Cognitive Wireless Networks Using the CSS Technology

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Lecture Notes in Electrical Engineering 384

Meiling LiAnhong WangJeng-Shyang Pan

Cognitive

WirelessNetworks

Using the CSSTechnology


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Lecture Notes in Electrical Engineering

Volume 384

Board of Series editors

Leopoldo Angrisani, Napoli, Italy

Marco Arteaga, Coyoacá n, México

Samarjit Chakraborty, München, Germany

Jiming Chen, Hangzhou, P.R. China

Tan Kay Chen, Singapore, Singapore

Rüdiger Dillmann, Karlsruhe, Germany

Haibin Duan, Beijing, China

Gianluigi Ferrari, Parma, Italy

Manuel Ferre, Madrid, Spain

Sandra Hirche, München, Germany

Faryar Jabbari, Irvine, USA

Janusz Kacprzyk, Warsaw, Poland

Alaa Khamis, New Cairo City, Egypt

Torsten Kroeger, Stanford, USATan Cher Ming, Singapore, Singapore

Wolfgang Minker, Ulm, Germany

Pradeep Misra, Dayton, USA

Sebastian Möller, Berlin, Germany

Subhas Mukhopadyay, Palmerston, New Zealand

Cun-Zheng Ning, Tempe, USA

Toyoaki Nishida, Sakyo-ku, Japan

Bijaya Ketan Panigrahi, New Delhi, India

Federica Pascucci, Roma, ItalyTariq Samad, Minneapolis, USA

Gan Woon Seng, Nanyang Avenue, Singapore

Germano Veiga, Porto, Portugal

Haitao Wu, Beijing, China

Junjie James Zhang, Charlotte, USA


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About this Series

“Lecture Notes in Electrical Engineering (LNEE)” is a book series which reports

the latest research and developments in Electrical Engineering, namely:

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http://www.springer.com/series/7818http://www.springer.com/series/7818


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Meiling Li • Anhong Wang

Jeng-Shyang Pan

Cognitive Wireless Networks

Using the CSS Technology

1 3


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Meiling LiTaiyuan University of Science

and TechnologyTaiyuanPeople’s Republic of China

Anhong WangTaiyuan University of Science

and TechnologyTaiyuanPeople’s Republic of China

Jeng-Shyang PanFujian University of TechnologyFuzhou, FujianPeople’s Republic of China

ISSN 1876-1100 ISSN 1876-1119 (electronic)Lecture Notes in Electrical EngineeringISBN 978-3-319-31094-7 ISBN 978-3-319-31095-4 (eBook)DOI 10.1007/978-3-319-31095-4

Library of Congress Control Number: 2016934678

© Springer International Publishing Switzerland 2016This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specically the rights of translation, reprinting, reuse of illustrations,recitation, broadcasting, reproduction on microlms or in any other physical way, and transmissionor information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar

methodology now known or hereafter developed.The use of general descriptive names, registered names, trademarks, service marks, etc. in thispublication does not imply, even in the absence of a specic statement, that such names are exempt fromthe relevant protective laws and regulations and therefore free for general use.The publisher, the authors and the editors are safe to assume that the advice and information in thisbook are believed to be true and accurate at the date of publication. Neither the publisher nor theauthors or the editors give a warranty, express or implied, with respect to the material contained herein or

for any errors or omissions that may have been made.

Printed on acid-free paper

This Springer imprint is published by Springer NatureThe registered company is Springer International Publishing AG Switzerland


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Preface

With the rapid development of wireless networks, more and more radio spectrum

resources will be needed. Cognitive radio (CR) is an exciting emerging technology

to improve spectrum ef ciency, by which the licensed spectrum resources can be

shared dynamically by cognitive users. The accurate and effective spectrum sensing

technologies are key to realizing the cognitive radio, which are still research

hot-spots in the wireless sphere.

The aim of this book is to provide some useful methods to improve the spectrum

sensing performance in a systematic way, and point out an effective method for the

application of cognitive radio technology in wireless communications. After givinga state-of-the-art survey, we propose some new cooperative spectrum sensing

(CSS) methods, with an attempt to achieve better performance. For each CSS, the

main idea and their corresponding algorithm design are elaborated in detail.

This book covers the fundamental concepts and the core technologies of CSS,

especially its latest developments. Each chapter is presented in a self-suf cient and

independent way so that the reader can select the chapters interesting to them. The

methodologies are described in detail so that the readers can repeat the corre-

sponding experiments easily.

For researchers, it would be a good book to understand the classi

cations of CSS, inspiring new ideas about the novel CSS technology for CR, and a quick way

to learn new ideas from the current status of CSS. For engineers, it would be a good

guidebook to develop practical applications for CSS.

