.12: Distribution of sensors and nodes

located within a certain search distance. We assume that the node can establish a common

control channel (CCC) [6, 35] with its neighboring sensors to collect their estimated PSD.

Then, the node processes the collected PSD of the different sensors by dividing the PSD of

each sensor by the square of the separation distance between the node and that sensor. After

performing the division the node will add up all the resultant PSD values and consider the result

as the PSD on its location. The node will compare the calculated PSD value with a predefined

threshold to determine the availability of the channel.

The PSD at the node location can be calculated by:

ϕi=

N(i)∑j=1

∧ϕj

d2(i, j)

(5.2)

where, ϕi

represents the calculated PSD at the node i location,∧ϕj

is the estimated PSD of

neighboring sensor j,N(i) is the number of neighboring sensors located within a certain search

distance from node i, and d(i, j) is the distance between the sensor j and node i.


The performance of the proposed cooperative spectrum sensing is investigated under dif-

ferent key parameters such as threshold, number of sensors, and search distance.

5.3.1 Changing Threshold Value

First we start by investigating the effect of changing threshold value on the performance of the

cooperative spectrum sensing method. We assume a number of sensors S = 1000 and they

Figure 5.13: ROC of cooperative spectrum sensing

are uniformly distributed in the simulation area A = 1 Km2 and a search distance d = 500m.

The performance of the cooperative spectrum sensing is measured by its ROC, which is shown

in Figure 5.13. The figure indicates that the cooperative spectrum sensing achieves higher PD

than the spectrum sensing performed by a single SU shown in Figure 5.8. Figure 5.14 plots PW

versus PMiss. The figure illustrates that the cooperative spectrum sensing approach achieves

higher PW values for lower values of PMiss compared to the result obtained from spectrum

sensing performed by a single SU displayed in Figure 5.11.


Figure 5.14: PW versus PMiss for cooperative spectrum sensing with different threshold values

5.3.2 Changing Number of Sensors

Now we want to see the effect of changing the number of sensors in the simulation area S =

0 : 700 on the performance of the cooperative spectrum sensing, while keeping the value

of the threshold and search distance fixed. Figure 5.15 discusses the effect of increasing the

number of sensors on the PD. Increasing the number of sensors means that the sensors will be

densely distributed on the simulation area, hence larger number of sensors will be available for

cooperation within the same search distance, which achieves a better PD.

The plot for PD versus PFA for different number of sensors is shown in Figure 5.16. This

figure is similar to the ROC figure, but here the PFA value does not reach 1 as we have a fixed

threshold value that is greater than zero. Figure 5.17 displays a plot for PW versus PMiss for

different number of sensors. When the number of sensors equal to zero, the value of PMiss

equals 1 . Having S = 0 means that the simulation area does not contain any sensors that

could participate in the cooperative spectrum sensing. So, we can not get information about the

primary transmitters. Increasing the number of sensors will decrease the PMiss to an acceptable

values, while keeping good PW values.


Figure 5.15: PD versus number of sensors for cooperative spectrum sensing

Figure 5.16: Average PD versus PFA for cooperative spectrum sensing with different numberof sensors


Figure 5.17: Average PW versus PMiss for cooperative spectrum sensing with different numberof sensors

5.3.3 Changing Search Distance

In this section, we investigate the effect of changing the search distance d = 0 : 700 on the

performance of the cooperative spectrum sensing, while keeping a fixed threshold value and

fixed number of sensors S = 700. Figure 5.18 indicates that increasing the search distance

achieves higher PD values. Figure 5.19 shows a plot for PD versus PFA for different search

distances. This figure is different from the ROC, as it is an increasing curve that starts from

PD = 0 when d = 0. Increasing the value of d will cause the PD value to grow. On the other

hand, Figure 5.20 is a decreasing curve representing the plot of PW versus PMiss for different

values of search distance. When the search distance d = 0 this is similar to having S = 0. We

do not have any information about primary transmitters, which makes PMiss = 1. Increasing

the search distance means that we will search for neighboring sensors in a larger distance.

Thus, it is expected that more sensors will cooperate and the PMiss will decrease. At the same

time, we will notice a slight decrease in the PW .


