Phy layer and mac layer protocol design and performance analysis for opportunistic spectrum access in cognitive radio networks

PHYSICAL LAYER AND MEDIUM ACCESS CONTROL
LAYER PROTOCOL DESIGN IN COGNITIVE RADIO
NETWORKS
CHEN QIAN
NATIONAL UNIVERSITY OF SINGAPORE
2011
PHYSICAL LAYER AND MEDIUM ACCESS CONTROL
LAYER PROTOCOL DESIGN IN COGNITIVE RADIO
NETWORKS
CHEN QIAN
(M. Eng., Xi’an Jiaotong University, China)
A THESIS SUBMITTED
FOR THE DEGREE OF DOCTOR OF PHILOSOPHY
DEPARTMENT OF ELECTRICAL AND COMPUTER ENGINEERING
NATIONAL UNIVERSITY OF SINGAPORE
2011
Acknowledgement
I would like to express my sincere gratitude and appreciation to my supervisors
Professor Lawrence Wai-Choong Wong and Assoc. Professor Mehul Motani for their
valuable guidance and helpful technical support throughout my Ph.D course. Had it
not been for their advices, direction, patience and encouragement, this thesis would
certainly not be possible.
I would like to thank Dr. Ying-Chang Liang at Institute for Infocomm Research
(I
2
R), Agency for Science, Technology and Research (A-STAR), Dr. Arumugam
Nallanathan at King’s College London, and Dr. Yan Xin at NEC Laboratories America,
for many helpful discussions on my research work.
My thanks go to my colleagues in the ECE-I
2
R Wireless Communications

Laboratory at the Department of Electrical and Computer Engineering and Ambient
Intelligence Lab at the Interactive and Digital Media Institute, and also go to the
research group at I
2
R A-STAR for their generous help and warm friendship during
these years.
Last, I would like to thank my family, especially, my wife Xue Tian and my son
Chen Xuesen Delmar, for their love and encouragement.
i
Contents
Acknowledgement i
Contents ii
Summary vii
List of Figures ix
List of Tables xii
List of Notations xiii
List of Abbreviations xiv
1 Introduction 1
1.1 Spectrum Sensing Techniques . . . . . . . . . . . . . . . . . . . . . 2
1.2 Spectrum Access Mechanisms . . . . . . . . . . . . . . . . . . . . . 4
1.2.1 Opportunistic Spectrum Access Model . . . . . . . . . . . . 4
1.2.2 Spectrum Sharing Model . . . . . . . . . . . . . . . . . . . . 6
1.2.3 Overlay Model . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.3 Motivations and Challenges . . . . . . . . . . . . . . . . . . . . . . . 8
1.4 Contributions and Organization of the Thesis . . . . . . . . . . . . . 10
ii
CONTENTS
2 Existing Techniques and Literature Review 13
2.1 Spectrum Sensing Strategies . . . . . . . . . . . . . . . . . . . . . . 13
2.1.1 Non-cooperative Spectrum Sensing . . . . . . . . . . . . . . 14

2.1.2 Cooperative Spectrum Sensing . . . . . . . . . . . . . . . . . 15
2.2 Opportunistic Spectrum Access . . . . . . . . . . . . . . . . . . . . . 16
2.2.1 Spatial OSA . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.2.2 Temporal OSA . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.3 Packet Scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.3.1 Guaranteed Access Model . . . . . . . . . . . . . . . . . . . 18
2.3.2 Random Access Model . . . . . . . . . . . . . . . . . . . . . 19
2.4 IEEE 802.11 MAC Protocol in WLAN and Multi-hop Networks . . . 20
3 Cooperative Spectrum Sensing Strategies for Cognitive Radio Mesh
Networks 24
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.2 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
3.3 Cooperative Spectrum Sensing Strategies for Single-Relay Model . . 29
3.3.1 Overview of Non-Cooperative Spectrum Sensing Method . . 29
3.3.2 Performance of AR . . . . . . . . . . . . . . . . . . . . . . . 31
3.3.3 Performance of DR . . . . . . . . . . . . . . . . . . . . . . . 35
3.4 Cooperative Spectrum Sensing Strategies for Multi-Relay Model . . . 36
3.4.1 AR for Multi-Relay Model . . . . . . . . . . . . . . . . . . . 37
3.4.2 DR for Multi-Relay Model . . . . . . . . . . . . . . . . . . . 39
3.5 Cooperative Spectrum Sensing Strategies with Known-CSI Condition 42
3.6 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
3.6.1 Performance of Single-Relay Model . . . . . . . . . . . . . . 44
3.6.2 Performance of Multi-relay Model . . . . . . . . . . . . . . . 47
iii
CONTENTS
3.6.3 Effects of Known-CSI and Unknown-CSI Cases . . . . . . . 49
3.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
4 A Two-Level MAC Protocol Strategy for Opportunistic Spectrum Access
in Cognitive Radio Networks 51
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