Chapter 1 provides a broad view of CR. Chapter 2 shows the CSS technologies

and current researches. Chapter 3 focuses on the CSS based on hard combination,

mainly devoted to the relationship of each performance parameter. Chapters 4–6 are

devoted to algorithms to solve the actual existing problems, mainly focusing on the

current research fruits of the authors. We provide the basic frameworks and the

experimental results, which may help the readers nd some new ideas. Chapter 7

introduces the application of CR and provides a basic realization method for mobile

communications.

v

http://dx.doi.org/10.1007/978-3-319-31095-4_1http://dx.doi.org/10.1007/978-3-319-31095-4_2http://dx.doi.org/10.1007/978-3-319-31095-4_3http://dx.doi.org/10.1007/978-3-319-31095-4_4http://dx.doi.org/10.1007/978-3-319-31095-4_6http://dx.doi.org/10.1007/978-3-319-31095-4_7http://dx.doi.org/10.1007/978-3-319-31095-4_7http://dx.doi.org/10.1007/978-3-319-31095-4_6http://dx.doi.org/10.1007/978-3-319-31095-4_4http://dx.doi.org/10.1007/978-3-319-31095-4_3http://dx.doi.org/10.1007/978-3-319-31095-4_2http://dx.doi.org/10.1007/978-3-319-31095-4_1


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This work was supported in part by the National Natural Science Foundation of

China (No. 61272262), National Science Foundation for Young Scientists of

Shanxi Province, China (Grant No. 2014021021-2) and Doctor Startup Foundation

of TYUST, China (No. 20122032).

We are grateful to the Springer in-house editors for the editorial assistance andexcellent cooperative collaboration to produce this important scientic work. We

hope that the reader will share our excitement to present this book and will nd it

useful.

Taiyuan, Shanxi

November 2015

vi Preface


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Contents

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.1 Dissertation Background. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.2 Cognitive Radio Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.2.1 Spectrum Sensing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

1.2.2 Spectrum Management . . . . . . . . . . . . . . . . . . . . . . . . . . 5

1.2.3 Spectrum Mobility. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

1.2.4 Spectrum Sharing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

1.2.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

1.3 Spectrum Sensing Technology. . . . . . . . . . . . . . . . . . . . . . . . . . 101.3.1 Spectrum Sensing Classication . . . . . . . . . . . . . . . . . . . 10

1.3.2 Spectrum Sensing Method . . . . . . . . . . . . . . . . . . . . . . . 12

1.4 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

2 CSS Technology. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

2.2 Cooperative Communication Model . . . . . . . . . . . . . . . . . . . . . . 24

2.2.1 Cooperative Diversity . . . . . . . . . . . . . . . . . . . . . . . . . . 24

2.2.2 Relay Cooperation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 252.3 CSS Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

2.3.1 Cooperative Diversity for CSS . . . . . . . . . . . . . . . . . . . . 30

2.3.2 Relay Diversity for CSS. . . . . . . . . . . . . . . . . . . . . . . . . 32

2.4 The Process of CSS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

2.5 Research Status . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

3 The Relationship Among the Performance Parameters in CSS . . . . . 43

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

3.2 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

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3.3 The CSS Detection Performance . . . . . . . . . . . . . . . . . . . . . . . . 46

3.3.1 Local Detection Performance . . . . . . . . . . . . . . . . . . . . . 46

3.3.2 CSS Performance Based on Decision Fusion. . . . . . . . . . . 48

3.3.3 Simulation Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

3.4 The CSS Secondary Throughput . . . . . . . . . . . . . . . . . . . . . . . . 533.4.1 Spectrum Utilization . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

3.4.2 Secondary Throughput . . . . . . . . . . . . . . . . . . . . . . . . . . 56

3.4.3 The Optimal Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . 58


3.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

4 The Censoring Based CSS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

4.2 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

4.3 The C-CSS Detection Performance . . . . . . . . . . . . . . . . . . . . . . 72

4.3.1 Performance Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 72

4.3.2 Optimal Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76


4.4 The C-CSS Secondary Throughput . . . . . . . . . . . . . . . . . . . . . . 83


4.4.2 Optimal Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84


4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

5 CSS Technology with Relay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

5.2 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

5.2.1 Problem Description . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

5.2.2 Signal Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99

5.3 Proposed Best Relay CSS Scheme . . . . . . . . . . . . . . . . . . . . . . . 101

5.3.1 Problem Description . . . . . . . . . . . . . . . . . . . . . . . . . . . 1015.3.2 The SINR-BRCS Scheme . . . . . . . . . . . . . . . . . . . . . . . . 103

5.3.3 Proposed Pe_BRCS Scheme . . . . . . . . . . . . . . . . . . . . . . 103

5.3.4 Complexity Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

5.3.5 Detection Performance . . . . . . . . . . . . . . . . . . . . . . . . . . 105


5.4 Proposed C-BR-CSS Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . 108


5.4.2 System Model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109



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5.5 Proposed Adaptive CSS Scheme with Best Relay . . . . . . . . . . . . 118


5.5.2 Algorithm Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119


5.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122

6 CSS Based on Soft Combination. . . . . . . . . . . . . . . . . . . . . . . . . . . 125

6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125

6.2 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126

6.3 SC-EF-CSS Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127


6.3.2 The Optimal Algorithm Based on N-P Criterion . . . . . . . . 131

6.3.3 The Optimal Algorithm Based on MDC . . . . . . . . . . . . . . 133


6.4 SC-DF-CSS Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136


6.4.2 The Optimal Algorithm Based on N-P Criterion . . . . . . . . 138

6.4.3 The Optimal Algorithm Based on MDC . . . . . . . . . . . . . . 140


6.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143

7 The SS Application in ICIC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1457.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145

7.2 Problem Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147

7.2.1 The SFR Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147

7.2.2 Interference Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 148

7.3 The ICI Coordination Based on SS . . . . . . . . . . . . . . . . . . . . . . 150

7.3.1 Scheme Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150

7.3.2 Interference Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 153

7.3.3 Detection Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . 155

7.3.4 Simulation Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1557.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158

Contents ix

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SDC Selective Diversity Combining

SFR Soft Frequency Reuse

SINR Signal to Interference and Noise Ratio

SNR Signal-to-Noise Ratio

SON Self-organization NetworkSR Secondary Relay

SU Secondary User

TDMA Time Division Multiple Access

WiMAX Worldwide Interoperability for Microwave Access

xG Next Generation

xii Abbreviations


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Chapter 1

Introduction

In this chapter, the background of the cognitive radio technology is stated. The key

technologies are then introduced simply. Finally, the classication and methods of

the spectrum sensing technology are described.