Figure 5.18: PD versus different search distances in cooperative spectrum sensing

Figure 5.19: Average PD versus PFA with different search distances for cooperative spectrumsensing


Figure 5.20: Average PW versus PMiss with different search distances for cooperative spec-trum sensing

Chapter 6

Conclusion and Future Work

In this thesis, we have discussed the spectrum scarcity problem that appeared due to the natu-

ral limitation of the radio spectrum, and the underutilization of the allocated spectrum bands.

In Chapter 1, we showed that the current FCA is not suitable to accommodate the increasing

demand for high data rate services and the increasing number of wireless users. DSA tech-

niques that can be used to overcome the limitation of the FCA scheme and the underutilization

of the allocated spectrum were covered in Chapter 2. DSA techniques allow SUs to oppor-

tunistically use the licensed spectrum when it is not used by PUs. For the SUs to access the

licensed spectrum, they have to be equipped with devices that have CR capability to determine

white spaces in the allocated spectrum. Chapter 3, discussed the four CR management func-

tions that must be performed by any CRD. In Chapter 4, we have proposed a spectrum sens-

ing based on the Capon PSD estimation method. In addition, we have discussed our system

model and covered the popular Periodogram spectral estimation method. Chapter 5 covered

a simulation implemented in Matlab for the Periodogram and Capon PSD estimation meth-

ods. The chapter presented the experimental results for three studied scenarios. The results

showed that the proposed spectrum sensing based on the capon method outperforms the one

based on the Periodogram method. The first scenario showed that the Capon method is more

accurate in identifying the frequency locations of primary transmitters. The second scenario

58

CHAPTER 6. CONCLUSION AND FUTURE WORK 59

illustrates that the PD value obtained from the Capon method is higher than that obtained from

the Periodogram method for any given PFa value. Similar results were obtained for PW . The

third scenario demonstrated that the performance of the cooperative scenario is affected by the

search distance and the number of sensors included in the cooperation. Results also showed

that the cooperative spectrum sensing is more efficient in detecting busy and free channels than

the spectrum sensing performed by a single SU.

The work done in this thesis can be extended as follow:

• In the thesis, the performance of the Capon method is investigated and compared to the

performance of the Periodogram method. However, the effect of using different fading

channels on the performance of the Capon method is not covered.

• In the thesis, the threshold value is set based on simulation results. Further investigation

is required to drive a mathematical expression for selecting the threshold value based on

a given PFA or using any of the criteria mentioned in Chapter 4.

• Although simulation results prove that, the Capon method outperforms the Periodogram

method, it needs more computational time than the Periodogram method to calculate the

estimated PSD. The thesis used TV white spaces which do not change so fast, hence the

Capon method can be an effective method, but in other applications time can be more

critical than accuracy.

• In the cooperative spectrum sensing, it is assumed that, the node can establish a Common

Control Channel (CCC) to collect the PSD from its neighboring sensors. The design of

the CCC is a challenging issue in cognitive radio networks which needs more investiga-

tion.

Bibliography

[1] Mahmood A Abdulsattar and Zahir A Hussein. Energy detection technique for spectrum

sensing in cognitive radio: a survey. International Journal of Computer Networks &

Communications, 4(5), 2012.

[2] Ian F Akyildiz, Won-Yeol Lee, Mehmet C Vuran, and Shantidev Mohanty. Next gener-

ation/dynamic spectrum access/cognitive radio wireless networks: a survey. Computer

Networks, 50(13):2127–2159, 2006.

[3] Ian F Akyildiz, Won-Yeol Lee, Mehmet C Vuran, and Shantidev Mohanty. A survey on

spectrum management in cognitive radio networks. Communications Magazine, IEEE,

46(4):40–48, 2008.

[4] Saman Atapattu, Chintha Tellambura, and Hai Jiang. Energy detection based cooperative

spectrum sensing in cognitive radio networks. Wireless Communications, IEEE Transac-

tions on, 10(4):1232–1241, 2011.

[5] Vo Nguyen Quoc Bao, Tran Dinh Thuan, Nguyen Thien Quy, Lam Minh Trung, et al.

Vietnam spectrum occupancy measurements and analysis for cognitive radio applications.

In Advanced Technologies for Communications (ATC), 2011 International Conference on,

pages 135–143. IEEE, 2011.