4.2 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
4.2.1 System Model . . . . . . . . . . . . . . . . . . . . . . . . . 54
4.2.2 Spectrum Sensing method . . . . . . . . . . . . . . . . . . . 55
4.2.3 Traffic Model and Assumptions . . . . . . . . . . . . . . . . 56
4.2.4 Spectrum Access Scheme . . . . . . . . . . . . . . . . . . . 57
4.2.5 PU’s Activities and Performance Parameters . . . . . . . . . 58
4.3 Slotted CR-ALOHA and its Performance . . . . . . . . . . . . . . . . 60
4.3.1 Slotted CR-ALOHA . . . . . . . . . . . . . . . . . . . . . . 60
4.3.2 Throughput Analysis . . . . . . . . . . . . . . . . . . . . . . 62
4.3.3 Delay Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 65
4.3.4 Optimal Spectrum Sensing Time . . . . . . . . . . . . . . . . 68
4.4 CR-CSMA and its Performance . . . . . . . . . . . . . . . . . . . . 68
4.4.1 CR-CSMA Protocol . . . . . . . . . . . . . . . . . . . . . . 68
4.4.2 Throughput Analysis . . . . . . . . . . . . . . . . . . . . . . 70
4.4.3 Delay Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 75
4.5 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
4.5.1 Performance of slotted CR-ALOHA . . . . . . . . . . . . . . 76
4.5.2 Performance of CR-CSMA . . . . . . . . . . . . . . . . . . . 81
4.5.3 Tradeoff between Performance and Interference . . . . . . . . 83
4.5.4 Tradeoff between Performance and Agility . . . . . . . . . . 84
4.5.5 Optimal Frame Length . . . . . . . . . . . . . . . . . . . . . 84
iv
CONTENTS
4.5.6 Effects of PU’s Activities . . . . . . . . . . . . . . . . . . . . 89
4.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
5 MAC Protocol Design and Performance Analysis for Multi-hop Cognitive
Radio Networks 93
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
5.2 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
5.2.1 System Model . . . . . . . . . . . . . . . . . . . . . . . . . 96

5.2.2 Spectrum Sensing and PU Protection . . . . . . . . . . . . . 97
5.2.3 PTS/RTS/CTS Access Mechanism . . . . . . . . . . . . . . . 98
5.2.4 Exponential Backoff and Blocking Mechanisms . . . . . . . . 101
5.3 Queueing Model for CR-CSMA/CA . . . . . . . . . . . . . . . . . . 102
5.3.1 The Packet Transmission Process . . . . . . . . . . . . . . . 102
5.3.2 Markov Chain Model for Packet Transmission Probability τ . 110
5.3.3 Packet Service Time T
s
. . . . . . . . . . . . . . . . . . . . . 113
5.3.4 Queue Empty Probability P
0
. . . . . . . . . . . . . . . . . . 122
5.3.5 Performance Metrics for G/G/1 Queueing Model . . . . . . . 123
5.3.6 Performance Metrics for M/G/1 Queueing Model . . . . . . . 126
5.4 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 126
5.4.1 Performance of CR-CSMA/CA for multi-hop CRN . . . . . . 128
5.4.2 Performance of CR-CSMA/CA for WLAN . . . . . . . . . . 135
5.4.3 Performance Comparisons . . . . . . . . . . . . . . . . . . . 135
5.4.4 Effects of Spectrum Utilization of Primary Network . . . . . 140
5.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142
6 Conclusions and Future Work 143
6.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143
6.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145
v
CONTENTS
6.2.1 Spectrum Sensing Technique in Transmission Process . . . . 145
6.2.2 Multi-Channel Access Mechanism . . . . . . . . . . . . . . . 145
6.2.3 Security Problem under CRN . . . . . . . . . . . . . . . . . 146
6.2.4 Cross-Layer Protocol Design for CRN . . . . . . . . . . . . . 146
A Appendices to Chapter 3 147