1.1 Dissertation Background

With the rapid development of wireless networks, the demand of radio spectrumresources also grow with each passing day, which results that the scarcity of

wireless spectrum resource is also becoming increasingly prominent. In the current

spectrum allocation framework, there are a lot of frequency bands being long-term

idle in time and space [1–7], which results the lower spectrum utilization. Visibly,

the spectrum scarcity problem is not only the actual lack of physical resources, but

also being related to the current wireless spectrum allocation mode. In the existing

xed spectrum allocation mode, each frequency band is xedly assigned to the

different authorized institutions. Other unlicensed users can not use those idle

spectrum resources, even if the licensed spectrum resources are not being usedtemporarily. For this reason, this part of the idle spectrum resources can not be fully

utilized which severely limited the utilization of spectrum resources and restricted

the development of wireless communication. Therefore, how to enhance the uti-

lization of the radio spectrum is one of the hot issues in the elds of domestic and

international communication at home and abroad. Dynamic spectrum access

(DSA) technology can effectively alleviate the contradiction between the low

spectrum utilization and the scarcity of spectrum resources, in which the unlicensed

users can use the free spectrum resources in time or space when assuring not

bringing influences to the licensed users’ communications, so as to achieve the

reuse of the spectrum resources [8–

14]. Cognitive radio (CR) is a kind of frequency

reuse technologies, which can achieve the dynamic spectrum sharing and improve

the spectrum ef ciency by DSA [15–20]. The concept of CR originates from the

© Springer International Publishing Switzerland 2016

M. Li et al., Cognitive Wireless Networks Using the CSS Technology,

Lecture Notes in Electrical Engineering 384,

DOI 10.1007/978-3-319-31095-4_1

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Pioneering research of Dr. Joseph Mitola III [21]. It has been quickly get wide

attentions from all around the world after the concept of CR was proposed.

Thereafter the spectrum management departments, the standardization organiza-

tions and the research institutions all over the world have launched their researches

on CR [22–

27].The standards related to CR include IEEE 802.22, 802.16 h and IEEE P1900.

The IEEE 802.22 working group is the rst standardization organization based on the

CR technology in the world [28], which is mainly to plan the air interface standard for

the cognitive users when accessing the broadcasting bands and assure that the

broadcasting services can not be interfered [27]. The IEEE 802.22 standard is of great

signicance to the development of CR technology. The IEEE 802.16 working group

has committed to the research of wireless broadband access technology, i.e. WiMAX

(Worldwide Interoperability for Microwave Access), which is limited to the spectrum

resources. For this reason, the 802.16 h working group was established, the purposeof which is that the series of 802.16 standards can be applied in the license-free band

by using CR technology [29]. In order to make a further study of CR, IEEE estab-

lished IEEE P1900 standard group [30–32], which committed to the next generation

wireless communication technology and advanced spectrum management technol-

ogy. This working group has great signicance to the development of CR technology

and the co-ordination and co-existence with other wireless communication systems.

The future mobile communication network will be a ubiquitous and heterogeneous

network mode, with the ability of self-conguration, self-optimization and

self-learning in the future [33]. Recently, in order to accommodate the need of thefuture network’s development, the network should perform the self-organization

functions, such as self-conguration or self optimization in IEEE 802.16m. The

concept and requirement of the self-organizing network (SON) have also been

proposed in LTE-Advanced. It requires that the network has the ability of

self-conguration, self-optimization or self-healing [33], which mainly rely on the

CR technology [34, 35]. At present, the discussion of SON is still in the preliminary

stage in the IEEE and LTE organization. There are still a lot of related technologies

which need in-depth study so that the SON can be applied in actual system.

Not only the standardization organizations have recognized the enormouspotential of CR, but also the academic communities have done widely researches to

the CR technology. Professor Simon Haykin, an international famous scholar,

dened the concept of CR from the perspective of signal processing [36, 37]. He

pointed out that the CR is an intelligent wireless communication system, which can

be aware of its surrounding environment (i.e., its outside world), and uses the

methodology of understanding-by-building to learn from the environment and adapt

its internal states to statistical variations in the incoming radio frequency

(RF) stimuli by making corresponding changes in certain operating parameters

(e.g., transmit power, carrier frequency, and modulation strategy) in real time, with

two primary objectives in mind: (1) Highly reliable communications whenever and

wherever needed; and (2) Ef cient utilization of the radio spectrum. The denitions

have been put forwarded by Simon Haykin promote a lot of extensive studies on

CR technology in the eld such as academic world, industries, research institutions

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and industry. A lot of projects have also been started, such as the CORVUS system

jointly developed by the University of California, Berkeley and the Technology

University of Berlin, Germany [38], the OCRA systems developed by the

Polytechnic University of Georgia, USA [19], the XG (next Generation) project and

E3 (End to End Ef ciency) project [39] that developed by U.S. military DARPA[40, 41]. Under the support of these projects, the CR has obtained some achieve-

ments in the eld of basic theory, network architecture, and its protocol design in

wireless communication systems.

