Dynamic Spectrum Allocation for Cognitive Radio Networks ...

Dynamic Spectrum Allocation for

Cognitive Radio Networks:

A Comprehensive Optimization Approach

by

Ayman Sabbah

A thesis submitted to the

Department of Electrical and Computer Engineering

in conformity with the requirements for

the degree of Doctor of Philosophy

Queen’s University

Kingston, Ontario, Canada

October 2015

Copyright c© Ayman Sabbah

Abstract

In Cognitive Radio Networks (CRNs), the role of the Medium Access Control (MAC)

layer is very important since it enables Secondary Users (SUs) to access the spectrum

without affecting Primary Users’ (PUs) communications. SUs’ and PUs’ geometry

has an effect on the performance of the spectrum sharing algorithms. Also, SUs’

mobility changes the topology of the network as well as interference between the PUs

and SUs. The scenario of multiuser multichannel CRNs introduces new challenges

such as co-channel interference. Consequently, the power budget should be allocated

to the SUs subject to specific constraints. Hence, different SUs will have different

power and interference limits depending on the activity of PUs and on which SUs

will be causing co-channel interference to each other. In addition, enabling Energy

Harvesting (EH) in CRNs is promising to extend their lifetime so that the hybrid

interweave/underlay access scheme is adopted, which means that SUs can access the

active and non-active PU bands.

In this thesis, I propose new optimal and suboptimal Dynamic Spectrum Allo-

cation (DSA) algorithms that employ an interweave/underlay access scheme. I also

study the impact of the following factors: mobility of the SUs, spectrum mobility,

the Primary Exclusive Regions (PERs), the geographical locations of the nodes, con-

nectivity of SUs, correlated shadow fading, and the activity of both PUs and SUs. A

i

cross-layer approach is adopted in order to benefit from the information of the other

layers.

Moreover, to increase both the energy efficiency and the spectrum efficiency, I also

propose a novel algorithm that enables SUs to harvest energy with minimal impact

on their spectrum access performance. The algorithm allows SUs to participate in

making decisions regarding their operating mode. Also, the algorithm ensures that

the energy level in CRN cannot be lower than a specific threshold.

Furthermore, I propose different optimal and suboptimal algorithms that optimize

the power allocation among SUs. The objective is to maximize the Spectral Efficiency

(SE) while respecting the power budget along with the other constraints.

Extensive simulations have been conducted and the results are presented for all

of the proposed algorithms.

ii

”Intellectual growth should commence at birth and cease only at death,”

Albert Einstein.

”Curiosity - the rover and the concept - is what science is all about: the quest to

reveal the unknown,”

Ahmed Zewail.

”Nature is the source of all true knowledge. She has her own logic, her own laws,

she has no effect without cause nor invention without necessity,”

Leonardo da Vinci.

iii

To my parents, with love & respect.

To my dear daughter, ”Harhoora Alsagheera”: Noora, with tenderness.

iv

Acknowledgments

I am deeply grateful to my thesis supervisor Prof. Mohamed Ibnkahla for his con-

tinuous guidance and support during the period of this work. This thesis would not

have been possible without his support and motivation. I am sincerely grateful for

his advice and suggestions.

I also would like to thank the members of my thesis committee, Prof. Abuelmagd

Noureldin, Prof. Hossam Hassanein, Prof. Praveen Jain, Prof. Sonia Aıssa, and

Prof. Andrew Pollard for their time and valuable comments. Many thanks to the

Communications Research Centre (CRC), Industry Canada, for their interest in my

research and for the collaboration opportunity.

My most heartfelt indebtedness goes to my beloved family for the endless support

they provided me with during the period of my study and my whole life. My deepest

gratitude is for my daughter Noora, thanks for giving me strength and hope when

life storms visited us. A big thank you goes to Mrs. Abida Khan and her family, who

stood beside me and Noora when we needed it the most. I can’t but send a special

thanks to Prof. Nihad Dib and his family. There are no words that can express my

gratitude to you, my lovely family.

I also want to thank many people at Queen’s. Special thanks goes to Debie

Fraser, Ita McConnel, Aphra Rogers, Prof. Kim McAuley, and many others. Also,

v

many friends at Queen’s University and all over the world contributed to making the

years of PhD journey enjoyable and I would like to thank all my true friends for their

kindness and for the nice moments we spent together.

vi

Contents

Abstract i

Acknowledgments v

Contents vii

List of Figures xi

List of Acronyms xv

Symbols and Notations xix

Chapter1: Introduction 1

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

1.1.1 Conventional MAC protocols Vs. CR-MAC Protocols . . . . . 3

1.1.2 Classification of MAC protocols for CRNs . . . . . . . . . . . 5

1.1.3 Functionalities to Enable CR Technology . . . . . . . . . . . . 7

1.1.4 Spectrum Mobility Management . . . . . . . . . . . . . . . . . 7

1.1.5 CR-MAC Requirements . . . . . . . . . . . . . . . . . . . . . 9

1.2 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

1.3 Research Objectives and Contributions . . . . . . . . . . . . . . . . . 12

vii

1.3.1 Spectrum Allocation Algorithms with low computational-cost 13

1.3.2 Multiuser Hybrid Interweave/Underlay Resource Allocation . 13

1.3.3 Supporting Mobility of SUs . . . . . . . . . . . . . . . . . . . 14

1.3.4 Enabling Energy Harvesting in the Context of Dynamic Spec-

trum Allocation . . . . . . . . . . . . . . . . . . . . . . . . . . 14

1.3.5 Adaptive Power Allocation Algorithms . . . . . . . . . . . . . 15

1.4 Organization of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . 15

Chapter2: Literature Review 16

2.1 Standardization of CR Technology . . . . . . . . . . . . . . . . . . . 16

2.2 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

2.2.1 Spectrum and Power Allocation . . . . . . . . . . . . . . . . . 17

2.2.2 Energy Harvesting . . . . . . . . . . . . . . . . . . . . . . . . 22

Chapter3: Efficient Spectrum Allocation Schemes for Cognitive

Radio Networks 25

3.1 Network Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

3.2 Traffic Flow Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

3.3 Correlated Shadow Fading Map . . . . . . . . . . . . . . . . . . . . . 29

3.4 Protecting PUs’ Communications . . . . . . . . . . . . . . . . . . . . 34

3.5 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

3.6 The Proposed PAH-DSA Algorithm . . . . . . . . . . . . . . . . . . . 38

3.7 Results and Interpretation . . . . . . . . . . . . . . . . . . . . . . . . 41

3.8 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

viii

Chapter4: Mobility-Supported Dynamic Spectrum Allocation for

Cognitive Radio Networks 48

4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

4.2 Mobility Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49


4.3.1 Constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

4.3.2 Formulating DSA as Optimization Problem . . . . . . . . . . 53

4.4 Description of MSDSA Algorithm . . . . . . . . . . . . . . . . . . . . 57



Chapter5: Integrating Energy Harvesting and Dynamic Spectrum

Allocation in Cognitive Radio Networks 70

5.1 Range of Harvesting . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

5.2 Energy Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

5.3 Framework of Enabling EH in CRNs . . . . . . . . . . . . . . . . . . 76

5.4 Results and Interpretations . . . . . . . . . . . . . . . . . . . . . . . 81


Chapter6: Power Allocation for Cognitive Radio Networks 88


6.2 The proposed Power Allocation Algorithms . . . . . . . . . . . . . . . 91

6.2.1 Optimal Power Allocation . . . . . . . . . . . . . . . . . . . . 91

6.2.2 Cap-Limited Heuristic (CLH) Algorithm . . . . . . . . . . . . 94


ix


Chapter7: Conclusions and Future Work 111

7.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111

7.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114

7.2.1 Impact of imperfect sensing on CR-MAC protocols . . . . . . 114

7.2.2 Mobility Modeling . . . . . . . . . . . . . . . . . . . . . . . . 114

7.2.3 Spectrum Maintenance . . . . . . . . . . . . . . . . . . . . . . 115

7.2.4 Harvesting Strategies and EH Capabilities . . . . . . . . . . . 115

7.2.5 Multi-cell Layout with Relaying and Cooperation . . . . . . . 115

Bibliography 117

Appendices 133

ChapterA: Derivation of mobile SUs’ connectivity Probability 134

x

List of Figures

1.1 Spectrum access schemes . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.2 BMC vs. adaptive channel allocation algorithms . . . . . . . . . . . . 5