A.1 Proof of Theorem 3.2 . . . . . . . . . . . . . . . . . . . . . . . . . . 147
A.2 Proof of Theorem 3.3 . . . . . . . . . . . . . . . . . . . . . . . . . . 147
A.3 Proof of Theorem 3.4 . . . . . . . . . . . . . . . . . . . . . . . . . . 148
B Appendices to Chapter 4 150
B.1 Proof of Lemma 4.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . 150
B.2 Proof of Lemma 4.2 . . . . . . . . . . . . . . . . . . . . . . . . . . . 151
B.3 Proof of Lemma 4.3 . . . . . . . . . . . . . . . . . . . . . . . . . . . 151
C Appendices to Chapter 5 153
C.1 Derivation of B

(1) and B

(1) . . . . . . . . . . . . . . . . . . . . . 153
C.2 Derivation of U
p
. . . . . . . . . . . . . . . . . . . . . . . . . . . . 154
Bibliography 168
List of Publications 170
vi
Summary
Cognitive radio (CR) communication technique is proposed to relieve the spectrum
scarcity problem, which allows the unlicensed secondary users (SUs) to use the
spectrum bands originally allocated to the licensed primary users (PUs). Generally,
SUs have lower access priority and must operate in a transparent manner without
interfering the PUs’ work. Thus, the coexistence of SUs and PUs at the same frequency
bands brings the challenges for the protocol design of the secondary network on both
physical (PHY) layer and medium access control (MAC) layer. In this thesis, we
focus on cognitive radio networks (CRN) and aim to improve the spectrum sensing
performance at the PHY layer and to solve the access contention problem at the MAC
layer. Thus, the performance of the secondary network is optimized, while the primary

network also can be adequately protected.
To improve detection performance, two cooperative spectrum sensing strategies
called amplify-and-relay (AR) and detect-and-relay (DR) using data fusion policy
are proposed to work at the PHY layer of CR mesh networks. Considering the
single-relay and multi-relay models with the conditions of known and unknown
channel state information (CSI), closed-form expressions of the performance metrics,
e.g., false alarm probability and detection probability, are derived for each strategy.
Then, the comparisons between our proposed strategies and an exiting method or the
non-cooperative spectrum sensing method are provided. In addition, the effect of the
number of relay users on detection performance is also investigated.
vii
Summary
To solve the channel access contention problem at the MAC layer, a two-level
OSA strategy is proposed, and two MAC protocols called Slotted CR-ALOHA and
CR-CSMA, are developed accordingly. Moreover, closed-form expressions of network
metrics -normalized throughput and average packet delay are derived, respectively.
For various frame lengths and different number of SUs, the optimal performances are
analyzed. Meanwhile, using the interference factor and the agility factor, the tradeoffs
between the achievable performance of the secondary network and the protection
effects on the primary network are studied, and the optimal frame length problem is
also addressed accordingly. In addition, the performance comparisons between slotted
CR-ALOHA and CR-CSMA are provided, and the effects of the spectrum utilization of
the primary network on the performance of the secondary network are also considered.
To address the problem of MAC protocol design for a multi-hop network, we
consider the hidden terminal problem and the spectrum sensing technique, and then
extend the traditional RTS/CTS mechanism to the PTS/RTS/CTS mechanism which
involves an asynchronous spectrum sensing technique to perform detection during the
process of the transmission link establishment. Based on this mechanism, a new MAC
protocol, namely, CR-CSMA/CA, is proposed to coordinate the channel access for the
secondary network and avoid interference to the primary network. Using the discrete