Compared with foreign, the research on CR technology has also received highly

attention in our country. The national 973 program, the national 863 program and

the National Natural Science Foundation of China all set up a special issue of CR.

Tsinghua University, Beijing University of Post and Telecommunications,

University of Electronic Science and Technology, Hong Kong University Science

& Technology and other universities have also make in-depth studies on CRtechnology. ZTE, Huawei and other companies has also been involved in the

related work in the standards development of IEEE 802.22. Judging from the

momentum of the development at home and abroad, the study on CR technology is

still in the ascendant. CR technology, as the “next big thing” in the wireless

communication area, opens up an effective way to solve the low utilization of the

spectrum resources and the shortage of spectrum resource. However, the study of

CR is still at a preliminary stage, there are still a lot of challenges to deal with

before the nal application such as the system architecture, protocol architecture,

standards, the specic key techniques etc.

1.2 Cognitive Radio Technology

CR technologies provide the capability to use or share the spectrum in an oppor-

tunistic manner. DSA technologies allow the CR to operate in the best available

channel. The term, CR, can formally be dened as follows [2, 19]:

A ‘‘

Cognitive Radio’’

is a radio that can change its transmitter parameters basedon interaction with the environment in which it operates. From this denition, two

main characteristics of the cognitive radio can be dened [19, 36, 42]:

• Cognitive capability: Cognitive capability refers to the ability of the radio

technology to capture or sense the information from its radio environment. This

capability cannot simply be realized by monitoring the power in some frequency

band of interest. More sophisticated techniques are required in order to capture

the temporal and spatial variations in the radio environment and avoid inter-

ference to other users, the vacant spectrum at a specic time or location can be

identied through this capability. Consequently, the best spectrum and appro-priate operating parameters can be selected [19].

• Recongurability: The cognitive capability provides spectrum awareness

whereas recongurability enables the radio to be dynamically programmed

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Spectrum sensing has been identied as a key enabling functionality to ensure

that SUs would not interfere with PUs, by reliably detecting PU signals. In addition,

reliable sensing plays a critical role on communication links of SUs since it creates

spectrum opportunities for them. In order to ef ciently utilize the available

opportunities, SUs must sense frequently all degrees of freedom (time, frequency,

and space) while minimizing the time spent in sensing [45–50]. We will introduce

the spectrum sensing technology for detail in the following subsection.

1.2.2 Spectrum Management

After spectrum sensing, the spectrum management is needed to capture the best

available spectrum to satisfy user communication requirements. The spectrum

management functions mainly include spectrum analysis and spectrum decision.

In cognitive radio networks (CRN), the available spectrum holes show different

characteristics which vary over time. Since the SUs are equipped with the CR based

physical layer, it is important to understand the characteristics of different spectrum

bands. Spectrum analysis enables the characterization of different spectrum bands,

which can be exploited to get the spectrum band appropriate to the user require-

ments [19].Once all available spectrum bands are characterized, appropriate operating

spectrum band should be selected for the current transmission considering the QoS

requirements and the spectrum characteristics. The spectrum decision is about

whether and how to access the spectrum. The goal of the spectrum decision is to

best meet the user communication requirements, while satisfying a set of con-

straints, e.g., the acceptable interference which can be created to other users in the

spectrum. The optimization goal, i.e., the outcome that best meets the user

requirements, can be a local or a global criterion. Next, it is important that the

spectrum decision is coordinated through SUs in the network. The classication of the spectrum decision is shown in Fig. 1.2. The spectrum decision can impact the

future spectrum sensing and hence the amount of “learning” the wireless com-

munication scene [44].

Transmitter detection area

Secondary userPrimary base station

Primary user

Receiver detection area=

Interference area

Fig. 1.1 Detection area with and without interference

1.2 Cognitive Radio Technology 5


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1.2.3 Spectrum Mobility

In CRN, spectrum mobility arises when current channel conditions become worse

or a PU appears. Spectrum mobility gives rise to a new type of handoff in CRN that

we refer to as spectrum hand off. The protocols for different layers of the network

stack must adapt to the channel parameters of the operating frequency. Moreover,

they should be transparent to the spectrum hand off and the associated latency. As

pointed out in earlier sections, a CR can adapt to the frequency of operation.

Therefore, each time a SU changes its frequency of operation, the network proto-

cols are going to shift from one mode of operation to another. The purpose of

spectrum mobility management in CRN is to make sure that such transitions are

made smoothly and as soon as possible such that the applications running on a SU

perceive minimum performance degradation during a spectrum hand off. It is

essential for the mobility management protocols to learn in advance about the

duration of a spectrum hand off. This information should be provided by the sensing

algorithm. Once the mobility management protocols learn about this latency, their

job is to make sure that the ongoing communications of a SU undergo only min-

imum performance degradation. Consequently, multi-layer mobility management

protocols are required to accomplish the spectrum mobility functionalities. These

protocols support mobility management adaptive to different types of applications.