1.3 CR-MAC protocols Classification according to learning and optimiza-

tion techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

1.4 Spectrum management framework . . . . . . . . . . . . . . . . . . . . 8

1.5 CR-MAC requirements . . . . . . . . . . . . . . . . . . . . . . . . . . 9

3.1 Packet arrival from a node that has traffic intensity of 0.5 . . . . . . . 29

3.2 Impact of shadow fading on nodes’ connectivity . . . . . . . . . . . . 30

3.3 Effect of fading and pathloss exponent on nodes connectivity . . . . . 32

3.4 Correlated shadow fading versus dij for different values of α . . . . . 33

3.5 Network topology under the correlated shadow fading model . . . . . 34

3.6 Employing the geometry of the networks to protect PUs’ QoS . . . . 35

3.7 Flowchart of the proposed PAH-DSA algorithm . . . . . . . . . . . . 40

3.8 Success probability vs. λSU for different θSU values. Number of chan-

nels = 30 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

3.9 Success probability vs. θSU for different λSU values. Number of chan-

nels = 30 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

xi

3.10 Success probability vs. number of available channels for different λSU

values. θSU = 50 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

3.11 Success probability vs. PER+ε for different λSU values. θSU = 30,

Number of channels = 50 . . . . . . . . . . . . . . . . . . . . . . . . . 46

4.1 Mobility of a SU following the random Waypoint mobility model . . . 50

4.2 Outage percentage, Pout%, vs. speed of SUs for different number of

SUs when the number of available channels = 10 . . . . . . . . . . . . 62

4.3 Outage percentage, Pout%, vs. number of SUs for different speeds when

the number of available channels = 10 . . . . . . . . . . . . . . . . . 63

4.4 Outage percentage, Pout%, vs. N for different number of SUs when

speed=15km/hr and the number of available channels = 10 . . . . . . 64

4.5 Outage percentage, Pout%, vs. N for different speeds when number of

SUs= 10 and the number of available channels = 10 . . . . . . . . . . 65

4.6 Outage percentage, Pout%, vs. speed for different number of SUs when

the number of channels = 10 and N is dependent on the speed of SUs 66

4.7 Outage percentage, Pout%, vs. speed of SUs for different number of

available channels when the number of SUs = 30 and N is dependent

on the speed of SUs . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

4.8 Outage percentage vs. number of SUs for different speeds when the

number of available channels = 10 and N is dependent on the speed . 68

4.9 Outage percentage, Pout%, vs. the number of available channels for

different speeds when number of SUs = 30 and N is dependent on the

speed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

5.1 Components of an EH-enabled SU . . . . . . . . . . . . . . . . . . . . 71

xii

5.2 General Platform for integrating EH with DSA . . . . . . . . . . . . 72

5.3 RoH Concept . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

5.4 Block diagram of an energy detector . . . . . . . . . . . . . . . . . . 74

5.5 CRBS’s decision parameter vs. the energy level in the SUs’ batteries

for different values of κi . . . . . . . . . . . . . . . . . . . . . . . . . 79

5.6 The average energy level in the SUs over time for different values of κ

when θSU is 50 and number of available channels is 10 and z(t) = 0.6 83

5.7 The cumulative % of dropped packets over time for different values of

κ when θSU is 50 and number of available channels is 10 and z(t) = 0.6 84

5.8 The average energy level in the SUs versus the number of channels in

the system for different values of κ at time slot 100 when number of

SUs is 50 and z(t) = 0.6 . . . . . . . . . . . . . . . . . . . . . . . . . 85

5.9 The cumulative % of lost packets versus the number of channels in the

system for different values of κ at time slot 100 when number of SUs

is 50 and z(t) = 0.6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

5.10 The Cumulative harvested energy units for different number of SUs at

time slot 200 and number of available channels is 30 and z(t) = 0.6 . 87

6.1 Total transmitted power versus γthr when the total available power

budget is 4W and σ2AWGN = 10−7 . . . . . . . . . . . . . . . . . . . . 98

6.2 The achieved spectral efficiency versus γthr when the total available

power budget is 4W and σ2AWGN = 10−7 . . . . . . . . . . . . . . . . 99

6.3 The assigned power for each channel in the case of the CLH algorithm

when γthr is set to 0.4mW . . . . . . . . . . . . . . . . . . . . . . . . 100

xiii

6.4 The assigned power for each channel in the case of the ET algorithm


6.5 The assigned power for each channel in the case of the ECM algorithm


6.6 The assigned power for each channel in the case of the CLH algorithm


6.7 The assigned power for each channel in the case of the ET algorithm


6.8 The assigned power for each channel in the case of the ECM algorithm


6.9 The total transmitted power vs. the available power budget when γthr

is set to 0.4mW . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106

6.10 The spectral efficiency vs. the available power budget when γthr is set

to 0.4mW . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107

6.11 The achieved spectral efficiency vs. γthr for the CLH algorithm when

the total available power budget is 4W and σ2AWGN is varying . . . . 108

6.12 The achieved spectral efficiency vs. γthr for the ET algorithm when


6.13 The achieved spectral efficiency vs. γthr for the ECM algorithm when


xiv

List of Acronyms

ADC Analog to Digital Converter

BB Branch-and-Bound

BINLP Binary Integer Non-Linear Programming

BMC Best Multi-Channels

BPF Band-Pass Filter

CC Control Channel

CLH algorithm Cap-Limited Heuristic algorithm

CMAC Access-based MAC

CR Cognitive Radio

CRBS CR Base Station

CRN CR Network

CSI Channel State Information

CSMA/CA Carrier Sense Multiple Access with Collision Avoidance

xv

DAB Direct Access Based

DPA Dynamic Power Allocation

DSA Dynamic Spectrum Allocation

ECM Algorithm Extra-Caution-Measure Algorithm

ED Energy Detector

EE Energy Efficiency

EH Energy Harvesting

ET Algorithm Equally-Treated Algorithm

ETSI European Telecommunications Standards Institute

FBMC Filter Bank Multi-Carrier

HPPP Homogeneous Poisson Point Processes

IID Independent and Identically Distributed

LoS Line of Sight

LTE Long Term Evolution

MAC Medium Access Control

MIMO Multi-Input Multi-Output

MINLP Mixed Integer Non-Linear Programming

MSDSA Mobility-Supported Dynamic Spectrum Allocation

xvi

OFDM Orthogonal Frequency Division Multiplexing

OMMAC Opportunistic Multi-radio MAC

OS-MAC Opportunistic Spectrum MAC

PAD-MAC PU Activity-Aware Distributed MAC

PAH-DSA PU-Aware Heuristic DSA Algorithm

PDF Probability Density Function

PER Primary Exclusive Region

PPP Poisson Point Process

Prsuccess Probability of Success

PU Primary User

QC-MAC QoS-Aware MAC

QoE Quality-of-Experience

QoS Quality-of-Service

RF Radio Frequency

RoH Range of Harvesting

RWPM Random Waypoint Model

SE Spectral Efficiency

SG Smart Grid

xvii

SINR Signal-to-Interference and Noise Ratio

SNR Signal-to-Noise Ratio

SU Secondary User

TDMA Time Division Multiple Access

WLAN Wireless Local Area Network

WS White Space

WSN Wireless Sensor Network

xviii

Symbols and Notations

M The set of SUs

Υ The set of channels

B Channel bandwidth

θPU Density of PUs

θSU Density of SUs

P jPU Transmission power of PU j

P iSU Transmission power of SU i

λPU PUs traffic flow parameter

λSU SUs traffic flow parameter

Gt Gain of the transmitting antenna

Gr Gain of the receiving antenna

Bthr Attenuation threshold

fj Centre frequency of channel j

c Speed of light

P jr,thr Reception sensitivity threshold of node j

ro Normalization distance

Λ(i, j) Connectivity between nodes i and j.

Ro Radius of PER region

xix

ε Guard Region (GR) distance

Ψ2 Variance of shadowing when correlation is not considered

σ2ij Variance of correlated shadowing at link (i, j)

η Pathloss exponent

νik Speed of SU i at waypoint Cik

α Shadowing Correlation coefficient

S(i, j) Mutual distance between nodes i and j

PAj Activity of PU j

SAi Activity of SU i

ψ Set of Time slots

Λ(i, t) Connectivity of SU i at time slot t

γthr Threshold of maximum allowed interference

Ω Cost matrix

s(i, j, t) Normalized Euclidean distance between (i, j) at time slot t

EjIo Aggregated interference from all SUs on channel j

EijIo|S(i, j) Interference from SU i to PU j given the mutual distance

EjKLIo Co-channel interference from SUK to SUL on channel j

γintra Co-channel interference threshold

xx

Pfa Probability of false alarm

Pd Probability of detection

Q(.) Q function

Γ(a, b) Incomplete Gamma function

LoBi The % of battery level in SU i

LoBithr Threshold of minimum allowed LoBi in SU i

κi Local EH controlling parameter for SU i

κ Local EH controlling parameter for all SUs

z(t) Global EH controlling parameter

EjiPUI0 Interference introduced from PU j to SU i

σ2AWGN Variance of the Additive White Gaussian Noise

γconn thr Threshold of SUs connectivity

%,$, ξ,ϕ, ς The Lagrange multipliers

N Optimization round length

xxi

1

Chapter 1

Introduction

Even though spectrum is becoming increasingly scarce, spectrum occupancy rates are

very low [1, 2]. New technologies are currently being adopted to overcome wireless

spectrum shortages. Cognitive Radio (CR) has been proposed in order to leverage

spectrum utilization efficiencies and communication reliability through adaptation of

operating parameters, continuous learning and possessing awareness of surrounding

environments and activities [3].

Within the CR framework, Secondary Users (SUs) are allowed to opportunisti-

cally access the licensed spectrum of the Primary Users (PUs), provided that the

interference level is below an acceptable threshold. If the interference condition is

not satisfied, SUs must evacuate the channel immediately. To ensure that such cri-

teria are met, seamless schemes to dynamically access the spectrum are vital for CR

Networks (CRNs).

SUs can access the spectrum using one of the following schemes: interweave,

underlay, or overlay [4, 5], as depicted in Fig. 1.1. In an interweave scheme, SUs

are not allowed to cause any interference to the PUs and SUs can access the vacant

channels only. Thus, the CRN must keep an eye on the activity of the PUs and

2

immediately vacate the channel once a PU becomes active and move to another

available channel. The concept of moving between the available White Spaces (WSs)

is referred to as spectrum mobility [1].

Figure 1.1: Spectrum access schemes

In an underlay scheme, SUs are allowed to share the channel with an active PU

provided that SU interference levels do not exceed an acceptable threshold. The

maximum acceptable interference level is often referred to as interference temperature

[6]. This will give the CRN access to the spectrum at any time with the cost of

restricting the transmission power in a way that prevents harmful interference to the

PUs.

The overlay scheme allows SUs to simultaneously access the spectrum along with

the PUs provided that the CRN implements an appropriate coding technique to

mitigate the interference caused to the primary network [7]. In this case, the CRN

has to have knowledge about the code books or even messages belonging to the

primary network which may raise security concerns. Each of the three access schemes

1.1. MOTIVATION 3

have their benefits and costs.

1.1 Motivation

The motivation for this thesis arose from the necessity to efficiently utilize the un-

derutilized spectrum. CR research is mainly focused on the physical layer aspects

such as sensing, analyzing the activity of PUs, detecting the available WSs, and in-

terference mitigation [8]. Good progress in this direction has been made and many

techniques and approaches have been proposed in the literature. In order to enable

CR technology, however, other aspects should be addressed, other than the physical

layer issues, such as the issues of Medium Access Control (MAC) layer.

1.1.1 Conventional MAC protocols Vs. CR-MAC Protocols

Resource allocation problems for conventional networks have been thoroughly stud-

ied in the literature [9, 10]. The developed schemes and algorithms are not suitable

for CRNs, however, due to the existence of two different types of users. As such,

certain constraints on the interference levels should be applied while designing the

multiple access algorithms. Spectrum heterogeneity is the main factor that deter-

mines the CRN’s performance, level of protection to the primary network, and the

overall Spectral Efficiency (SE) gain. This heterogeneity imposes the need for new

MAC protocols designed specifically for CRN’s spectrally heterogeneous environment.

A comparison between the conventional MAC protocols and the CR-tailored MAC

protocols is provided in table 1.1.

1.1. MOTIVATION 4

Table 1.1: A comparison between conventional MAC protocols and CR-MAC proto-cols

Conventional MAC CR-MACMain design Efficient allocation of - Efficient utilization of spectrumchallenges and the available channels among SUs & PUs.requirements among the users - Protecting PUs from interference

- Adapt to spectrum mobility- Supervise fairness among SUs

Spectral - Static and constant Heterogeneous (time/frequency/environment spectrum availability space) dependable availability

- Homogeneous spectrumallocation policies

Cross layer Not a necessity due Necessary to enhance CRNs’design to the homogeneity performance

Structurally speaking, CR-MAC protocols are more tightly coupled with the phys-

ical layer and the higher layers as well, as compared to the conventional MAC proto-

cols [11]. Due to this tight coupling, cross-layer design is required where each layer

shares some information among layers for efficient use of networks’ resources and for

achieving high adaptivity. Each layer under investigation is characterized by a few

key parameters that are passed to other layers to help them in determining the best

adaptation rules for their parameters according to the current network status. Cross

layering and tight operational coupling between the layers can achieve higher CRN

performances. In addition, cross layering is a necessity in spectrally heterogeneous

environments and inherent features of the wireless communication system [12].

The solutions proposed for channel assignment problems in traditional wireless

networks typically try to select the best available channel. When Best Multi-Channels

(BMC) schemes are applied in CRNs, the access failure probability for SUs can in-

crease and hence will reduce the network throughput as shown in Fig. 1.2 [13].

This can be interpreted by assuming that there are two available channels and

1.1. MOTIVATION 5

Figure 1.2: BMC vs. adaptive channel allocation algorithms

two pairs of nodes who want to communicate simultaneously. Both of the channels

are suitable to connect the pair (A,B). Also, channel 1 is the best channel for

communications between the pair (A,B). On the other hand, channel 2 cannot be

used for communications between the pair (C,D) for some reason (e.g., the distance

is far away, the power restriction on this channel is strict, etc.). If a BMC scheme

is applied, then channel 1 will be assigned to the pair (A,B) and the pair (C,D)

will not be assigned a channel to communicate simultaneously. Hence, traditional

protocols work well under the static spectrum policies, but they are not suitable for

CRNs since the parameters of these traditional MAC protocols are fixed and are not

designed for opportunistically accessing the licensed spectrum [14].

1.1.2 Classification of MAC protocols for CRNs

MAC protocols for CRNs can be divided into two categories as shown in Fig. 1.3:

Direct Access Based (DAB) and Dynamic Spectrum Allocation (DSA) [15]. Resource

negotiation in DAB protocols is addressed by the simple handshake procedure [16].

Nonetheless, DAB protocols do not allow any global resource optimization. Also,

1.1. MOTIVATION 6

when DAB protocols are adopted, each transmitter-receiver pair tries to maximize

its own benefits in a selfish manner. The simple architecture of DAB protocols may

be able to limit the computational cost, but it will neither allow all SUs to have

fair access nor will it optimize the spectrum globally and efficiently, as opposed to

DSA. On the other hand, DSA protocols integrate complex optimization algorithms

Figure 1.3: CR-MAC protocols Classification according to learning and optimizationtechniques

in order to achieve global optimization goals in an adaptive way. DSA algorithms

should realize intelligent, fair, and efficient allocation of the available spectrum where

each opportunistic user adapts its communication parameters (such as transmission

rate, transmitted power, transmission schedule, and used channels) to the changes

in the wireless environment. For example, fading, users’ activity, required level of

Quality-of-Service (QoS), SUs’ mobility, connectivity, and the geographical locations

are important parameters in the wireless environment. In order to design an optimum

system, all of the above-mentioned constraints should be taken into consideration

while prohibiting high computational cost, complexity, and delay.

Another classification for MAC protocols can be developed based on the archi-

tecture of the network: centralized or distributed. Since distributed networks do

not have a central CR Base Station (CRBS), the coordination between nodes might

1.1. MOTIVATION 7

complicate the DSA algorithm and it is anticipated that misdetection of the nodes’

activities will be much higher as compared to a centralized architecture. Hence, the

former architecture can potentially be more efficient in resource allocation and coordi-

nation by exploiting global information about the network and coordinating between

all of the nodes.

1.1.3 Functionalities to Enable CR Technology

There are many dynamic conditions under which CRNs must be able to operate

robustly. For example, it is expected that the availability of spectrum resources in

CRNs change over time and space [17, 18]. In addition, frequency resources can be

reclaimed by the PUs at any time [19]. To provide reliable communication sessions

for SUs, SUs must periodically sense the spectrum and move between the available

WSs whenever deemed necessary. In order to enable SUs to efficiently access the

available WSs, spectrum mobility management is required, as explained in the next

subsection.