time G/G/1 queuing model with the assumption of unsaturated network condition,
the performance of CR-CSMA/CA is analyzed, and closed-form expressions of the
performance metrics are also derived, viz., packet successful transmission probability,
normalized throughput, average packet service time, average buffer queue size, etc. In
addition, the achievable performance of the secondary network are studied for both
multi-hop CRN and WLAN, in consideration with the number of neighboring SUs, the
offered traffic load, and the spectrum utilization of the primary network.
viii
List of Figures
1.1 The OSA model: The shadowed areas with solid line denote the
spectra occupied by PUs and the white areas with dash line denote
the spectra occupied by SUs. . . . . . . . . . . . . . . . . . . . . . . 5
1.2 The spectrum sharing model: SUs share the same spectrum with
PUs while the interference power at each PU receiver is lower than
a threshold. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.3 The overlay model: SU transmitter has a priori knowledge of PU
transmitter’s message. . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.1 Hidden/exposed terminal problem. . . . . . . . . . . . . . . . . . . . 22
3.1 Cooperative spectrum sensing model. . . . . . . . . . . . . . . . . . 27
3.2 The designed MAC frame structure for our proposed strategies. . . . . 27
3.3 Detection performances for single-relay model (E
0
= 0 dB). . . . . . 45
3.4 Detection performances for single-relay model (E
0
= 4 dB). . . . . . 45
3.5 Detection performances for single-relay model (E
0
= 7.8 dB). . . . . 46
3.6 Detection performances for multi-relay model (E

0
= 0 dB). . . . . . . 48
3.7 Detection performances for multi-relay model (E
0
= 4 dB). . . . . . . 48
3.8 Detection performances for multi-relay model (E
0
= 7.8 dB). . . . . . 49
4.1 The system model of CRN. . . . . . . . . . . . . . . . . . . . . . . . 54
ix
LIST OF FIGURES
4.2 The MAC frame structure for two-level OSA strategy: The blue arrows
denote the packet arrivals. . . . . . . . . . . . . . . . . . . . . . . . . 55
4.3 The operation scheme of slotted CR-ALOHA: Solid box indicates the
inactive P
t
in the current frame and dotted box indicates the active case.
61
4.4 The operation scheme of CR-CSMA: Solid box indicates the inactive
P
t
in the current frame and dotted box indicates the active case. . . . . 69
4.5 Normalized throughput S versus t for slotted CR-ALOHA. . . . . . . 77
4.6 Average packet delay D versus t for slotted CR-ALOHA. . . . . . . . 77
4.7 Maximum normalized throughput S
max
versus N for optimal t and
maximum t (slotted CR-ALOHA). . . . . . . . . . . . . . . . . . . . 79
4.8 Minimum average packet delay D
min

versus N for optimal t and
maximum t (slotted CR-ALOHA). . . . . . . . . . . . . . . . . . . . 79
4.9 Normalized throughput S versus t for CR-CSMA. . . . . . . . . . . . 80
4.10 Average packet delay D versus t for CR-CSMA. . . . . . . . . . . . 80
4.11 Maximum normalized throughput S
max
versus N for optimal t and
maximum t (CR-CSMA). . . . . . . . . . . . . . . . . . . . . . . . . 82
4.12 Minimum average packet delay D
min
versus N for optimal t and
maximum t (CR-CSMA). . . . . . . . . . . . . . . . . . . . . . . . . 82
4.13 Tradeoff between performance and interference for slotted CR-ALOHA. 85
4.14 Tradeoff between performance and interference for CR-CSMA. . . . 86
4.15 Tradeoff between performance and agility for slotted CR-ALOHA. . . 87
4.16 Tradeoff between performance and agility for CR-CSMA. . . . . . . 88
4.17 Effects of P
H
0
on the performance of slotted CR-ALOHA. . . . . . . 90
4.18 Effects of P
H
0
on the performance of CR-CSMA. . . . . . . . . . . . 91
5.1 System model of a multi-hop scenario: The user with black color
denotes the SU, and the user with grey color denotes the PU. . . . . . 96
x
LIST OF FIGURES
5.2 PTS/RTS/CTS access mechanism. . . . . . . . . . . . . . . . . . . . 99
5.3 Packet transmission process. . . . . . . . . . . . . . . . . . . . . . . 103