For example, a TCP connection can be put to a wait state until the spectrum hand

off is over. Moreover, since the TCP parameters may change after a spectrum hand

off, it is essential to learn the new parameters and ensure that the transitions fromthe old parameters to new parameters are carried out rapidly. For a data commu-

nication e.g., FTP, the mobility management protocols should implement mecha-

nisms to store the packets that are transmitted during a spectrum hand off, whereas

Spectrum decision

Optimization behaviour Architecture Coordination

CooperativeNon-

cooperativeCentralizedDistributed

Common

channel

No common

channel

Control only Data channel

Fig. 1.2 Spectrum decision classication

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for a real-time application there is no need to store the packets as the stored packets,

if delivered later, will be stale packets and can not be used by the corresponding

application [19].

1.2.4 Spectrum Sharing

As introduced in the previous section, DSA technology is needed to achieve a better

use of the spectrum. DSA is the opposite of the current static spectrum management

policy. However, various approaches are possible to make the spectrum manage-

ment more adaptive, as presented in Fig. 1.3 [44].Dynamic licensing results in a dynamic spectrum allocation that gives exclusive

use to the technology or network that currently has the most prot of spectrum use.

It is similar to the current spectrum regulation in that it licenses spectrum bands for

exclusive use. This dynamic licensing is, however, much more flexible, to be able

to adapt to the wireless communication dynamics [44].

Ideally, spectrum sharing should adapt very fast to all dynamics present in

wireless communication, which can be caused by the channel variations or because

of the burst application demands. Coexistence or dynamic sharing allows such

sharing, in theory, on a packet per packet basis since it licenses spectrum to net-works simultaneously, while relying on in-network spectrum sharing techniques to

avoid conflicts. This model for spectrum sharing assumes that all networking nodes

have equal regulatory status. As a result, this model is also referred to as open

Dynamic spectrum

access

Dynamic licensing

(Dynamic exclusive use)

Horizontal sharing

Dynamic sharing

(coexistence)

Vertical sharing

Heterogeneous

networks

Homogeneous

networksUnderlay Overlay

Symmetric Asymmetric

Fig. 1.3 Dynamic spectrum access, classication along regulatory status



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sharing model [51] or as spectrum commons [52, 53]. Medium access protocols for

wireless networks are working according to this model and considerable literature

can be found on both centrally controlled or distributed access techniques for

spectrum sharing between nodes of a single network [44].

In the above-discussed case for coexistence, both considered networks adapt their transmission schemes as function of the environment. This is because both

networks can benet from avoiding the mutual interference, and both networks

have (limited) adaptive or cognitive capabilities. In this case, spectrum sharing can

be classied as symmetric in nature. This asymmetric spectrum sharing, in which

only one of the technologies is adaptive, is somewhat similar to vertical spectrum

sharing [44].

The initial denition of spectrum sharing assumes the existence of a PU and a

SU. While the spectrum has been licensed to the PU only, the SU can use it

opportunistically provided this does not affect the PUs’ performance. Twoapproaches exist for spectrum access to minimize the interference caused to the PUs

by the SUs’ communication: spectrum overlay and spectrum underlay. Overlay

spectrum sharing refers to the spectrum access technique used. More specically, a

node accesses the network using a portion of the spectrum that has not been used by

licensed users [19, 54–60]. As a result, interference to the primary system is

minimized. Underlay spectrum sharing exploits the spread spectrum techniques

developed for cellular networks [61]. Once a spectrum allocation map has been

acquired, a SU begins transmission such that its transmit power at a certain portion

of the spectrum is regarded as noise by the licensed users. This technique requiressophisticated spread spectrum techniques and can utilize increased bandwidth

compared to overlay techniques.

1.2.5 Conclusions

The core idea of CR technology is that the SUs can intelligently detect and analyze

the spectrum in wireless environment, and then identify the free spectrum inspecic time or specic position. By this way, the SUs can access the selected

optimal spectrum to realize spectrum sharing and the improved spectrum utilization

[62–64]. To achieve this goal, the SUs have to implement spectrum sensing to nd

the PU and detect the idle spectrum resources. Spectrum analysis is needed after the

completion of spectrum sensing. The SUs will analyze the characteristics of the

available spectrum resources in the CRN so that they can determine the appropriate

frequency bands for radio transmission. The key of spectrum analysis is how to

select the appropriate spectrum according to the results of the spectrum sensing to

realize reliable communication. The purpose of the spectrum decision is making

preparations for spectrum sharing [19, 65] by conrming the access parameters

(including modulation encoding and the transmitted power) based on the spectrum

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sensing and the spectrum analysis. Spectrum analysis and spectrum decision

belongs to the scope of spectrum management. Once the work band is selected, the

SU can transmit the signal in this band, however, the SU access the spectrum in an

opportunistic way in CRN, because the available spectrum are changed dynamically

in different space and time [66–71]. When the PU appears, the SU must release the

occupied spectrum and switch to other available spectrum to achieve seamless hand

off, which is called spectrum hand off, named as spectrum shift. In the process of

spectrum hand off, how to ensure the continuity and reliability of the SU ’s trans-