1.1.4 Spectrum Mobility Management

The ultimate goal of spectrum mobility management is to perform successful and fast

spectrum access while minimizing the interference with PUs. A general framework

of the spectrum mobility management and the inter-layer coupling is shown in Fig.

1.4 [19, 20]. Spectrum mobility management has four main functions:

1. Spectrum sensing: SUs must monitor the available spectrum bands, and then

detect WSs. Spectrum sensing is a basic functionality in CRNs, and is closely

related to other spectrum management functions.

1.1. MOTIVATION 8

2. Spectrum decision: based on the spectrum sensing outcome, the spectrum de-

cision procedure assigns the available channels to the SUs.

3. Spectrum sharing: the role of spectrum sharing is to ensure fairness among SUs

especially when multiple SUs request access to the spectrum simultaneously.

4. Spectrum mobility: the spectrum mobility procedure collaborates with the other

three functionalities to detect the events that must initiate the spectrum evac-

uation process.

Figure 1.4: Spectrum management framework

1.1. MOTIVATION 9

1.1.5 CR-MAC Requirements

Looking at spectrum mobility management through the lens of MAC layer, CR-MAC

protocols should address the following multidimensional requirements (as depicted in

Fig. 1.5):

Figure 1.5: CR-MAC requirements

• Protecting the primary network from SUs’ interference: the operation of the

CRNs in the licensed bands should not disrupt the operation of the primary

1.1. MOTIVATION 10

system. Hence, the level of the introduced interference from SUs to PUs should

be monitored all the time and should not exceed a specific threshold.

• Accessing the radio environmental information: the CRN deployment should

provide the CR-MAC layer with radio environmental information by enabling

cross layer design. This means that the physical layer should implement spec-

trum sensing mechanisms and make this information available by storing the

up-to-date radio environmental information in databases.

• Efficient spectrum sharing strategies: CR-MAC is expected to provide dynamic

and efficient spectrum access and resource allocation with the aim to increase

the overall performance of the CRN. Spectrum sharing is a fundamental compo-

nent of the CR technology which enables and provides efficient utilization of the

available resources. It consists of two main functionalities: assigning the chan-

nels dynamically and allocating the available power budget fairly. The channel

allocation process is responsible for finding the most suitable bands, whereas

the power allocation process is responsible for managing the transmission power

of SUs while satisfying the interference constraints along with the total power

budget.

• Enabling Energy Harvesting (EH) is a good feature to have in CRNs especially

in scenarios such as forest monitoring where the nodes will not have access

to any external source of power and they will die upon the depletion of their

batteries. EH is responsible for notifying SUs about the potential opportunities

to harvest energy while maintaining a minimum level of energy to be available

in the CRN. Enabling EH in the context of DSA will in return increase the

1.2. PROBLEM STATEMENT 11

lifetime of CRNs and prevent SUs’ batteries from being fully discharged while

reducing the impact of EH on DSA performance.

• Fairness among SUs: SUs have conflicting interests when it comes to accessing

the spectrum. For instance, each SU will attempt to increase their accessing

time regardless of other SUs’ needs. Hence, fairness measures need to be put in

place in order to allow all SUs to access the spectrum equitably.

• Supporting SUs’ mobility: As the users’ locations change from one place to

another, the available bands and the network topology change. Consequently,

continuous allocation of spectrum to accommodate SUs’ mobility is a major task

that needs to be addressed during the design phase of the CR-MAC protocols.

MAC layer protocols are among the key aspects for designing CRNs. The func-

tionalities that are highlighted with green color in Fig. 1.5 are addressed in this

thesis. In addition, the environmental information is assumed to be provided by the

physical layer to MAC layer using cross layer approach.

1.2 Problem Statement

As mentioned in the previous section, CR research is mainly focused on the physical

layer aspects such as sensing, analyzing the activity of PUs, and detecting the available

WSs. In order to enable CR technology, however, other aspects should be addressed,

other than the physical layer issues, such as the issues of MAC layer.

There are three big challenges that need to be addressed during the design of

MAC protocols. First, the accessing schemes should be adaptive to the surrounding

environment and maximize SUs’ access rate to the spectrum while protecting PUs

1.3. RESEARCH OBJECTIVES AND CONTRIBUTIONS 12

from any interference. This can be achieved by developing efficient DSA algorithms.

Second, the power allocation among the SUs should be optimized in a way that

maximizes the achieved SE while maintaining fairness among SUs and respecting the

networks’ power budgets. Dynamic Power Allocation (DPA) algorithms are necessary

for CRNs. Third, the energy level in the CRN should be monitored and EH should

be enabled in the context of DSA in order to increase the lifetime while reducing the

impact of EH on the opportunities of accessing the spectrum.

Any new algorithms that are developed to address the aforementioned challenges

should take the following issues into consideration: SUs’ mobility, spectrum mobility,

Primary Exclusive Regions (PERs), the geographical locations of the nodes, SUs’

connectivity, interference introduced from SUs to PUs, co-channel interference, the

activity of both PUs and SUs, impact of EH on DSA, and the fairness among SUs.

Furthermore, the complexity and computational-costs of the optimal optimization

approaches proposed in the literature are high, as will be discussed in the next chap-

ters. Thus, it is desirable to develop new algorithms that are able to achieve near

optimal resource allocation results with low complexity and minimal computational-

costs. Despite the ever progressing research in CR, agile MAC protocols that facilitate

DSA, DPA, and EH are still emerging topics.

1.3 Research Objectives and Contributions

In this thesis, I focus on the spectrum management functions from the point of view

of the MAC layer with the objective of designing efficient resource allocation algo-

rithms in multiuser multichannel based CRNs. Multiuser multicarrier communication

systems are considered appropriate for practical CRNs because of their flexibility in


allocating the resources among different users as well as the capability of filling the

available WSs [21]. Moreover, a cross layer approach is adopted in designing a fully

functioning spectrum mobility management framework where the information from

physical and network layers is employed. The specific contributions of this thesis are

described in the following subsections.

1.3.1 Spectrum Allocation Algorithms with low computational-cost

Allowing SUs to access the licensed spectrum without causing harmful interference to

PUs is crucial in enabling the CR technology. In order to increase the utilization of

the spectrum bands, I propose a new DSA algorithm that integrates both interweave

and underlay spectrum access schemes. The proposed algorithm jointly takes into

account the geographical locations of the nodes, the shadow fading, the interference

between the primary and the secondary networks, the interference between SUs that

are transmitting on the same channel, and the communications activity of the users.

A PU-Aware Heuristic (PAH-DSA) algorithm that jointly takes into consideration all

of the aforementioned issues, while requiring low computational- and time-costs, is

developed.

1.3.2 Multiuser Hybrid Interweave/Underlay Resource Allocation

Most of the algorithms so far assume either interweave or underlay access schemes.

This might not be the best approach, however. I use a hybrid interweave/underlay

access scheme throughout the thesis. Also, I compare the performance of the proposed

algorithm using a hybrid access scheme versus either underlay only or interweave only

access schemes. In addition, allocating a channel to more than one SU should be


designed carefully. To do so, I take into consideration the co-channel interference and

optimize spectrum and power allocations to minimize the interference between SUs

accessing the same channel simultaneously.

1.3.3 Supporting Mobility of SUs

SUs’ mobility changes networks’ topology as well as the interference between PUs and

SUs. Moreover, the connectivity between SUs plays a significant role when assigning

channels to multiple mobile SUs. In this thesis, I propose a DSA algorithm, called

Mobility-Supported DSA (MSDSA) that optimizes spectrum allocation when SUs are

mobile. The goal is to maximize the spectrum utilization and the access success-rate.

To achieve this goal, the MSDSA algorithm allocates the bands dynamically by joint

consideration of different factors such as mobility of the SUs, SUs’ connectivity, PERs,

the geographical locations of the nodes, shadow fading, and the activity of both PUs

and SUs.

1.3.4 Enabling Energy Harvesting in the Context of Dynamic Spectrum

Allocation

Enabling EH for CRNs is of value for extending their lifetime. Since SUs can either

access the spectrum or harvest energy, EH should be integrated within the context

of DSA, in order to increase both the energy efficiency and the spectrum efficiency. I

propose a novel algorithm that enables SUs to harvest energy with minimal impact

on their spectrum access performance. The algorithm allows SUs to participate in

making decisions regarding their operating mode. Moreover, the algorithm ensures

that the level of energy in the CRN cannot be lower than a specific threshold.

1.4. ORGANIZATION OF THE THESIS 15

1.3.5 Adaptive Power Allocation Algorithms

I propose two algorithms that optimize power allocation among SUs that were success-

ful in accessing the spectrum. The objective is to maximize the SE while respecting

the power budget constraints. I address the scenario in which CRN has multiple SUs

that are interfering with several PUs. Consequently, the power budget should be

allocated to SUs subject to different power constraints especially so that the hybrid

interweave/underlay access scheme can be adopted, which means that SUs can access

the active and non-active PU bands. Hence, different SUs will have different power

and interference limits depending on PUs’ activity and on the set of SUs that are

expected to access the same channel simultaneously. Moreover, since the complexity

of the optimal algorithms can be high, I propose a suboptimal Cap-Limited Heuris-

tic (CLH) algorithm. CLH algorithm considers assigning power to the SUs from a

discrete set of power levels, as will be discussed later.

1.4 Organization of the Thesis

The remainder of this thesis is organized as follows: Chapter 2 reviews CR technol-

ogy standardization’s efforts and a literature review of the proposed approaches in

the fields of spectrum allocation, power allocation, and EH for CRNs. Chapter 3

presents the system model and the proposed algorithm for DSA. In Chapter 4, I dis-

cuss supporting the mobility of SUs and study its impact on allocating the spectrum.

Chapter 5 presents the proposed approach to integrate EH in the context of DSA. In

Chapter 6, I propose a number of novel algorithms to optimize the power allocation

while satisfying the available power budget and the maximum transmission power of

SUs. Finally, the conclusion and directions for future work are given in Chapter 7.

16

Chapter 2

Literature Review

Several research and regulation efforts have been undertaken in the field of CRNs.

An overview of CR technology standardization’s efforts is presented in section 2.1

and a literature review of the proposed approaches for spectrum allocation, power

allocation, and EH in CRNs is provided in section 2.2.

2.1 Standardization of CR Technology

Over the past few years, there has been significant progress in the spectrum regulation

domain to address the growing demands of radio communication services. The first

CR-based standard, IEEE 802.22, is a centralized one where a CRBS is acting as

the control unit. Moreover, the CRBS might have information about the wireless

environment, users’ activities, and type of transmitted data. Benefiting from such

information in the resource allocation is expected to improve the coexistence between

the networks; hence, the algorithm efficiency will increase. There have been two

amendments of the standard: IEEE 802.22a and the IEEE 802.22b amendments [22].

The IEEE 802.22a amendment proposes standardization for management and control

interfaces and IEEE 802.22b amendment discusses supporting broadband services and

2.2. LITERATURE REVIEW 17

monitoring applications, such as supporting a large number of low energy units and

different QoS classes.

Another example of wireless standards for CR technology is IEEE 802.11.(af, ac,

n) series of standards which are proposed to support the traditional 802.11 Wireless

Local Area Network (WLAN) services [23,24]. Also, IEEE 1900.x series of standards

aim to define a standardized framework for radio resource management in future

wireless systems [25–27]. Another standardization effort is that of the European

Telecommunications Standards Institute (ETSI) to regulate the licensed shared access

among Long Term Evolution (LTE) operators in the band 2.3 GHz and 2.4 GHz [28].

Moreover, IEEE 802.19 standard is aiming to set the framework for coexistence of

several unlicensed systems such as 802.11af, 802.22, and 802.15.4 [29].

2.2 Literature Review

In the following subsections we provide a literature review of the proposed approaches

in the research areas of spectrum and power allocation, and EH.

2.2.1 Spectrum and Power Allocation

Autonomous spectrum allocation algorithms are proposed in [30–34], where the spec-

trum access is accomplished by achieving individual goals like the QoS requirements

or the energy consumption of a given SU. In [30, 31], the focus is on computing the

minimal SU’s transmission power that satisfies the individual SUs’ QoS goals. The

algorithm proposed in [32] employs Stackelberg’s game theory to calculate the opti-

mal resource allocation, while the algorithm proposed in [33] selects the SU pair with

the highest Signal to Noise Ratio (SNR) to utilize the lowest transmission power. [34]


presents a pricing-based non-cooperative game model for power control by SUs. The

objective is to provide throughput fairness among these users while guaranteeing a

minimum Signal to Interference and Noise Ratio (SINR) at the secondary receiver.