5.4 Network topology of a multi-hop CRN: The circle with solid line
denotes the area X
t
or X
i
, and the circle with dash line denotes the
area X
c
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
5.5 The vulnerable period of SIFS prior to DATA. . . . . . . . . . . . . . 108
5.6 The vulnerable period of SIFS prior to ACK. . . . . . . . . . . . . . 109
5.7 Markov chain model for backoff steps. . . . . . . . . . . . . . . . . . 111
5.8 Generalized state transition diagram of packet service process. . . . . 119
5.9 Normalized throughput of CR-CSMA/CA. . . . . . . . . . . . . . . . 129
5.10 Performance metrics for multi-hop CRN. . . . . . . . . . . . . . . . 133
5.11 Performance metrics for WLAN. . . . . . . . . . . . . . . . . . . . . 139
5.12 Effects of the spectrum utilization of the primary network on the
performance of the secondary network. . . . . . . . . . . . . . . . . . 141
xi
List of Tables
5.1 System configurations used to obtain the simulation results. . . . . . . 127
xii
List of Notations
a lowercase letters are used to denote scalars
a boldface lowercase letters are used to denote column vectors
A boldface uppercase letters are used to denote matrices
(·)
T
the transpose of a vector or a matrix
(·)

H
the conjugate transpose of a vector or a matrix
E[·] or E{·} the statistical expectation operator
|a| or a the length or magnitude or norm of a vector
Pr{·} the occurrence probability of an event
· the floor function of a real number
· the ceiling function of a real number
C
k
S
or

S
k

the set of all k-combinations of a set S
dom the domain of an element or a function
f

or df the first order derivative of a function f
f

or d
2
f the second order derivative of a function f
xiii
List of Abbreviations
ACK Acknowledgement
AI Artificial Intelligent
AP/SAP Access Point/Secondary Access Point

AR Amplify-and-Relay
BP Busy Period
BPSK Binary Phase Shift Keying
BS Base Station
CCC Common Control Channel
CDF/cdf Cumulative Distribution Function
CGRV Complex Gaussian Random Variable
CR Cognitive Radio
CRN Cognitive Radio Networks
CSCG Circularly Symmetric Complex Gaussian
CSI Channel State Information
CSMA Carrier Sense Multiple Access
CSMA/CA Carrier Sense Multiple Access with Collision Avoidance
CSR Carrier Sensing Range
CTS Clear-To-Send
CW Contention Window
xiv
Abbreviations
DARPA Defense Advanced Research Projects Agency
DCF Distributed Coordination Function
DIFS Distributed Inter-Frame Space
DR Detect-and-Relay
EIFS Extended Inter-Frame Space
FCC Federal Communications Commission
FFT Fast Fourier Transform
FP False-busy Period
IP Idle Period
IR Interference Range
ISM Industrial, Scientific and Medical
LIFO Last-In-First-Out

MAC Medium Access Control
MBS Mesh Base Station
MSS Mesh Subscriber Station
NAV Network Allocation Vector
OSA Opportunistic Spectrum Access
PDF/pdf Probability Density Function
PGF/pgf Probability Generating Function
PHY Physical
PMF/pmf Probability Mass Function
POMDP Partially Observable Markov Decision Process
PSK Phase Shift Keying
PTI Packet Type Identifier
PTS Prepare-To-Sense
PU Primary User
xv
Abbreviations
QoS Quality-of-Service
QPSK Quadrature Phase Shift Keying
RR Round-robin
RSSI Received Signal Strength Indication
RTS Request-To-Send
SIFS Short Inter-Frame Space
SNR Signal-to-Noise Ratio
SS Spectrum Sensing
ST Slot Time
SU Secondary User
TDMA Time Division Multiple Access
TP Transmission Period
TR Transmission Range
UP Useful Period

WLAN Wireless Local Area Network
XG neXt Generation
xvi
Chapter 1
Introduction
Recent advances in communication technology have resulted in the boom of wireless
applications. Limited to the hardware constraint, most of these applications operate
at the 900 MHz and 2.4 GHz bands which are the so-called industrial, scientific
and medical (ISM) bands. However, with the growing deployment of wireless
applications, these unlicensed frequency bands are increasingly getting congested and
will eventually be exhausted. This leads to a spectrum scarcity problem. In order to
address this problem, the Federal Communications Commission (FCC) has recently
approved opening licensed bands to unlicensed devices (e.g. TV white bands). In
the traditional spectrum management mechanism, the licensed spectrum bands are
exclusively allocated to a few particular customers. However, the corresponding
spectrum utilization is usually very low. As a matter of fact, measurement results
show that, in the US, only 2% of the spectrum resource is in use at any given time
and location [1]. In Singapore, the spectrum utilization efficiency is only 5% [2].
Furthermore, even if a spectrum band is being used, there still exists an abundance
of opportunities for access at the slot time level. This contradiction motivates the
development of cognitive radio networks (CRN) [3–5], where unlicensed secondary
users (SUs) are approved to be organized by using the spectrum bands originally
1
1.1 Spectrum Sensing Techniques
belonging to licensed primary users (PUs).
Obviously, the coexistence of PUs and SUs in the same frequency bands brings
the challenges for both physical (PHY) and medium access control (MAC) protocol
design in CRN. As compared with the traditional networks, PUs and SUs in a CR
environment are usually unknown to each other, and the PUs’ information (e.g.,
modulation technique and location) seems to be a “black box” for SUs. Obviously,