[6] Kaigui Bian, Jung-Min Park, and Ruiliang Chen. Control channel establishment in cog-

nitive radio networks using channel hopping. Selected Areas in Communications, IEEE

Journal on, 29(4):689–703, 2011.

60

BIBLIOGRAPHY 61

[7] Danijela Cabric, Shridhar Mubaraq Mishra, and Robert W Brodersen. Implementation

issues in spectrum sensing for cognitive radios. In Signals, systems and computers, 2004.

Conference record of the thirty-eighth Asilomar conference on, volume 1, pages 772–776.

IEEE, 2004.

[8] Industry Canada. Framework for the use of certain non-broadcasting applications in

the television broadcasting bands below 698 mhz. http://www.ic.gc.ca/eic/

site/smt-gst.nsf/eng/sf10493.html, April 2013. Accessed Feb. 6, 2015.

[9] Industry Canada. Consultation on a policy and technical framework for the use of non-

broadcasting applications in the television broadcasting bands below 698 mhz. http://

www.ic.gc.ca/eic/site/smt-gst.nsf/eng/sf10058.html#a5.3, Au-

gust 2011. Accessed Feb. 6, 2015.

[10] K-C Chen, Y-J Peng, Neeli Prasad, Y-C Liang, and Sumei Sun. Cognitive radio network

architecture: part i–general structure. In Proceedings of the 2nd international confer-

ence on Ubiquitous information management and communication, pages 114–119. ACM,

2008.

[11] Yunfei Chen. Improved energy detector for random signals in gaussian noise. Wireless

Communications, IEEE Transactions on, 9(2):558–563, 2010.

[12] Ivan Christian, Sangman Moh, Ilyong Chung, and Jinyi Lee. Spectrum mobility in cog-

nitive radio networks. Communications Magazine, IEEE, 50(6):114–121, 2012.

[13] Selami Ciftci and Murat Torlak. A comparison of energy detectability models for spec-

trum sensing. In Global Telecommunications Conference, 2008. IEEE GLOBECOM

2008. IEEE, pages 1–5. IEEE, 2008.

[14] Federal Communications Commission. Longley-rice methodology for evaluating

tv coverage and interference. http://transition.fcc.gov/Bureaus/

BIBLIOGRAPHY 62

Engineering_Technology/Documents/bulletins/oet69/oet69.pdf,

Feb. 2004. Accessed Jan. 8, 2015.

[15] Federal Communications Commission et al. Establishment of interference temperature

metric to quantify and manage interference and to expand available unlicensed operation

in certain fixed mobile and satellite frequency bands. Et Docket, (03-237), 2003.

[16] Federal Communications Commission et al. Unlicensed operation in the tv broadcast

bands. ET Docket, (04-186), 2004.

[17] Federal Communications Commission et al. In the matter of: Unlicensed operation in the

tv broadcast bands (et docket no. 04-186) and additional spectrum for unlicensed devices

below 900 mhz and in the 3 ghz band (et docket no. 02-380). FCC 10-174: Second

Memorandum Opinion and Order, 2010.

[18] Michael Corral. Vector Calculus. Michael Corral, 2008.

[19] U.S FCC. Second report and order and memorandum opinion and order. Act, Enforcement

and ET Docket No, (08-260), 2008.

[20] Mark Felegyhazi and Jean-Pierre Hubaux. Game theory in wireless networks: A tutorial.

Technical report, 2006.

[21] FCC Spectrum Policy Task Force. Report of the spectrum efficiency working group,

2002.

[22] A William Gardner. Exploitation of spectral redundancy in cyclostationary signals. Signal

Processing Magazine, IEEE, 8(2):14–36, 1991.

[23] Anita Garhwal and Partha Pratim Bhattacharya. A survey on dynamic spectrum access

techniques for cognitive radio. arXiv preprint arXiv:1201.1964, 2012.

[24] Simon Haykin and K. J. Ray Liu. Handbook on array processing and sensor networks,

volume 63. John Wiley & Sons, 2010.

BIBLIOGRAPHY 63

[25] Ekram Hossain and Vijay K Bhargava. Cognitive wireless communication networks.

Springer, 2007.

[26] Ghazale Hosseinabadi. Downlink channel assignment and power control for cognitive

radio networks using game theory. http://ufdcimages.uflib.ufl.edu/UF/

E0/04/60/86/00001/OJOWU_O.pdf. Accessed Feb. 6, 2015.