In [35], the authors propose a scalable MAC protocol for heterogeneous machine-

to-machine networks. The proposed protocol achieves hierarchical performance by

using different contending priorities and incorporates both the persistent Carrier

Sense Multiple Access (CSMA) and Time Division Multiple Access (TDMA) schemes.

Moreover, an incremental contention priority scheme is used to guarantee fair access

among multiple heterogeneous devices.

A DAB MAC protocol for CRNs (CMAC) is proposed in [36] where each SU is

equipped with a single transceiver to detect PUs’ activities in its vicinity and then

SU shares its sensing information with other SUs. CMAC divides the time frame

into two parts: the beacon period and the data transfer period. Each SU periodically

visits a common control channel to obtain information about PUs’ activities.

In [37], authors introduce a DAB Opportunistic Spectrum MAC (OS-MAC), in

which SUs that want to communicate with each other are grouped together to form

a cluster. Cluster heads are responsible for acquiring the traffic load information

of a channel and for propagating this information within their respective clusters.

OS-MAC uses a probabilistic channel selection scheme to reduce the inter-cluster

interference. However, interference caused by PUs, which is a key role of CR, has not

been implemented in OS-MAC.

In contrast to the autonomous non-cooperative techniques, which aim to optimize

the performance of a single SU through local decisions, the goal of cooperative spec-

trum sharing techniques is to maximize the entire CRN performance by introducing


cooperation among all active SUs. This is done by solving complex system-level op-

timization problems that commonly focus on the overall system performance [38].

In [39], the resource allocation problem is considered as a binary integer optimal

programming in a quasi-static spectrum environment.

In [40], a mixed integer nonlinear programming optimization model for spectrum

sharing is developed where the interference avoidance is considered. Another approach

is presented in [41], where an objective of maximizing the throughput of the whole

CRN is achieved by a suboptimal strategy for channel assignment.

Queuing theory is used as well to model the performance of single-channel CRNs.

For example, In [42], a simple network consisting of one SU and one PU is modeled

as an M/D/1 queuing model where both users share a single channel [43]. Another

scheme is introduced in [44], where accessing management is considered for a single

hop CR. The average transmission rate of a stationary SU, that is providing video

services, is studied using M/M/1 queuing model. In [45], the network with a single

PU and multiple SUs, sharing a single channel, is modeled with an M/G/1 queuing

model. The considered networks are simple and do not reflect real-life deployment

scenarios.

Also, a distributed QoS-Aware MAC (QC-MAC) protocol for multi-channel CRNs

is proposed in [46]. It deals with QoS-aware transmissions with the objective of

minimizing the PU-to-SU collision rate. In [47], the authors propose a distributed

PU Activity-Aware Distributed MAC (PAD-MAC) protocol for heterogeneous multi-

channel CRNs that selects the best channel for each SU to enhance its throughput.

PAD-MAC controls SUs’ activities by allowing them to exploit the licensed channels


only for the duration of estimated idle slots. However, the distributed MAC archi-

tecture still has a long path to go in order to be considered as a reliable solution for

a minimal-interference cognitive communication holding to its strict requirements.

Two schemes that are based on game theoretic approaches are discussed in [48,49].

In these models, SUs behave selfishly where each node will use all of its available

power to transmit data. Recent advances in employing bargaining game theoretic

approaches for DSA introduce the Nash bargaining approach as an efficient solution

[50]. The objective is to perform a joint channel and power allocation which maximizes

the SU throughput while taking into consideration the PU protection requirements.

The bargaining game allows SUs to reach a mutually beneficial agreement. However,

SUs have conflicts of interest; hence no agreement may be imposed on any individual

without its consent [51].

In [52], the authors propose a Quality-of-Experience (QoE)-driven channel alloca-

tion scheme for SUs and CRN base station. The historical QoE data under different

primary channels is collected by the SUs and delivered to the base station which

will then allocate the available channel resources to the SUs based on their QoE ex-

pectations and maintain a priority service queue. The modified ON/OFF models of

channels and service queue models of SUs are jointly investigated for this channel

allocation scheme.

An algorithm called Double hopping is proposed in [53] where a hopping pattern is

generated in a way that minimizes the time of using any channel. This will maximize

the number of channels used for a transmission and might also reduce the interference

to the PUs. This algorithm may lead to channel over-assignment, however, and the

system would not be able to guarantee a good QoS for SUs.


A biologically inspired spectrum sharing algorithm is proposed in [54]. It is based

on an insect colony’s adaptive task allocation model. A genetic algorithm is devel-

oped in [55] to jointly solve the channel allocation and power control problems. A

localized knowledge of the network is assumed which reduces the signaling overhead.

The algorithm needs a large number of iterations to converge, however.

An opportunistic Multi-Radio MAC (OMMAC) is proposed in [56], where a multichannel-

based packet scheduling algorithm is employed and packets are sent using the channel

with the highest achievable bit rate. Another algorithm that is based on Carrier Sense

Multiple Access with Collision Avoidance (CSMA/CA) is proposed in [57].

In [58], a spectrum access strategy for SUs is presented. This strategy is based on

the α-retry policy in queueing theory, where a pre-empted SU joins the transmission

queue with probability α for retrial. A two-dimensional discrete-time Markov chain

model is used to analyze performance of the proposed channel access strategy.

Employing an overlay access scheme, in which the SU transmitter employs part of

its resources to help the communication between the PUs, to enable SUs to access the

spectrum is proposed in [59–61]. Using Orthogonal Frequency Division Multiplexing

(OFDM) as the transmission technology for CRNs is studied in [62, 63] where the

resource allocation process is formulated as an optimization problem that looks for

the optimal power and subcarrier allocation. Employing an underlay access scheme

is addressed in [64,65]. The focus in [64] is on the routing process in ad-hoc underlay

based CRNs, while the authors of [65] study using a multi-antenna scenario to increase

the spatial diversity, where the resource allocation process is defined in such a way

that it maximizes SUs’ performance and diminishes their impact on the PUs.


In [66], a spectrum scheduling scheme is proposed for mobile CRN by formulat-

ing a throughput maximization problem and solving it using a bipartite graph. The

mobility of the nodes is assumed to be deterministic and an interweave access scheme

is employed. A one-dimensional mobility model is studied in [67] where the SUs

are assumed to be moving only in the x-direction of the cartesian coordinates. The

studied network consists of one PU and four SUs where SUs can access the channel

using an interweave access scheme. In [68], a handoff management scheme is proposed

for mobile SUs. It allows SUs that are moving according to a linear mobility model

to switch from one primary network to another. This scheme adopts a multi-agent

based solution that uses a trading and pricing system between SUs and the primary

networks. Also, in [69], a mobility management scheme for multi-cell CRN is pro-

posed. SUs are allowed to access the spectrum using an interweave scheme under this

management scheme.

2.2.2 Energy Harvesting

As with DSA algorithms, in order to enable CRNs to harvest energy, the dynamistic

nature of the available EH opportunities in CRNs should be taken into considera-

tion. EH has been studied and reported in the literature for regular Wireless Sensor

Networks (WSNs). For instance, a game-theoretic sleep and wake-up strategy [70],

queuing theoretic transmission policies [71], and modified back-pressure-based algo-

rithms with energy queues [72] are proposed to model a WSN that consists of one

EH transmitter and one receiver.

Some studies that investigate enabling EH in CRNs have been conducted as well.

For example, the authors of [73] analyze the achievable throughput of a SU, which


harvests energy from ambient sources while opportunistically accessing the spectrum.

The primary traffic is modeled as a time-homogeneous discrete Markov process.

In [74], the performance of a SU with EH capability is studied, where the goal

is to determine an optimal spectrum sensing policy that maximizes the expected to-

tal throughput subject to an energy causality constraint and a collision constraint.

The energy causality constraint keeps the consumed energy lower than the harvested

energy, and the collision constraint mandates that the probability of accessing the

spectrum while a PU is active is equal to or less than a predefined maximum proba-

bility of collision.

In [75], the maximum stable throughput region for a simple CRN, with one SU

pair and one PU pair, is analyzed. The SU transmitter harvests ambient energy while

the PU transmitter is assumed to be plugged into a reliable power supply.

Authors of [76] analyse the optimal random access for a SU with EH capabilities.

The access probabilities are obtained under the constraints of primary queue stability

and primary queueing delay being kept below a specified value.

In [77], an EH and information transfer protocol in a cognitive two-way relay

network is developed. In this protocol, a SU harvests energy from a neighbouring

PU while assisting the primary’s transmission in an overlay setting. Specifically, the

network is assumed to have two PUs that exchange information through a SU which

will first harvest energy from these two PUs and then use the harvested energy to

forward the remaining primary signals along with the secondary signals in the second

part of the operation cycle.

The work in [78] proposes a probabilistic access strategy by a SU based on the

number of energy units at its energy queue. The system is assumed to have one SU


and one PU. The authors investigate the effect of the energy arrival rate, and the

capacity of the energy queue on the SU’s performance.

25

Chapter 3

Efficient Spectrum Allocation Schemes for

Cognitive Radio Networks

CRNs have the ability to reconfigure and adapt their software components and archi-

tectures, thus enabling flexible delivery of broad services, as well as sustaining robust

operation under highly dynamic conditions [5]. The geometry of the SUs and PUs

has an effect on the performance of the spectrum sharing algorithms. Since PUs have

the right of claiming the spectrum whenever needed, it is crucial to ensure that PUs’

satisfaction and QoS are not impacted by SUs’ activity on the shared bands.

A review of the literature shows that several factors have not been comprehensively

studied. For instance, the impact of the correlated shadow fading maps on the DSA

algorithm was not investigated. Also, fairness between the SUs is an important issue

that needs to be addressed during the design of the DSA algorithm. Moreover, the

practical implementation of complex algorithms should be considered.

In order to increase the utilization of the spectrum bands, I propose a DSA al-

gorithm that integrates both interweave and underlay spectrum access schemes. The

proposed algorithm will jointly take into account the geographical locations of the

26

nodes, the shadow fading, the interference between the primary and the secondary

networks, the co-channel interference between SUs that are transmitting on the same

channel, and the communications activity of the users. Moreover, a heuristic DSA

algorithm that jointly takes into consideration all of the aforementioned issues, while

requiring lower computational- and time-costs, is developed.

The contributions in this Chapter are illustrated in the following steps. First, the

problem of dynamic spectrum sharing is formulated as a binary integer optimization

problem. Second, due to the complexity of this optimization problem, a PAH-DSA

algorithm is developed. Third, the surrounding environment is taken into considera-

tion by employing the model of a correlated shadow fading map. Moreover, fairness

between SUs is considered where any SU cannot access more than one channel at any

given time slot. Furthermore, an extra measure of protecting PUs’ QoS is enforced

by not allowing SUs that are close to a specific active PU to access the channel that

is assigned to this PU. Also, multiuser access to the same channel is enabled provided

that the interference between the SUs is acceptable. Lastly, to increase the chances

of successful access to the spectrum, a hybrid interweave/underlay access scheme is

adopted. To the best of my knowledge, a study of these issues jointly has not been

reported in the literature previously.

The remainder of this Chapter is organized as follows: In the next section, I de-

scribe the network model. The communication traffic model is described in Section

3.2, and the correlated shadow fading maps are explained in Section 3.3. The extra

measure in protecting PUs’ communication is provided in Section 3.4. The formula-

tion of the Optimization problem is given in Section 3.5. The proposed algorithm is

presented in 3.6. Simulation results and interpretations are presented in Section 3.7.

3.1. NETWORK MODEL 27

Finally, the Chapter’s concluding remarks are given in Section 3.8.

3.1 Network Model

The CRN is assumed to have M SUs and one CRBS which is assumed to be aware of

PUs’ activity. This is made possible by employing some sensing techniques [79]. The

CRBS is located in the middle of the operating area and its main duty is to coordinate

the assignment of the channels and optimize the spectrum allocation. Both the CRN

and the primary network are located in close proximity and the topology of each of

the networks follows a homogeneous Poisson Point Process (PPP) with nodes’ den-

sity of θPU and θSU for the primary and secondary networks, respectively [80]. The

transmission power of SUs and PUs are referred to as PSU and PPU respectively. The

system is assumed to have Υ PUs where each PU is assigned one channel with a band-

width B. Hence, the number of channels is equal to the number of PUs. The CRN

physical layer is assumed to be Filter Bank Multi-Carrier (FBMC). The interference

from other PUs on the channels that they are not transmitting on is considered neg-

ligible. This can be justified by the fact that FBMC has very small sidelobe which

significantly reduces the interference [81]. Also, Channel State Information (CSI)

and sensing results are assumed to be sent from SUs to the CRBS using a dedicated

Control Channel (CC) that is not affected by the activity of PUs.