if SUs are located outside the carrier sensing range (CSR) of PUs, it is impossible
for them to know PUs’ ON/OFF states only by the carrier sense technique adopted
in traditional collision detection. In this case, we must consider using the spectrum
sensing technique to perform the primary user detection at the PHY layer. Moreover,
we must consider the MAC protocol design for CRN which solves the channel access
contention problem between PUs and SUs. To avoid interference and to protect
the PUs’ operations, SUs are required to agilely vacate the channel or transparently
transmit their packets when PUs are being active.
In this chapter, we will briefly introduce the background and implementation
issues at the PHY layer and the MAC layer under CRN. At the end of this chapter,
we present the objectives and contributions of this thesis.
1.1 Spectrum Sensing Techniques
Spectrum sensing [6] plays an important role in the realization of CRN, which
enables SUs to exploit the unused spectrum bands adaptively to the changing radio
environment. Since SUs are assumed to have no real-time interaction with PUs, they
do not know the exact information of the ongoing transmissions within the primary
network. Thus, SUs rely only on the local radio observations in the secondary
network to detect PUs’ ON/OFF states. Therefore, the performance of a spectrum
sensing method, determined by two parameters called detection probability and false
2
1.1 Spectrum Sensing Techniques
alarm probability in signal detection theory, not only deeply affects the achievable
performance of the secondary network, but also has a profound influence on the
operation of the primary network. Here, the detection probability is defined as the
probability that a SU can correctly detect the active states of PUs when PUs are active,
and false alarm probability refers to the occurrence probability that a SU claims the
existence of active PUs while the truth is that no PU is active at that time. According
to these definitions, we can easily conclude that the lower the false alarm probability,
the more access opportunities for SUs, and vice versa. Similarly, higher detection
probability decreases the possibility that the secondary network interferes with the

primary network. Therefore, to achieve better performance for the secondary network
and to properly protect the operation of the primary network in a CRN, SUs must
maintain a lower false alarm probability and a higher detection probability. However,
the performances of these two parameters cannot always be satisfied at the same time,
thus a compromise between performance and protection arises. In most of applications,
the secondary network is required to operate in a transparent mode, i.e., the primary
network could work as usual and “feel” that no SU exists. Therefore, the main problem
in the implementation of a CRN is to improve the spectrum utilization efficiency and
maximize the system performance of the secondary network while the constraint on
protecting the primary network is satisfied.
Generally, three spectrum sensing techniques are widely used for different
applications [7–12]. The first technique called matched filter is a linear optimal filter
used for coherent signal detection to maximize the signal-to-noise ratio (SNR) in the
presence of additive stochastic noise. The second one called energy detector is optimal
in detecting the unknown signal if the noise power is known. The third one called
cyclostationary feature detection determines the presence of PU signals by extracting
their specific features such as pilot signals, cyclic prefixes, symbol rate, spreading
codes, or modulation types. In addition, the advantages and disadvantages of these
3
1.2 Spectrum Access Mechanisms
three techniques are summarized in [10]. We will detail these techniques in the next
chapter.
1.2 Spectrum Access Mechanisms
According to the definition in [5], CR is an intelligent wireless communication
system that is aware of its surrounding environment, adapts its transmission to the
electromagnetic environment, and improves the utilization efficiency of the radio
spectrum. Based on the properties of the different access methods, spectrum access
mechanisms proposed to address the access contention problem can be classified
into three categories: opportunistic spectrum access (OSA) model, spectrum sharing
model, and overlay model.