mission and realize the fast seamless hand off are dif cult, which rstly require that the SU should continuously perform spectrum sensing to detect the availability of

the free spectrum resources. Multiple SUs may use the same idle spectrum to

communicate simultaneously when the idle spectrum is detected, therefore, in order

to avoid the SUs interference with each other, spectrum sharing is needed. In the

spectrum sharing, the SUs’ transmissions can be appropriately coordinated and

managed by certain resource allocation strategies which are similar to the MAC

media access protocols in the existing systems [71–73]. The above process usually

can be described by a cognitive ring, as shown in Fig. 1.4. In short, the goal of CR

is to provide available spectrum resources for SUs when assuring suf cient pro-tection to the PU. In order to achieve this goal, various aspects of the cognitive ring

need to be considered, in which the spectrum sensing is the premise and the key of

the CR technology. It is very important to study the spectrum sensing technology so

as to make full use of the limited spectrum resources.

In summary, the SUs need to search spectrum constantly until the idle spectrum

are detected in the process of spectrum sensing. When the PU returns to the channel

which has been occupied by a SU, the SU have to release this channel to avoid

interference to the PU. However, it is hard to guarantee that the PU signals can be

detected absolutely up to 100 % in actual. Therefore, the core problem of spectrumsensing technology is how to detect the idle spectrum accurately.

Fig. 1.4 Cognitive ring



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1.3 Spectrum Sensing Technology

1.3.1 Spectrum Sensing Classi cation

The spectrum sensing is to detect the unused spectrum bands in order to enable CR

to adapt its environment. Since there exist multiple SUs in CRN, according to

whether they implement spectrum sensing collaboratively, the spectrum sensing

schemes can be categorized as cooperative spectrum sensing scheme and

non-cooperative spectrum sensing scheme. Further, cooperative spectrum sensing

can be divided into centralized sensing [74–76], distributed sensing [77] and

relay-assisted sensing [78–80] on how cooperative CR users share the sensing data

in the network, as shown in Fig. 1.5 [81].

In centralized cooperative spectrum sensing, a central identity called fusion

center (FC) controls the three-step process of cooperative spectrum sensing. Firstly,

the FC selects a channel or a frequency band of interest for sensing and instructs all

cooperating SUs to individually perform local sensing. Secondly, all cooperating

SUs report their sensing results via the control channel. Then the FC combines the

received local sensing information, determines the presence of PUs, and transmits

the decision back to cooperating SUs. As shown in Fig. 1.6a, SU0 is the FC and

SU1–SU5 are cooperating SUs performing local sensing and reporting the results to

SU0. For local sensing, all SUs are tuned to the selected licensed channel or

frequency band where a physical point-to-point link between the PU transmitter and

each cooperating SU for observing the primary signal is called a sensing channel.For data reporting, all SUs are tuned to a control channel where a physical

point-to-point link between each cooperating SU and the FC for sending the sensing

results is called a reporting channel. Note that centralized cooperative spectrum

sensing can occur in either centralized or distributed CRN. In centralized CRN, a

Spectrum sensing

Cooperative

sensing

Non-Cooperative

sensing

Centalizedsensing

Distributedsensing

Relay-assistedsensing

Fig. 1.5 Spectrum sensing classication

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CR base station (BS) is naturally the FC. Alternatively, in CR ad hoc networkswhere a CR BS is not present, any SU can act as a FC to coordinate cooperative

spectrum sensing and combine the sensing information from the cooperating

neighbors [81].

Unlike centralized cooperative spectrum sensing, distributed cooperative spec-

trum sensing does not rely on a FC for making the cooperative decision. In this

case, SUs communicate among themselves and converge to a unied decision on

the presence or absence of PUs by iterations. Figure 1.6b illustrates the cooperation

in the distributed manner. After local sensing, SU1–SU5 share the local sensing

results with other users within their transmission range. Based on a distributedalgorithm, each SU sends its own sensing data to other users, combines its data with

the received sensing data, and decides whether or not the PU is present by using a

local criterion. If the criterion is not satised, SUs send their combined results to

other users again and repeat this process until the algorithm is converged and a

decision is obtained. In this manner, this distributed scheme may take several

iterations to reach the unanimous cooperative decision [81].

In addition to centralized and distributed cooperative spectrum sensing, the third

scheme is relay-assisted cooperative spectrum sensing. Since both sensing channel

and reporting channel are not perfect, a SU who experience a weak sensing channel

and a strong reporting channel and a SU who experience a strong sensing channel

and a weak reporting channel, for example, can complement and cooperate with

each other to improve the performance of cooperative spectrum sensing. In

Fig. 1.6c, SU1, SU4, and SU5, who observe strong PU signals, may suffer from a

weak report channel. SU2 and SU3, who have a strong reporting channel, can serve

as relays to assist in forwarding the sensing results from SU1, SU4, and SU5 to the

FC. In this case, the reporting channels from SU2 and SU3 to the FC can also be

called relay channels. Note that although Fig. 1.6c shows a centralized structure, the

relay-assisted cooperative spectrum sensing can exist in distributed scheme. In fact,

when the sensing results need to be forwarded by multiple hops to reach the

destination, all the intermediate hops are relays. Thus, if both centralized and

distributed structures are one-hop cooperative spectrum sensing, the relay-assisted

structure can be considered as multi-hop cooperative spectrum sensing. In addition,

SU5

PU

SU2

SU1

SU0(FC)

SU4

SU3

SU5

PU

SU2

SU1

SU0(FC)

SU4

SU3

(Relay)(Relay)

(a) (b) (c)

SU5

PU

SU2

SU1

SU4

SU3

Fig. 1.6 Classication of cooperative spectrum sensing

1.3 Spectrum Sensing Technology 11


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the relay for cooperative spectrum sensing here serves a different purpose from the

relays in cooperative communications [71], where the CR relays are used for for-

warding the PU traf c [81].