3.2 Traffic Flow Model

In order to increase the efficiency of the DSA algorithm and reduce the collision rate

between SUs, the operating time is divided into time slots where SUs are allowed to

begin transmitting only at the start of any time slot. The communications traffic flows

3.2. TRAFFIC FLOW MODEL 28

of the PUs and SUs are modelled as Bernoulli arrival processes with parameters λPU

and λSU , respectively [82]. Bernoulli process is the discrete-time analog of the Poisson

arrival process where the arrivals of the packets can take place at some time slot k.

Mathematically, the traffic of a SU can be described as a point process consisting of

a sequence of arrival instants T1, T2, ..., Tm, ... measured from the origin To = 0.

The number of arrivals for slot k follows a binomial distribution:

PNk = n =

(k

n

)λnSU(1− λSU)k−n (3.1)

Also, the number of time slots between two arrivals is geometrically distributed with

parameter λSU and the probability of having O packets arriving at the same timeslot

is given by:

PAn = O = λSU(1− λSU)O , O ∈ N0 (3.2)

Under this model, communication sessions are allowed to begin at the start of any

time slot and if a SU generates a packet before the start of a time slot, this packet has

to be stored in a local buffer and the SU would wait till the next time slot to contend

for a channel. The SU then will initiate a communication session if it is granted

access. It is important to note that the change in the PUs’ communication behaviour

does not happen frequently and can be assumed to be fixed for a minimum duration

of one time slot. Hence, the sensing information is valid for at least one time slot.

Fig. 3.1 shows the activity of a node that has traffic intensity of 0.5, which means

that this node will be producing packets with a probability of 0.5 at each time-slot.

This node is producing one packet at time slots 1, 3, 5 and 8, and two packets at time

slot 2.

3.3. CORRELATED SHADOW FADING MAP 29

Figure 3.1: Packet arrival from a node that has traffic intensity of 0.5

3.3 Correlated Shadow Fading Map

When the transmitter and the receiver are located in free space with a Line of Sight

(LOS) link, we can use (3.3) to calculate the level of the received power as follows:

Pr = PtGtGr

(λ

4πS(i,j)

)2

(3.3)

where Pt is the transmitted power, Gt is the gain of the transmitting antenna, Gr

is the gain of the receiving antenna, λ is the wavelength of the transmitted signal,

and S(i, j) is the Euclidean distance between the transmitter i and the receiver j.

The strength of the received signal depends also on the surrounding environment and

its topography, however. This can be modeled by shadow fading [83]. The standard


deviation of the shadow fading (σ) is in the range of 3 to 12 dB [84]. Shadow fading

affects the connectivity of the nodes where some nodes that are far away from each

other might be connected while some neighboring nodes might be disconnected due

to the level of fading between them. The impact of shadow fading on the connectivity

of the network is shown in Fig. 3.2.

Figure 3.2: Impact of shadow fading on nodes’ connectivity

The transmission range of nodes is considered as a stochastic process and in or-

der to find the probability of connectivity between the pair of nodes i and j let us

define the event of having a direct communication link between them as Λ(i, j). The

conditional probability of having a link given the Euclidian distance is defined as [85]:

P (Λ(i, j)|S(i, j)) = P (β(i, j) ≤ βthr|S(i, j))

= Q

(10η

σlog10

S(i, j)

r0

) (3.4)

where η is the pathloss exponent and σ is the standard deviation of the shadow


fading. Q(.) is the Q-function defined as:

Q(x) =1√2

∫ ∞x

exp

(−u

2

2

)du (3.5)

βthr is the attenuation threshold that considers the reception sensitivity of the

nodes. When the attenuation, experienced by any channel between (i, j), is less than

βthr, a direct communication will be available through this channel. βthr is defined

as:

βthr = 10 log10

P it

P jr,thr

(3.6)

P it is the transmission power of node i and P j

r,thr is the reception sensitivity of node

j (i.e. if the received power at node j from node i is larger than P jr,thr, then a

direct communication is available through this channel. ro is a normalization term

which represents the maximum range that a node can reach directly under the purely

geometric link model (as shown in Fig. 3.2(a)) and is defined as:

ro = 10βthr10η (3.7)

Figure 3.3 shows the link probability over s/ro for η = 2 and 4 with different

values of σ. For instance, when η = 4 and σ = 6dB, there is still a link probability

of more than 12% at a distance S = 1.5ro.

To accurately model the statistical nature of the channels, the correlation between

the shadowing that is affecting different links should be considered due to the fact that

the links within the same vicinity are impacted by the same nearby large objects. Also,

the correlation of shadow fading is of high importance when studying CRNs due to

their coexistence with the primary network [84]. As such, the proposed DSA algorithm


Figure 3.3: Effect of fading and pathloss exponent on nodes connectivity

takes the correlation and variance of the shadowing into account when assigning

channels to SUs in such a way that guarantees a minimal level of shadowing to be

introduced to SUs, and the correlated shadow fading can be modeled as an exponential

correlation model [86]. The normalized correlation function can be written as [87]:

r(x) = e−αx, x ≥ 0 (3.8)

where α is the correlation coefficient. The variance of the correlated shadow fading

for link (i, j) can be calculated using:

σ2ij = Ψ2

[1 +

1

‖d(i, j)‖exp(−‖d(i, j)‖α)− 1

‖d(i, j)‖

](3.9)

where Ψ2 is the variance of the shadow fading when the correlation is not taken into

consideration, and ‖d(i, j)‖ is the distance between nodes i and j. For an intermediate

environment between suburban and urban areas, a value of α = 1/20 was suggested

in [88]. Fig. 3.4 shows the correlated shadow fading versus the distance between the


transmitter and the receiver for different values of α. As shown, σ2ij will change slowly

when the value of α is relatively low, and vice versa.

0 20 40 60 80 1003.6

3.7

3.8

3.9

4

||dij||

σ ij2

α=0.025α=0.05α=0.67α=0.1

Figure 3.4: Correlated shadow fading versus dij for different values of α

Fig. 3.5 shows an example of both of the networks being under the effect of a

correlated shadow fading map. The density of the nodes is set to 10 for each of the

networks. The connectivity between the nodes is shown in the figure as well. Each

color represents a different shadowing level and the contour lines show the correlation

in the standard deviation of shadow fading within the simulation region.

3.4. PROTECTING PUS’ COMMUNICATIONS 34

Figure 3.5: Network topology under the correlated shadow fading model

3.4 Protecting PUs’ Communications

In order to protect the PUs from harmful interference, while facilitating the hybrid

access scheme, the concept of PER is used, where SUs are not allowed to transmit on

a band that is allocated for a PU if they are located inside the PER of this PU [89].

As such, the network geometry is employed to protect the communication of PUs.

Let the radius of the PER be Ro, as shown in Fig. 3.6. All SUs that are transmit-

ting on the same channel of a PU must be at least ε Guard Region (GR) distance away

from any primary receiver. Practically, the exact location of the primary receivers is

unknown to the SUs. Hence, as shown in Fig. 3.6, SUs that lie inside a circle centered

at the primary transmitter with a radius of (Ro + ε) cannot transmit on the same


Figure 3.6: Employing the geometry of the networks to protect PUs’ QoS

channel as the nearby PU. While this can be considered to have a negative impact

on the performance of the DSA algorithm, it can be viewed as a great opportunity

to increase the lifetime of the CRN by directing the affected SUs to harvest energy

from the nearby active PUs, instead of contending to access the spectrum, as will be

explained in chapter 5.

Practically speaking, the impact of SUs on PUs’ communications is captured by

the expected amount of interference introduced to the PUs due to the SUs’ activity.

Let Ijo be the aggregated interference power from all SUs to PU j. Assuming that all

SUs are located at the border of PU j PER region, the expected interference power

experienced by PU j is given by [90]:


EjIo = Gjo(Ro + ε)−η

M∑i=1

exp

(1

2

(σij

log 10

10

)2)P iSU (3.10)

where Gjo = (c/4πfj)

2, fj is the centre frequency of the channel under investiga-

tion, c is the speed of light, P iSU is the transmission power of SU i, η is the pathloss

exponent, and σij is the standard deviation of the correlated shadowing at link (i, j).

As expected, when the radius of the PER and ε increases, the aggregated interference

will decrease. A more realistic scenario is to consider the actual locations of the SUs

when calculating the aggregated interference. This can be done as follows:

EjIo =M∑i=1

GjoS(i, j)−η exp

(1

2

(σij

log 10

10

)2)P iSU

, s.t. S(i, j) ≥ Ro + ε, ∀j ∈ Υ

(3.11)

where S(i, j) is the mutual Euclidean distance between SU i and PU j. Moreover,

the activity of SUs affects the value of EjIo since all of the SUs will neither be active

at a specific time slot nor will they be assigned the same channel. Let xij(t) ∈ 0, 1

be a binary integer variable where 1 means that PU j’s channel is allocated to SU i

to transmit at time slot t, and 0 means that channel j is not allocated to SU i. The

actual aggregated interference AEjIo is calculated as follows:

AEjIo =M∑i=1

xij(t)GjoS(i, j)−η exp

(1

2

(σij

log 10

10

)2)P iSU ,

s.t. S(i, j) ≥ Ro + ε, ∀j ∈ Υ

(3.12)

3.5. PROBLEM FORMULATION 37

3.5 Problem Formulation

In order to satisfy the protection needs of the PUs, let us introduce the maximum

interference threshold as following:

AEjIo < γthr,∀j ∈ Υ (3.13)

where γthr is the maximum allowed interference level to be introduced to the PUs

from SUs. Equation (3.13) ensures that PUs are experiencing an interference that is

less than the allowed level all the time. This constraint holds for all of the PUs. The

optimization problem can then be formulated as follows:

Minimize

M∑i=1


(1

2

(σij

log 10

10

)2)P iSU ,∀j ∈ Υ (3.14)

such that:

S(i, j) ≥ (Ro + ε), ∀ pairs (i, j) (3.15)

AEjIo < γthr, ∀j ∈ Υ (3.16)

1 ≤M∑i=1

xij + PAj, ∀ i ∈M, j ∈ Υ (3.17)

Υ∑j=1

xij ≤ 1, ∀ i ∈M (3.18)

M∑K=1

EjKLIo < γintra, ∀L ∈M,K 6= L,∀j ∈ Υ (3.19)

where PAj is the activity of PU j. Condition (3.15) prevents allocating channel j

3.6. THE PROPOSED PAH-DSA ALGORITHM 38

to any SU that is located in the vicinity of PU j. Also, condition (3.16) guarantees

that the level of the interference is less than the acceptable threshold. Condition

(3.17) assures that any channel is assigned to as many users as possible and that the

channels are allocated to at least one active user. Moreover, condition (3.18) restricts

the maximum number of channels that any SU can access at any time slot to one.

Finally, condition (3.19) ensures that the interference between SUs K and L that

are assigned channel j simultaneously, EjKLIo , does not exceed a prescribed limit,

γintra. EjKLIo is defined as follows:

EjKLIo = Gj

oS(K,L)−η exp

(1

2

(σKL

log 10

10

)2)PSU (3.20)

3.6 The Proposed PAH-DSA Algorithm

The problem formulated in the previous section is a Binary Integer Non-Linear Pro-

gramming (BINLP) which is difficult to solve. Alternatively, the binary optimization

variable can be relaxed and then the new problem can be solved using the primal-dual

interior-point method. Such an approach still requires huge computational resources

to find the solution for six sets of Lagrangian multipliers, however.

To overcome this obstacle, I propose a PAH-DSA algorithm that provides a sat-

isfactory protection level to the PUs while quickly assigning the available bands to

the SUs. The algorithm will employ both interweave and underlay access schemes.