[27] Md Habibul Islam, Choo Leng Koh, Ser Wah Oh, Xianming Qing, Yoke Yong Lai, Cavin

Wang, Ying-Chang Liang, Bee Eng Toh, Francois Chin, Geok Leng Tan, et al. Spec-

trum survey in singapore: Occupancy measurements and analyses. In Cognitive Radio

Oriented Wireless Networks and Communications, 2008. CrownCom 2008. 3rd Interna-

tional Conference on, pages 1–7. IEEE, 2008.

[28] Bisera Jankuloska, Vladimir Atanasovski, and Liljana Gavrilovska. Combined

power/channel allocation method for efficient spectrum sharing in tv white space sce-

nario. In Proceedings of the 4th International Conference on Cognitive Radio and Ad-

vanced Spectrum Management, page 59. ACM, 2011.

[29] Ode Ojowu Jr. Data-adaptive spectral estimation algorithm and their sensing appli-

cations. http://ufdcimages.uflib.ufl.edu/UF/E0/04/60/86/00001/

OJOWU_O.pdf, 2013. Accessed Feb. 6, 2015.

[30] S.M. Kay. Fundamentals of Statistical Signal Processing: Detection theory. Prentice

Hall Signal Processing Series. Prentice-Hall PTR, 1998.

[31] Vladimir I Kostylev. Energy detection of a signal with random amplitude. In Communica-

tions, 2002. ICC 2002. IEEE International Conference on, volume 3, pages 1606–1610.

IEEE, 2002.

[32] Won Yeol Lee. Spectrum management in cognitive radio wireless networks. Thesis,

(PHD) Georgia Institute of Technology, 2009.

BIBLIOGRAPHY 64

[33] Won-Yeol Lee and Ian F Akyildiz. Joint spectrum and power allocation for inter-cell spec-

trum sharing in cognitive radio networks. In New Frontiers in Dynamic Spectrum Access

Networks, 2008. DySPAN 2008. 3rd IEEE Symposium on, pages 1–12. IEEE, 2008.

[34] Won-Yeol Lee and Ian F Akyildiz. Optimal spectrum sensing framework for cognitive ra-

dio networks. Wireless Communications, IEEE Transactions on, 7(10):3845–3857, 2008.

[35] Brandon F. Lo. A survey of common control channel design in cognitive radio networks.

Physical Communication, 4(1):26–39, 2011.

[36] Liangping Ma, Yingxue Li, and Alpaslan Demir. Matched filtering assisted energy detec-

tion for sensing weak primary user signals. In Acoustics, Speech and Signal Processing

(ICASSP), 2012 IEEE International Conference on, pages 3149–3152. IEEE, 2012.

[37] Moshe Timothy Masonta, Mjumo Mzyece, and Ntsibane Ntlatlapa. Spectrum decision

in cognitive radio networks: A survey. Communications Surveys & Tutorials, IEEE,

15(3):1088–1107, 2013.

[38] Meftah Mehdawi, N Riley, K Paulson, A Fanan, and M Ammar. Spectrum occupancy

survey in hull-uk for cognitive radio applications: Measurement & analysis. INTERNA-

TIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH, 2(4), 2013.

[39] Robert F Mills and Glenn E Prescott. A comparison of various radiometer detection

models. Aerospace and Electronic Systems, IEEE Transactions on, 32(1):467–473, 1996.

[40] Jayanta Mishra, Deepak Kumar Barik, and Ch Manoj Kumar Swain. Cyclostationary

based spectrum sensing in cognitive radio: Windowing approach. International Journal

of Recent Technology and Engineering (IJRTE), 3(1), 2014.

[41] Joseph Mitola and Gerald Q Maguire Jr. Cognitive radio: making software radios more

personal. Personal Communications, IEEE, 6(4):13–18, 1999.

BIBLIOGRAPHY 65

[42] Ola A. Mohammed, Khalil El-Khatib, and Miguel V. Martin. A survey of cognitive

radio management functions. In COCORA 2014, The Fourth International Conference

on Advances in Cognitive Radio, pages 6–13, 2014.