1.3.2 Spectrum Sensing Method

In cooperative spectrum sensing, each SU can utilize the sensing method in

non-cooperative spectrum sensing to implement the spectrum sensing, the detail

classication is shown as in Fig. 1.7. The current non-cooperative spectrum sensing

methods include: the sensing based on PU receiver, the sensing based on inter-

ference temperature model and the sensing based on the PU transmitter [19]. The

main idea of the sensing based on the interference temperature model is that in a

certain frequency band, if all of the energy that the PU received from the inter-

ference source do not exceed a predened maximum limit (also known as inter-ference temperature limit), the SU can share this band with the PU. The interference

temperature model manages interference at the receiver through the interference

temperature limit, which is represented by the amount of new interference that the

receiver could tolerate. In other words, the interference temperature model accounts

for the cumulative RF energy from multiple transmissions and sets a maximum

threshold on their aggregate level. As long as SUs do not exceed this limit by their

transmissions, they can use this spectrum band. The dif culty of this detection

model lies in how to effectively measure the interference temperature. A SU is

naturally aware of its transmit power level and its precise location with the help of apositioning system. With this ability, however, its transmission could cause sig-

nicant interference at a neighboring receiver on the same frequency. However,

currently, there exists no practical way for a cognitive radio to measure or estimate

Spectrum sensing

Transmitter Receiver

Energy detection Matched filter

Interference

temperature

Cyclostationary

Detection

Fig. 1.7 Spectrum sensing method

12 1 Introduction


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the interference temperature on nearby primary receivers. Since primary receivers

are usually passive devices, a SU cannot be aware of the precise locations of

primary receivers. Therefore, if SUs cannot effectively measure their transmission

on all possible receivers, a useful interference temperature measurement may not be

feasible [19]. The sensing based on PU receiver is also known as local oscillator leakage detection, in which, whether there exist available spectrum resources are

judged by detecting the target users who are receiving signal in the authorization

system, it mainly makes a judgment by using local oscillation leakage energy that

released by RF front-end when the PU’s receiving equipment are working, those

leaked signal energy are often very weak, so this method is only applied to test

television receiver at present. In practice, it is dif cult for SU to obtain accurate

position information of the PU receiver [50, 82–87]. The sensing based on the PU

transmitter is that the SU determine whether there are idle spectrum by detecting the

transmitted signal of the PU. This method of spectrum sensing is simple and easy tooperate. Therefore, most existing spectrum sensing algorithms focus on the

detection of the primary transmitted signal based on the local observations of the

CR. The typical detection methods based on primary transmitted signal include:

energy detection, matched lter detection and cyclostationary detection [19, 71].

1.3.2.1 Energy Detection

If prior knowledge of the PU signal is unknown, the energy detection method isoptimal for detecting any zero-mean constellation signals [88]. In the energy

detection approach, the radio-frequency (RF) energy in the channel or the received

signal strength indicator is measured to determine whether the channel is idle or not.

First, the input signal is ltered with a band-pass lter to select the bandwidth of

interest. The output signal is then squared and integrated over the observation

interval. Lastly, the output of the integrator is compared to a predened threshold to

infer the presence or not of the PU signal, as shown in Fig. 1.8. When the spectrum

is analyzed in the digital domain, fast Fourier transform (FFT) based methods are

used. Speci

cally, the received signal x t ð Þ, sampled in a time window, is

rst passed through an FFT device to get the power spectrum X f ð Þj j2. The peak of thepower spectrum is then located. After windowing the peak of the spectrum, we get

Y f ð Þj j2. The signal energy is then collected in the frequency domain. Although theenergy-detection approach can be implemented without any prior knowledge of the

PU signal, it still has some drawbacks. The rst problem is that it has poor per-

formance under low SNR conditions. This is because the noise variance is not

accurately known at the low SNR, and the noise uncertainty may render the energy

detection useless [88]. Another challenging issue is the inability to differentiate the

Fig. 1.8 Energy detection

model



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interference from other secondary users who share the same channel with the PU

[87]. Furthermore, the threshold used in energy selection depends on the noise

variance, and small noise power estimation errors can result in signicant perfor-

mance loss [71]. As energy detection method is simple and easy to realize, it has

been widely applied in practice.

1.3.2.2 Matched Filter Detection

When a SU has a prior knowledge of the PU signal, the optimal signal detection is a

matched lter, as it maximizes the signal-to-noise ratio (SNR) of the received

signal. A matched lter is obtained by correlating a known signal, or template, with

an unknown signal to detect the presence of the template in the unknown signal.

This is equivalent to convolving the unknown signal with a time-reversed versionof the template [71]. So the matched lter can be considered as an optimal linear

lter that maximizes the SNR in the presence of additive noise [50, 89, 90].