First, let us define the expected interference caused by SU i to PU j given the mutual

Euclidean distance between them as follows:

EijIo|S(i, j) = GjoS(i, j)−η exp

(1

2

(σij

log 10

10

)2)P iSU (3.21)


Next, let Ω be the cost function where Ωij is the cost of assigning channel j to

SU i. Ωij is defined as follows:

Ωij =(LN |S(i, j) < Ro + ε)(√

2− s(i, j))EijIo|S(i, j)

+ 0.6PAj + (SAi + 1)−1 ∀ i, j(3.22)

where LN is any large number greater than 1, s(i, j) is the normalized Euclid-

ian distance between SU i and PU j, and SAi is the activity of SU i. The term

(LN |S(i, j) < Ro + ε) increases the cost of assigning channel j to SU i considerably,

if it is close to PU j. Also, the term (√

2− s(i, j)) ensures maximizing the Euclidian

distance between SUs and PUs that will be transmitting on the same channel. For

example, when s(i, j) has a high value, this means that PU j and SU i are far away

from each other, and hence the cost will be reduced. On the other hand, as s(i, j)

decreases, the cost will increase. The second term in (3.22), 0.6 PAj, ensures use of

an interweave scheme first by adding an extra cost for invoking the underlay scheme.

The last term represents the activity of SUs; if SU i is very active, it will get a higher

priority to access the spectrum. The flow chart of the proposed algorithm is shown

in Fig. 3.7.

The mechanism of the PAH-DSA algorithm is described as follows: the algorithm

will start by checking the needs of all SUs for spectrum. Once the CRBS receives this

information, it will calculate the cost function for all of the SUs. Next, the CRBS

will define two new sets of channels, Υ1 =set of the channels that do not have active

PUs and Υ2 =set of the channels that have active PUs. After that, the channels

that are in Υ1 will be allocated to the SUs that have the lowest cost and introduce

the least amount of noise to the system. Then the algorithm will check if the demand


Figure 3.7: Flowchart of the proposed PAH-DSA algorithm

3.7. RESULTS AND INTERPRETATION 41

from all SUs is satisfied. If not, the underlay access scheme will be invoked to assign

channels in Υ2 to the demanding SUs provided that the interference level is less than

the threshold and SUs’ locations are outside the PER and GR of the active PUs.

A maximum of one channel is assigned to any SU at a given time slot. This will

guarantee a level of fairness between SUs to some extent, where the demanding SUs

will not be affecting the requests from other SUs. Finally, when the number of SUs

is large as compared to the number of available channels, the algorithm will double

check whether the conditions of PER, interference introduced to the primary network,

and co-channel interference conditions are met or not. If these conditions are met,

then the algorithm will declare the allocation results and exit. If not, SUs that do not

fulfill any of these conditions will be prohibited from transmission. However, when

SUs number is comparable to the number of channels, the double check step is not

necessary since the algorithm will efferently allocate the channels to the SUs that are

faraway from the PUs.

The computational complexity of the PAH-DSA algorithm is lower than or equal

to O(max(MΥ1,MΥ2)) + O(logM), which is much lower than the complexity of

finding the optimal solution (O((MΥ)3 )).

3.7 Results and Interpretation

The performance of the proposed algorithm is evaluated using the probability of suc-

cess (Prsuccess) parameter, which is the probability that SUs with data to transmit

succeed in getting channels assigned to them. The correlation of the shadow fading

map is set to 1/20 and the pathloss exponent (η) is set to 4. Also, the commu-

nication activity of the PUs (λPU) is set to 0.5. The performance of the proposed


algorithm is compared to two different algorithms: a CSMA/CA-based algorithm and

an interweave-only algorithm.

Fig. 3.8 shows the effect of SUs’ communication traffic (λSU) on Prsuccess

when the number of available channels is 30. The PAH-DSA algorithm always out-

performs the CSMA/CA-based algorithm for all values of λSU . When PER is taken

into consideration, the performance will slightly degrade, especially when λSU > 0.3

for θSU = 50 and when λSU > 0.7 for θSU = 30. This is due to the increased demand

from SUs, which in return leads to assigning channels to some SUs even if they are

located near the active PUs. Moreover, the PAH-DSA algorithm outperforms the

interweave-only algorithm when λSU is less than 0.5. When λSU is larger than 0.5,

the interweave-only algorithm will be a better option. This is due to the fact that

the PAH-DSA algorithm puts the protection of PUs’ communications first regardless

of how this may affect SUs’ QoS due to rejecting their access to the spectrum.

A plot of Prsuccess versus the θSU is given in Fig. 3.9. The PAH-DSA algo-

rithm outperforms both CSMA/CA-based and interweave-only algorithms when λSU

is small. However, the interweave-only algorithm outperforms the PAH-DSA algo-

rithm when SUs are acting on saturated communication mode (λSU = 1) and the

θSU is larger than 30. This is due to the fact that PAH-DSA algorithm puts the

protection of PUs’ communications as the first and foremost priority that comes even

before satisfying the needs of SUs to access the spectrum.

The impact of the number of available channels on Prsuccess is shown in Fig.

3.10. As expected, when the number of channels increases, the performance of all of

the three algorithms improves. Also, the PAH-DSA algorithm outperforms both of

the other algorithms and can reach a success rate of 95% in cases where the number


0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

λSU

Pr

succ

ess

PAH, No PER, θSU

= 30

PAH, with PER, θSU

= 30

Interweave only, θSU

= 30

CSMA/CA, No PER, θSU

= 30

CSMA/CA, with PER, θSU

= 30

PAH, No PER, θSU

= 50

PAH, with PER, θSU

= 50

Interweave only, θSU

= 50

CSMA/CA, No PER, θSU

= 50

CSMA/CA, with PER, θSU

= 50

Figure 3.8: Success probability vs. λSU for different θSU values. Number of channels= 30

of channels is greater than 30 and θSU is equal to 50.

Fig. 3.11 studies the impact of (Ro+ε) radius on the performance of the algorithms

when the θSU = 30 and the number of channels = 50. In the case of a small λSU ,

increasing Ro + ε radius does not affect the performance of the PAH-DSA algorithm


10 15 20 25 30 35 40 45 500.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

θSU

Pr

succ

ess

PAH, No PER, λSU

= 0.3

PAH, with PER, λSU

=0.3

Interweave only, λSU

=0.3

CSMA/CA, No PER, λSU

=0.3

CSMA/CA, with PER, λSU

=0.3

PAH, No PER, Saturated modePAH, with PER, Saturated modeInterweave only, Saturated modeCSMA/CA, No PER, Saturated modeCSMA/CA, with PER, Saturated mode

Figure 3.9: Success probability vs. θSU for different λSU values. Number of channels= 30

since the channels are already assigned to the furthest SU from the active PU. When

λSU = 1 and Ro + ε = 100 m, however, the performance will degrade by 7% , as

compared to when Ro + ε ≤ 70 m, due to the increase in the SUs’ demand to access

the spectrum, which will lead to an increase in the failure of assigning appropriate


channels to a portion of the SUs. For the CSMA/CA-based algorithm, as (Ro + ε)

radius increases, the success rate will drop dramatically.

10 20 30 40 50 60 700

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Number of Channels

Pr

succ

ess

PAH, No PER, λSU

= 0.3

PAH, with PER, λSU

=0.3


=0.3


=0.3


=0.3


Figure 3.10: Success probability vs. number of available channels for different λSUvalues. θSU = 50

3.8. CONCLUDING REMARKS 46

10 20 30 40 50 60 70 80 90 1000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

PER+ε (m)

Pr

succ

ess

PAH, No PER, λSU

= 0.3

PAH, with PER, λSU

=0.3


=0.3


=0.3


=0.3


Figure 3.11: Success probability vs. PER+ε for different λSU values. θSU = 30,Number of channels = 50

3.8 Concluding Remarks

In this Chapter, a PAH-DSA algorithm was proposed to dynamically allocate spec-

trum in CRNs. The proposed algorithm employs a hybrid interweave/underlay ap-

proach and supports the mobility of SUs. The results demonstrate a high success

level in fulfilling CRNs’ demands for spectrum access while protecting the QoS of the


primary network. Simulation results have also shown that the proposed algorithm

outperforms traditional spectrum allocation algorithms.

48

Chapter 4

Mobility-Supported Dynamic Spectrum Allocation

for Cognitive Radio Networks

4.1 Introduction

Since nodes’ connectivity is coupled with their locations and the Euclidean distances,

it will be dynamically changing when nodes are moving. Moreover, mobility will

change the network topology and hence will cause losses of some links between nodes.

In addition, it can change the geographical distribution of the traffic and the interfer-

ence pattern will be changing accordingly. Taking mobility into consideration while

designing DSA algorithms is required for an effective design. A Mobility-Supported

Dynamic Spectrum Allocation (MSDSA) is essential for enabling the future mobile

CRNs.

The schemes proposed in the literature are mainly focused on the spectrum al-

location without jointly considering several important factors such as a multiuser

multichannel scenario, the geographical locations of the nodes, the correlated shadow

fading maps, the PER, and the hybrid allocation scheme. Moreover, only a few algo-

rithms took users’ mobility into consideration. The impact of mobility on DSA and

4.2. MOBILITY MODEL 49

how to mitigate this effect was not addressed thoroughly, however.

The contributions in this chapter are as follows:

• I propose a DSA algorithm that is designed to support mobility by considering

SUs’ behaviour during the design phase.

• The algorithm employs a PER region concept to provide an extra measure

of protection for PUs’ communications. It also jointly considers users’ activ-

ity, correlation in shadow fading, and the geographical locations of the nodes.

Moreover, a hybrid access scheme is adopted to increase the success rate, as

described in the previous chapter.

• The proposed algorithm considers a multichannel multiuser scenario where the

system will have multiple channels and more than one SU will be allowed to

access the same channel simultaneously.

• A suboptimal MSDSA algorithm is proposed in order to reduce the computa-

tional costs of solving the optimization problem.

The remainder of this chapter is organized as follows: Section 4.2 explains the

mobility Model. The problem formulation is given in Section 4.3, while the subopti-

mal MSDSA algorithm is explained in Section 4.4. Extensive simulation results and

interpretations are given in Section 4.5. Conclusions to the chapter appear in Section

4.6.

4.2 Mobility Model

Modeling the mobility can be performed by extracting a pattern from mobility traces

[91]. SUs are moving according to a Random Waypoint Model (RWPM). The RWPM

4.2. MOBILITY MODEL 50

is very useful in evaluating the performance of mobile wireless networks. It is a random

model for the movement of mobile users where it considers the changes over time in

the nodes’ locations, velocities and accelerations [92]. This model is flexible for a wide

range of wireless networks and can be used in simulating the movement of SUs [93].

Fig. 4.1 illustrates the mobility of a SU following the RWPM. The mobility of this

SU is described as follows:

1. The node starts from an initial location Ci0 = (x0, y0), and a variable H is set

to 1.

2. A destination location CiH = (xH , yH) is chosen, using normal distribution,

within the network area.

3. The movement speed, νH , is chosen uniformly from an interval [υmin, υmax].

After that, the node moves along the line segment between the two locations

toward CiH at speed νH .

4. Node i will stay at waypoint CiH for T ip,H time.

5. Set H = H + 1, then go to step 2.

Figure 4.1: Mobility of a SU following the random Waypoint mobility model

The impact of mobility on the connectivity of the nodes is derived in appendix A.

The probability of connectivity of SU i to the network can be obtained as follows:


Pr(Λi) =M−1∑k=1

p(Λ(i, k)|s(i, k))p(s ≤ rt)∀i 6= k, (i, k) ∈M (4.1)

where Λ(i, j) is the event of having a direct communication link between nodes (i, j),

s is the normalized mutual Euclidian distance, and rt is the maximum transmission

range for a SU.

The connectivity of mobile SUs determines whether they will be successful in

transmitting their data or not. If a mobile SU is no longer connected to the network

then they will lose access to the spectrum and will not be able to gain this access until

they are connected again to the network and successfully contend for the spectrum.


The objective of the proposed algorithm (MSDSA) is to optimize the spectrum re-

sources by allowing mobile SUs to fairly access the available bands while protecting

PUs from any harmful interference. The proposed algorithm is also expected to jointly

allocate the spectrum while taking into consideration SUs’ mobility and its impact

on the connectivity, along with the other issues mentioned in the previous section. A

detailed description of the proposed algorithm is provided in the following sections.

4.3.1 Constraints

DSA algorithms that support mobility can be formulated as a constrained optimiza-

tion problem. The main constraints of the proposed scheme are as follows:

1. Channel allocation: In order to optimize channel allocation, a binary integer

variable xij ∈ 0, 1 is introduced, where a value of one means that channel j


is assigned to SU i, and zero means otherwise. Also, SU pairs are restricted to

access only one channel at any given time slot. This can be enforced by the

following constraint:

Υ∑j=1

xij(t) ≤ 1 ∀ i ∈M, ∀t ∈ ψ (4.2)

where ψ = 1, 2, 3, ..., T is the set of the simulated time slots.