[43] Rohan Murty, Ranveer Chandra, Thomas Moscibroda, and Paramvir Bahl. Senseless:

A database-driven white spaces network. Mobile Computing, IEEE Transactions on,

11(2):189–203, 2012.

[44] Ofcom. Implementing geolocation. http://stakeholders.ofcom.org.uk/

binaries/consultations/geolocation/statement/statement.pdf.

Accessed Jan. 8, 2015.

[45] Dong-Chan Oh and Yong-Hwan Lee. Energy detection based spectrum sensing for sens-

ing error minimization in cognitive radio networks. International Journal of Communi-

cation Networks and Information Security (IJCNIS), 1(1), 2009.

[46] KP Pavithra. Optimization of energy detection in cognitive radio networks. International

Journal of Computer Trends and Technology (IJCTT), 12(1), 2014.

[47] V Prithiviraj, B Sarankumar, A Kalaiyarasan, P Praveen Chandru, and N Nandakumar

Singh. Cyclostationary analysis method of spectrum sensing for cognitive radio. In

Wireless Communication, Vehicular Technology, Information Theory and Aerospace &

Electronic Systems Technology (Wireless VITAE), 2011 2nd International Conference on,

pages 1–5. IEEE, 2011.

[48] A. Quinquis. Digital Signal Processing Using Matlab. ISTE. Wiley, 2010.

[49] V Vijayakumari R Vadivelu, K Sankaranarayanan. Matched filter based spectrum sens-

ing for cognitive radio at low signal to noise ratio. Journal of Theoretical and Applied

Information Technology, 62(1), 2014.

BIBLIOGRAPHY 66

[50] Theodore S Rappaport et al. Wireless communications: principles and practice, volume 2.

prentice hall PTR New Jersey, 1996.

[51] S Shobana, R Saravanan, and R Muthaiah. Matched filter based spectrum sensing on

cognitive radio for ofdm wlans. International Journal of Engineering and Technology

(IJET), 5(1), 2013.

[52] Carl R. Stevenson, Gerald Chouinard, Zhongding Lei, Wendong Hu, Stephen J. Shell-

hammer, and Winston Caldwell. Ieee 802.22: The first cognitive radio wireless regional

area network standard. IEEE Communications Magazine, 47(1):130–138, 2009.

[53] Petre Stoica and Randolph L. Moses. Spectral Analysis of Signals. Prentice Hall, Upper

Saddle River, NJ, 2005.

[54] SURA. Chart of the electromagnetic spectrum. https://commons.wikimedia.

org/wiki/File%3aLambdaPlaques.jpg, 2006. Accessed Jan. 8, 2015.

[55] Elias Z Tragos, Sherali Zeadally, Alexandros G Fragkiadakis, and Vasilios A Siris. Spec-

trum assignment in cognitive radio networks: A comprehensive survey. IEEE Communi-

cations Surveys and Tutorials, 15(3):1108–1135, 2013.

[56] H.L. Van Trees. Detection, Estimation, and Modulation Theory. Number pt. 1 in Detec-

tion, Estimation, and Modulation Theory. Wiley, 2004.

[57] Harry Urkowitz. Energy detection of unknown deterministic signals. Proceedings of the

IEEE, 55(4):523–531, 1967.

[58] Hano Wang, Gosan Noh, Dongkyu Kim, Sungtae Kim, and Daesik Hong. Advanced sens-

ing techniques of energy detection in cognitive radios. Communications and Networks,

Journal of, 12(1):19–29, 2010.

BIBLIOGRAPHY 67

[59] Ben Wild and Kannan Ramchandran. Detecting primary receivers for cognitive radio

applications. In New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN

2005. 2005 First IEEE International Symposium on, pages 124–130. IEEE, 2005.

[60] Jwo-Yuh Wu, Pei-Hsin Huang, Tsang-Yi Wang, and Vincent WS Wong. Energy detection

based spectrum sensing with random arrival and departure of primary user’s signal. In

Globecom Workshops (GC Wkshps), 2013 IEEE, pages 380–384. IEEE, 2013.

[61] Alexander M Wyglinski and Di Pu. Digital Communication Systems Engineering with

Software-defined Radio. Artech House, 2013.

[62] Qing Zhao and Brian M Sadler. A survey of dynamic spectrum access. Signal Processing

Magazine, IEEE, 24(3):79–89, 2007.