1.2.1 Opportunistic Spectrum Access Model
OSA envisioned by the DARPA XG program [13] is a feasible and key approach
to implement the coexistence of SUs and PUs over the same bands in CRN, which
allows SUs access the unused channels only when PUs are detected to be inactive,
as seen in Fig. 1.1. This mechanism is also called listening-before-transmission,
where the listening function is fulfilled by spectrum sensing at the PHY layer, and
the transmission function refers to the packet scheduling at the MAC layer. Based on
the OSA model, when PUs are detected, SUs must defer their transmissions, vacate
the channel, and then try again later after a predefined duration called blocking time.
Conversely, if PUs are undetected, SUs are allowed to access immediately.
To find more spectrum access opportunities without interfering with the primary
networks, Iran et al. [11] considered two issues: (1) how long and frequently SUs
should sense the spectrum to achieve sufficient sensing accuracy in in-band sensing,
4
1.2 Spectrum Access Mechanisms
Frequncy
Time
PU
SU PU
SU PU
SU
PU
SU
Figure 1.1: The OSA model: The shadowed areas with solid line denote the spectra
occupied by PUs and the white areas with dash line denote the spectra occupied by
SUs.
and (2) how quickly SUs can find the available spectrum band in out-of-band sensing.
The first issue is related to the sensing-throughput tradeoff problem studied in [14].
Generally, in-band sensing aims at the performance optimization of CRN on a specific
channel, which adopts the periodic spectrum sensing policy during the whole channel

access time. In this case, longer sensing time leads to higher sensing accuracy, and
hence results in less interference. However, as the sensing time becomes longer, the
transmission time will decrease accordingly. Therefore, how to decide the optimal
sensing and transmission times over a single channel is an important issue in OSA
model.
The second issue touches upon the channel discovery and selection problems
among multiple channels. Since the spectrum environment changes over time, SUs
must find the new available spectrum bands in real time (out-of-band sensing).
As a result, spectrum discovery time and channel selection time arise. Therefore,
out-of-band sensing must not only discover as many spectrum opportunities as possible
5
1.2 Spectrum Access Mechanisms
Tx
PU
Rx
PU
SAP
SU
x x
: Transmitter; : Re ceiver; SAP: Secondary Access PoT R int.
Figure 1.2: The spectrum sharing model: SUs share the same spectrum with PUs while
the interference power at each PU receiver is lower than a threshold.
but also minimize the expense in finding them. In a multi-channel scenario, the proper
selection of the spectrum sensing order and the next hop spectrum band is another
crucial technique to determine the performance of CRN.
1.2.2 Spectrum Sharing Model
Unlike the OSA model, SUs in the spectrum sharing model are allowed to transmit
simultaneously with PUs, which is shown in Fig. 1.2. However, the resulting
interference from secondary network should not cause performance loss of the primary
network or at least remain below an acceptable level. Therefore, the transmission

power of SUs must be controlled lower than a threshold which is named as interference
power constraint [15–17]. To satisfy this constraint, SUs are assumed to have the
channel state information (CSI) from SUs to PUs.
In either a single-antenna or a multi-antennas scenario, dynamic resource
6
1.2 Spectrum Access Mechanisms
Message
x x
: Transmitter; : Re ceT R iver
Tx
SU
Tx
PU
Rx
PU
Rx
SU
Figure 1.3: The overlay model: SU transmitter has a priori knowledge of PU
transmitter’s message.
allocation becomes the crucial technique to practice the spectrum sensing model,
where transmit power, bit-rate, bandwidth, and antenna beam of SUs need to be
dynamically adjusted based upon the CSI and interference power constraint.
Actually, many existing studies focus on performance optimization issues
regarding resource allocation in a spectrum sharing model, e.g. [18–20].
1.2.3 Overlay Model
Overlay model [21] is another mechanism to relieve the access contention problem,
which adopts the policy that allows the secondary network to take over the operation
of the primary network. Thus, SUs must assist PUs to transmit packets in the first
place and then consider themselves. To implement this model, SUs are assumed to
have perfect a priori knowledge of PUs’ messages. As illustrated in Fig. 1.3, SUs

act as the role of relays to help PUs transmit. If PUs have packets to transmit, SUs
must allocate part of their power for primary transmission, and the remaining power
still can be used for secondary transmission. On the contrary, if PUs have no packet,
SUs can fully make use of their power on their own packet transmissions. Obviously,
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