2. Hybrid Underlay/Interweave Scheme: To increase the success rate and fulfill

as many access requests as possible, multiple access to the same channel is

allowed, provided that the interweave scheme is favoured. After allocating all

of the vacant channels, SUs can access the spectrum using an underlay scheme

if needed. This constraint can be defined mathematically as follows:

PAj(t) +M∑i=1

xij(t) ≥ 1 ∀ i ∈M, ∀j ∈ Υ, ∀t ∈ ψ (4.3)

where PAj(t) is the activity of PU j at time slot t.

3. Primary Exclusive Region: The formula to calculate the aggregated interference

introduced to the PUs from all SUs in the system while considering the PER

is given in equation (3.11). We are interested in the aggregated interference

from SUs that will be allocated a specific channel j for transmission, however.

SUs that will not be allocated this specific channel are assumed to introduce no

interference to it. To enforce the described condition, the following constraint

is added:


M∑i=1


(1

2

(log 10

10

)2

σ2ij

)P iSU

≤ γthr, ∀ S(i, j) ≥ (Ro + ε), ∀j ∈ Υ

(4.4)

where γthr is the threshold of maximum allowed aggregated interference to be

introduced to PUs from SUs.

4. Impact of mobility on the optimization problem:

As mentioned in the previous section, the mobility of the nodes has a major

impact on the topology of the network and on SUs’ connectivity. In order to

accurately assign the limited resources and to prevent wasting the spectrum,

the following condition is added:

Pr(Λ(i, t))xij(t) ≥ γconn thr, ∀i ∈M, j ∈ Υ, t ∈ ψ (4.5)

where Pr(Λ(i, t)) is the connectivity of SU i at time slot t and can be calculated

using (4.1), and γconn thr is the connectivity threshold where only SUs that have

a connectivity probability higher than this threshold can compete to access the

spectrum. If the condition is not fulfilled, then the requests will be rejected and

no channels will be assigned to SUs that do not meet this requirement.

4.3.2 Formulating DSA as Optimization Problem

The optimization problem of DSA for mobile SUs is defined as:

MinimizeM∑i=1

Υ∑j=1

Ωij(t)xij(t) (4.6)


such that:

Υ∑j=1

xij(t) ≤ 1 ∀ i ∈M, t ∈ ψ (4.7)

PAj(t) +M∑i=1

xij(t) ≥ 1 ∀ j ∈ Υ, t ∈ ψ (4.8)

M∑i=1

xij(t)Gjo exp

(1

2

(log 10

10

)2

σ2ij

)P iSU

S(i, j)|S(i, j) ≥ (Ro + ε)−η ≤ γthr, ∀ j ∈ Υ, t ∈ ψ

(4.9)

Pr(Λ(i, t))xij(t) ≥ γconn thr, ∀i ∈M, t ∈ ψ (4.10)

where

Ωij(t) =(√

2− s(i, j, t))EijIo|PER

+ (SAi(t) + 1)−1 ∀ i, j, t(4.11)

xij(t), ∈ 0, 1 ∀ i, j, t (4.12)

ψ = 1, 2, 3, ..., T (4.13)

s(i, j, t) ∈ [0,√

(2)] (4.14)

Where s(i, j, t) is the normalized Euclidian distance between SU i and PU j (that

is assigned channel j) at time slot t, SA(i, t) represents the activity of SU i at time

slot t, and EijIo|S(i, j) is the expected interference caused by SU i to PU j given


the mutual Euclidean distance between them. EijIo|S(i, j) can be calculated as

given in equation (4.15).

EijIo|PER = GjoS(i, j)|S(i, j) ≥ (Ro + ε)−η exp

(1

2

(σij

log 10

10

)2)P iSU

(4.15)

The CRBS generates the cost function Ω by calculating the cost value for each

pair of (SU i, channel j) as defined in (4.11) where the first term (√

2− s(i, j, t)) en-

sures maximization of the Euclidian distance between SUs and PUs that are assigned

the same channel. This is very helpful in cases when the algorithm is not updating

the environment parameters very frequently. In such a scenario, the interference from

SUs will be as minimal as possible until the algorithm computes the optimal channel

assignment again. The second term, EijIo|S(i, j), is an indicator of the level of

interference that SU i will bring to PU j; when the interference is high, the cost of

assigning this channel to that specific SU will increase. The last term represents the

activity of the SUs; if SU i is very active, it will get a higher priority to access the

spectrum.

Because of the discrete binary variable, the problem cannot be formulated as a

convex programming problem. In other words, there is no mathematical algorithm to

solve this problem even though all of the other constraints are convex. It is known that

solving Mixed Integer Non-Linear Programming (MINLP) problems is much more

difficult than solving convex optimization problems. This is due to their intrinsically

combinatorial nature so that different variables and node choices result in different

search trees with high uncertainty and extremely unpredictable computational time.


A Branch-and-Bound (BB) approach is usually used to solve such MINLP problems

[94]. BB adopts the idea of divide and conquer where the original problem is divided

into linear subproblems and then all of them will be solved. Unfortunately, the

number of subproblems can grow exponentially and this will consume considerable

time and computational resources [95].

Another alternative is to use the primal-dual interior-point method. This method

transforms the non-convex problem into a Lagrange dual problem and instead of solv-

ing the original problem, the dual problem can be solved by finding the Lagrangian

and solving for the Lagrangian multipliers by invoking the primal-dual concept. Start-

ing with forming the Lagrangian for the optimization problem, it can be written as

given in equation (4.16).

G(%,$, ξ,ϕ) =

M∑i=1

Υ∑j=1

Ωijx∗ij +

M∑i=1

Υ∑j=1

%i(x∗ij(t)− 1)−

M∑i=1

Υ∑j=1

$j

(x∗ij(t) + PAj(t)− 1

)+

M∑i=1

Υ∑j=1

ξj

(GoS(i, j)−η exp

(1

2

(log 10

10

)2

σ2ij

)P iSUx

∗ij(t)− γthr

)

−M∑i=1

Υ∑j=1

ϕj(Λ(i, t)x∗ij(t)− γconn thr

)(4.16)

where %,$, ξ,ϕ, ς are the Lagrange multipliers. The Karush-Kuhn-Tucker (KKT)

conditions should be satisfied [96]. The KKT optimality conditions are defined in

equations (4.17) to (4.22).

x∗ij(t) ≥ 0, %i ≥ 0, $j ≥ 0, ξj ≥ 0, ϕj ≥ 0, ∀i, j, t (4.17)

4.4. DESCRIPTION OF MSDSA ALGORITHM 57

%i

Υ∑j=1

(x∗ij(t)− 1) = 0 (4.18)

$j

M∑i=1

(x∗ij(t) + PAj(t)− 1) = 0 (4.19)

ξj

M∑i=1

(GoS(i, j)|S(i, j) ≥ (Ro + ε)−η exp

(1

2

(log 10

10

)2

σ2ij

)P iSUx

∗ij(t)− γthr

)= 0

(4.20)

ϕj

Υ∑j=1

(Λ(i, t)x∗ij(t)− γconn thr

)= 0 (4.21)

∂G

∂x∗ij= 0 (4.22)

The optimal allocation can be found by solving the minimization problem of the

Lagrangian using Newton’s method to sequence of equality constrained problems.

The more Lagrangian multipliers the problem has, however, the more time and

computational resources the problem will be consuming [96, Chapter 11]. In order to

provide a low complexity algorithm that can be implemented practically, I propose

the MSDSA suboptimal algorithm. Suboptimal MSDSA will eliminate the number

of required Lagrangian multipliers by carrying out the steps explained next.

4.4 Description of MSDSA Algorithm

The Pseudo code for the algorithm is given in Algorithm 1. The MSDSA algorithm

starts with ensuring that SUs have the capability of transmitting their data to the

intended receiving station, i.e. being connected to the network. As such, the MSDSA


algorithm will start by checking the connectivity of each SU prior to performing the

optimization of the resources. Let M ′ = SU1, SU2, ..., SUm′ represent the set of the

connected SUs. Next, the cost matrix Ω(t) is modified accordingly, by omitting the

data relevant to the disconnected nodes (in order to prevent considering the discon-

nected SUs as contending users). This will introduce two advantages to the algorithm:

First, the size of the problem is reduced, and second the constraint defined in (4.10)

and the accompanying Lagrangian multipliers (ϕ) will be removed.

The next step is to give priority to the interweave allocation scheme by enforcing

the assignment of the vacant channels before invoking the underlay scheme, without

needing to consider condition (4.8) in the optimization problem. This is done by

modifying the definition of Ω′ij to include the PU activity as follows:

Ω′

ij =0.6(PAj(t)) + (√

2− s(i, j, t))EijIo|PER

+ (SAi(t) + 1)−1 ∀i ∈M ′, ∀ j ∈ Υ, ∀t ∈ ψ

(4.23)

where j represents all of the available channels and i iterates over all of the con-

nected SUs. Another advantage for defining Ω this way is that the cost will be

dependent on the mutual Euclidian distance. Hence, the channels will be assigned

to the SUs that are far away from the PER. Based on this observation, condition

(4.20) and its Lagrangian multipliers will be omitted from the Lagrangian problem.

To guarantee that the omission of this condition will not impact the PUs’ QoS, the

CRBS will double check the total interferences introduced to the PUs after perform-

ing the DSA. If the level of the interference is larger than the threshold, then the SUs

will not be allowed to transmit simultaneously on that timeslot. Based on the above


simplification, the modified Lagrangian is given in (4.24).

G(%,$) =M∑i=1

Υ∑j=1

Ωijx∗ij +

M∑i=1

Υ∑j=1

%i(log(−x∗ij(t) + 1))

−M∑i=1

Υ∑j=1

$j

(x∗ij(t) + PAj(t)− 1

) (4.24)

This will leave only two sets of Lagrangian multipliers: % and $ . Moreover,

since the optimization problem has to be solved in real time, a new parameter is

introduced to reflect the frequency of updating the optimization parameters. Let N

be a real positive integer and call it the round length. N defines the time spacing

between the consecutive runs of the allocation algorithm. For instance, if N = 100

then this means that the algorithm will solve for the optimal allocation once every 100

timeslots. To keep the focus of the algorithm on protecting the PUs’ communications,

however, SUs that were successful in getting access to the spectrum will be obliged

to frequently check their compliance with the PER-transmission free region and the

maximum allowed interference level. At this stage, the primal-dual interior-point

method can be used to solve the simplified optimization problem.

The computational complexity of the MSDSA algorithm is lower than or equal to

O((2M ′Υ/N)3)+O(log N). On the other hand, the complexity of solving the original

optimization problem is (O(5MΥ)3 ) where M could potentially be much higher than

M ′. As previously stated, the complexity of the proposed solution is lower than the

optimal solution.


Algorithm 1 Suboptimal MSDSA Algorithm

1: procedure MSDSA F,∀i ∈M, j ∈ Υ2: F = N, γthr, γconn thr, PAj, SAi,Ω, EijIo|PER, S3: Define ζ := timeslots counter4: Initialize ζ := 05: Optimization cycle:6: Update PAj, SAi,Ω, EijIo|PER, S(i, j),∀i ∈M, j ∈ Υ7: Calculate Pr(Λi) using (4.1), ∀i ∈M8: Define M ′ := Set of the active & connected SUs9: Initialize M ′ := 10: for i := 1 to M do Eliminate disconnected SUs11: begin12: if Pr(Λi) > γconn thr then13: M ′ = M ′, SUi14: end for loop15: Calculate Ω′ ∀i ∈M ′, j ∈ Υ16: Solve (4.24) and find x∗ij17: for L := 1 to M’ do Check co-channel interference18: begin19: if

∑M ′

K=1EjKLI0 > γintra, K 6= L, ∀j ∈ Υ then

20: xLj = 021: end for loop22: Declare MSDSA results and inform SUs23: while ζ ≤ ζ + N do24: begin25: for T := 2 to N do Perform local monitoring26: for all (i, j) pairs:27: begin28: if EijIo|PER < γthr & S(i, j) > Ro + ε &

∑MK=1E

jKLI0 < γintra then

29: xij(ζ)← xij(ζ − 1)30: else xij(ζ)← 031: end for loop32: end while loop33: Wait until N timeslots pass then goto 5.



The performance of the algorithm is evaluated by the outage percentage (Poutage) of

the SU nodes. Poutage is the percentage of the time slots when any SU that requests

a channel fails to be assigned one. Success rate is defined as the percentage of time

slots that the SUs that have data to transmit succeed in getting a channel assigned

for them. Success rate is equivalent to 100 − Poutage, hence we discuss the perfor-

mance using the Poutage parameter only. SUs are assumed to be working in saturated

communication mode. As such, all SUs will have data to transmit all the time. The

communication traffic parameter, (λPU), of the PUs is set to 0.5.