The main advantage of matched lter is that it needs less time to achieve high

processing gain due to coherent detection [88]. Another signicant disadvantage of

the matched lter is that it would require a dedicated sensing receiver for all

primary user signal types. In the CR scenario, however, the use of the matched lter

can be severely limited since the information of the PU signal is hardly available at

the CRs. The use of this approach is still possible if we have partial information of

the PU signal such as pilot symbols or preambles, which can be used for coherent detection [15]. For instance, to detect the presence of a digital television

(DTV) signal, we may detect its pilot tone by passing the DTV signal through a

delay-and-multiply circuit. If the squared magnitude of the output signal is larger

than a threshold, the presence of the DTV signal can be detected [71].

1.3.2.3 Cyclostationary Detection

Cyclostationary detection is more robust to noise uncertainty than energy detection.If the signal of the PU exhibits strong cyclostationary properties, it can be detected

at very low SNR values by exploiting the information (cyclostationary feature)

embedded in the received signal. A signal is said to be cyclostationary (in the wide

sense) if its autocorrelation is a periodic function of time t with some period [71,

91]. The cyclostationary detection can be performed as follows.

• First, the cyclic auto-correlation function (CAF) of the observed signal x t ð Þ is

calculated as E x t þ sð Þ x t sð Þe j 2pat

, where E f g denotes the statistical

expectation operation and a is called the cyclic frequency.

• The spectral correlation function (SCF) S f ; að Þ is then obtained from the discreteFourier transformation of the CAF. The SCF is also called cyclic spectrum,

which is a two-dimension function in terms of frequency f and cyclic frequency a.

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• The detection is completed by searching for the unique cyclic frequency cor-

responding to the peak in the SCF plane.

This detection approach is robust to random noise and interference from other

modulated signals because the noise has only a peak of SCF at the zero cyclicfrequency and the different modulated signals have different unique cyclic fre-

quencies. In [92], the cyclostationary detection method is employed for the

detection of the Advanced Television Systems Committee DTV signals in wireless

region-area network systems. Experimental results show superior detection per-

formance even in very low SNR region. In [93], distributed detection is considered

for scanning spectrum holes, where each CR employs a generalized likelihood ratio

test for detecting primary transmissions with multiple cyclic frequencies.

The above approach can detect the PU signal from other SUs signals over the

same frequency band provided that the cyclic features of the PU and the CR signals

differ from each other, which is usually the case, because different wireless systems

usually employ different signal structures and parameters. By exploiting the distinct

cyclostationary characteristics of the PU and the CR signals, a strategy of extracting

channel-allocation information is proposed in spectrum pooling systems [94], where

the PU is a GSM network and the CR is an OFDM-based WLAN system. However,

cyclostationary detection is more complex to implement than the energy detection

and requires a prior knowledge of PU signal such as modulation format [71].

1.4 Motivation

Since there exists the hidden terminal problem in CRN as shown in Fig. 1.9, when

the channel between the PU and the SU is effected by the shadow of building, the

SU cannot accurately detect whether the PU is present or not in the interested bands

of the SU. As a result, the SU may incorrectly detect the PU signal whereas the

spectrum is idle, which results that the SU can not use this frequency band and thus

lose the opportunities to access it. On the other hand, when the band is occupied by

the PU while the PU signal is not detected by the SU, the SU will access this bandwhich causes serious interference to the PU. Accordingly, when the channel

between the PU and the SU exists multipath fading and shadowing, the PU signal

PUSUFig. 1.9 The hidden terminal

problem between the PU and

the SU



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can not be accurately detected by single-user spectrum sensing. To address this

issue, multiple CRs can be designed to collaborate in spectrum sensing [15, 71].

Cooperative communication technology can effectively resist wireless channels’

fading and greatly improve the transmission reliabilities, which has been regarded

as one of the ways to deal with the problems of CR technology [95, 96]. References[22, 23, 78, 79] show that the system performance can be improved by introducing

the cooperative communication into the CR system, in which, both of the advan-

tages can be utilized. The cooperative spectrum sensing (CSS) is a main application

in the combination of CR technology and cooperative communication technology.

Previous studies have shown that the CSS can effectively improve the detection

performance under fading channels [15, 24, 25, 75, 80, 97–104]. As discussed

above, from the perspective of the network architecture, the CSS can be divided

into centralized based CSS and distributed based CSS. In the centralized CSS mode,

there exist a control channel and a central controller which is also called the FC, inwhich, each SU transmit the sensing result to the FC by the control channel, and

then the FC makes a nal decision on whether the PU signal exists or not by

collecting all of the results from each SU and broadcast the availability of detected

spectrum in the whole network. In the distributed CSS mode, there is no central

controller, each SU sharing its local sensing information and decide whether the PU

signal is present by combining its local sensing information with others’. In the

distributed CSS mode, each SU will perform information fusion, which results a

large amount of data computing. From the perspective of spectrum sharing, the FC

is responsible for allocating the idle spectrum resources in the centralized CSS;while in the distributed CSS, each node is self-centered and mainly satisfy the need

of itself, it will access the spectrum as long as the idle spectrum is detected without

considering about the needs of other SUs’. We mainly concentrate on the cen-

tralized based CSS in this book.

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