Fig. 4.2 represents the outage percentage when the speed of the nodes is varying,

the round length N = 50, and number of channels = 10. It can be seen that as

the speed of mobile nodes increases, the outage percentage increases. Also, the MS-

DSA algorithm outperforms both underlay-only and random (CSMA/CA) allocation

algorithms.

Fig. 4.3 shows the impact of the number of SUs on Poutage when the speed of

the nodes is fixed at 3.6 km/hr or 100 km/hr. N is set to 50 and the number of

available channels is set to 10. When the SUs’ number is much larger than the

number of available channels in the system, the speed will have a small impact on the

performance of the MSDSA algorithm since the outage percentage is already high,

as illustrated in Fig. 4.3. Poutage can reach 70% at a speed of 3.6 km/hr and can

increase to 85% at a speed of 100 km/hr. This is due to the fact that the system is

trying to accommodate many nodes in this scenario, which are working in saturated

communication mode. This will lead to over-assigning the channels to many SUs

which will result in higher collision rates and more scrambled signals.


Figure 4.2: Outage percentage, Pout%, vs. speed of SUs for different number of SUswhen the number of available channels = 10

On the other hand, when the number of SUs is comparable to the number of

available channels (e.g. number of SUs≤ 3 x number of channels), then the algorithm

will perform much better and the success rate can reach more than 60% in fulfilling

the access requests from SUs that have data to transmit all the time (saturated

communication mode).

The impact of N on the performance of the algorithm for different numbers of SUs

and different speeds is illustrated in Figs. 4.4 and 4.5, respectively. It can be seen in

Fig. 4.4 that as N grows, the performance degrades faster when the number of SUs


Figure 4.3: Outage percentage, Pout%, vs. number of SUs for different speeds whenthe number of available channels = 10

is large. This is due to the fact that the duration between two consecutive rounds of

solving the optimization problem is long. While this may save a lot of computational

cost, the outage percentage will increase considerably especially when there are many

SUs contending to access the spectrum.

As illustrated in Fig. 4.5, the performance will degrade by 7.8%, at a speed of

3.6 km/hr, when N is 500 timeslots as compared to the case when N is set to 10.

This degradation in performance is acceptable since the computational cost will be

50 times less. When the speed is 100 km/hr, however, the performance will degrade


Figure 4.4: Outage percentage, Pout%, vs. N for different number of SUs whenspeed=15km/hr and the number of available channels = 10

by 52% and the outage percentage will jump from 26% to 78%. In other words, the

cost of increasing N from 10 to 500 timeslots is very high when the nodes are moving

at high speed since the geographical locations, the network topology, and the inter-

ference levels are changing quickly in this case.

In the following, the algorithm is improved by letting N be dependent on the

speed. Specifically, N is calculated as follows: N = min(⌊

500Speed

⌋, 500

). As such,

when nodes are moving on low speed, the algorithm will be run less often (N will


Figure 4.5: Outage percentage, Pout%, vs. N for different speeds when number ofSUs= 10 and the number of available channels = 10

have a high value, e.g. 500 at a speed of 1 km/hr) as compared to the case of moving

on high speed (e.g. 5 at a speed of 100 km/hr). Fig. 4.6, represents the outage

percentage versus speed when the number of channels is 10 and the number of SUs

is set to 10 or 30. Also, Fig. 4.7 shows the outage percentage versus speed when the

number of SUs is 50 and for a number of channels equal to 10 or 30. As seen in both

figures, the impact of mobility on the performance of the algorithm is reduced and

we can say that the performance is stable with minimal effect of the SUs’ mobility

speed.


Figure 4.6: Outage percentage, Pout%, vs. speed for different number of SUs whenthe number of channels = 10 and N is dependent on the speed of SUs

Moreover, Fig. 4.8 shows the performance when the SUs’ number is varying from

10 to 100, when N is dependent on the speed, the number of channels is equal to 10

and at speeds of either 3.6 km/hr or 100 km/hr. As the number of SUs grows, the

performance degrades since the number of channels (and hence access opportunities)

is fixed, while the demand is increasing. In order to comment on the effect of the

number of available channels on the performance of the algorithm, Fig. 4.9 shows

the outage percentage when the number of channels is varying from 10 to 100 when


Figure 4.7: Outage percentage, Pout%, vs. speed of SUs for different number of avail-able channels when the number of SUs = 30 and N is dependent on thespeed of SUs

N is dependent on the speed,. The number of SUs is set to 30 and mobility speed

is set to either 3.6 km/hr or 100 km/hr. As expected, the performance gets better

as the number of channels increases. When the number of channels is more than 50,

however, the performance will not be enhanced. This is due to the fact that most of

the SUs will be spread over different channels and there will be very little room for

improvement when the number of channels is significantly larger than SUs’ number.


Figure 4.8: Outage percentage vs. number of SUs for different speeds when the num-ber of available channels = 10 and N is dependent on the speed


In this chapter, I proposed a suboptimal DSA with a mobility support algorithm.

The algorithm considers a multichannel multiuser scenario. The proposed algorithm

is different from the traditional resource allocation algorithms in that it supports the

mobility of SUs and jointly takes into consideration nodes’ activity, the interference

levels, the connectivity, the PER regions and the correlated shadowing. It also adopts

an interweave/underlay hybrid approach as an access scheme while favouring the


Figure 4.9: Outage percentage, Pout%, vs. the number of available channels for dif-ferent speeds when number of SUs = 30 and N is dependent on the speed

interweave scheme. The algorithm outperforms other classical DSA protocols while

being suitable to be implemented practically by simplifying the optimization problem

and reducing the computational cost.

70

Chapter 5

Integrating Energy Harvesting and Dynamic

Spectrum Allocation in Cognitive Radio Networks

Harvesting energy from ambient energy sources is an efficient method for extending

the lifetime of energy-constrained networks without the need for external power sup-

ply or periodic battery replacements [97]. Moreover, EH is promising to increase the

Energy Efficiency (EE) of CRNs, which is important due to the limited capacity of

SUs’ batteries. Although EH is an attractive technique, it introduces new challenges

due to the dynamic and discontinuous characteristics of EH opportunities.

In order to enable EH, it is crucial to discover the EH opportunities, which can be

performed using energy detection. Fig. 5.1 shows the components that an EH-enabled

SU should have to perform EH [98]. Namely, these components are:

• A software-defined radio-based wireless transceiver.

• A Radio Frequency (RF) energy harvester.

• A spectrum analyzer which observes and analyzes the activity of spectrum us-

age.

71

• A memory-equipped processing unit which can maintain a database, extract

information and make intelligent decisions about sensing and the available EH

opportunities.

• A decision making unit.

• An energy storage device, which could be a rechargeable battery to store the

harvested energy for future use.

• A power management unit, which dispatches the energy from the RF energy

harvester.

Figure 5.1: Components of an EH-enabled SU

SUs that have only one transceiver can either harvest energy or access the spec-

trum at any given time. This means that SUs that harvest energy will miss opportuni-

ties to access the spectrum. EH should not severely impact the performance of DSA,

however. This can be ensured by employing two strategies: first, by implementing

EH within the context of the DSA algorithm and second, by introducing some local

and global parameters to be used to control the operating mode preferences of the

72

individual SUs and the overall CRN. The general platform for integrating EH and

DSA is provided in Fig. 5.2.

Figure 5.2: General Platform for integrating EH with DSA

In previous studies, reviewed in Chapter 2, PUs’ activity is the main factor in

making decisions regarding EH. The average level of energy in the CRN and the SUs’

operating-mode preferences had no control over the EH process. This approach is

not the best since it may severely impact the performance of the DSA and the QoS

of SUs that have delay-sensitive data.

Moreover, all of the previous efforts assumed very simple networks that consist

of two SUs at most. In practical scenarios, however, CRNs can have multiple SUs

that contend to access the spectrum and/or harvest energy. Furthermore, the work

that has been done in this field focused on the theoretical aspects of the EH and its

5.1. RANGE OF HARVESTING 73

impact on the achieved throughput for simple systems. The practical implementation

of this technology in CRN and how to integrate it with the DSA algorithms was

not investigated. Motivated by this and the recent advances in designing efficient

circuits and devices for RF energy harvesting that are suitable for low-power wireless

applications [98], I propose a flexible and effective operating framework to enable EH

in CRNs.

Specifically, I develop a novel mode-selection strategy that allows every SU to

have some control over its preferred mode of operation: either transmitting data,

harvesting energy, or staying silent. The goal is to improve both energy efficiency

and spectral efficiency while being able to control the impact of EH on the DSA

algorithm.

The remainder of this chapter is organized as follows: Range of Harvesting (RoH)

concept is described in Section 5.1. Discovering the opportunities for EH is discussed

in section 5.2. Section 5.3 describes the proposed EH-enabling platform for CRNs.

The results and interpretations are given in Section 5.4 and concluding remarks are

provided in Section 5.5.

5.1 Range of Harvesting

The CRBS executes optimizing the DSA and EH. It also supervises the level of energy

in the overall CRN and has the power to force SUs to harvest energy in case the level

of energy is lower than a specific threshold, as will be discussed in Section 5.3.

The considered system is similar to the one described in Chapter 3. In addition,

SUs that are within a distance of RoH of an active PU will be identified as SUs that

potentially can harvest one unit of energy every time slot, as shown in Fig. 5.3. Given

5.2. ENERGY DETECTION 74

the current advancement is EH circuits, RoH can be in the range of 10 meters from

the active nodes. The energy detector is also used to make decisions concerning the

EH cycle, as explained in the following section.

Figure 5.3: RoH Concept

5.2 Energy Detection

Energy detection is a widely used approach to detect the strength of a signal [99],

and based on this, a decision on the availability of EH opportunities can be made.

The block diagram of an Energy Detector (ED) is given in Figure 5.4.

Figure 5.4: Block diagram of an energy detector

5.2. ENERGY DETECTION 75

The Band-Pass Filter (BPS) removes the out-of-band signals and keeps the signal

in the band under consideration. An Analog to Digital Converter (ADC) digitizes

the filtered signal Rs(t) at the Nyquist rate (fs = 2B samples/sec) where fs is the

sampling frequency. This results in a set of Sset = 2BT samples which are then fed

to a squaring device followed by an accumulator to get the energy content,Ec, in Sset.

After that, Ec is compared to a threshold, EDthr , to decide whether there exists an

EH opportunity or not.

The performance of the ED is linked to its specificity (measured by the false

alarm probability, Pfa, which represents the probability that the detection algorithm

falsely decides that a PU is present in the considered frequency band when it is not)

and sensitivity (measured by the probability of detection, Pd, which represents the

probability of correctly detecting the PU signal in the scanned frequency band) [100].

Mathematically speaking, Pfa and Pd can be defined as:

Pfa = Pr(signal is detected|H0) = Pr(Ec > EDthr |H0)

=

∫ ∞EDthr

f(Ec|H0)du(5.1)

Pd = Pr(signal is detected|H1) = Pr(Ec > EDthr |H1)

=

∫ ∞EDthr

f(Ec|H1)du(5.2)

where f(Ec|Hl) represents the probability Density Function (PDF) of Ec when

the investigated PU is active (H1) or inactive (H0). In order to maximize the amount

of harvested energy units, Pd should be maximized while minimizing Pfa. There is a

trade-off, however, between Pd and Pfa where choosing higher Pd will result in having

high Pfa and vice versa. Moreover, the EDthr has full control over the performance

5.3. FRAMEWORK OF ENABLING EH IN CRNS 76

of the ED and it is crucial to have a suitable EDthr [101].

Practically speaking, if the goal of the EH algorithm is to have a low probability

of mis-detecting EH opportunities, Pfa is set to a reasonably small value and EDthr

is chosen such that Pd is maximized. This requirement is met by evaluating EDthr

based on (5.1). In this case, Pfa is given by [102]:

Pfa =Γ(Ec2, EDthr

2σ2ij

)Γ(Ec)

∆=FEc

2

(EDthr

2σ2ij

)(5.3)

where Γ(a) is the Gamma function, Γ(a, b)∆=∫∞bxa−1e−xdx is the incomplete

Gamma function and σ2ij is the variance of the correlated shadow fading map that

SU i is experiencing at channel j. Next, EDthr can be found based on (5.3) as follows:

EDthr = 2σ2ijF−1Ec2

(Pfa) (5.4)

To this end, the formulation of detecting the available EH opportunities was pro-

vided. The next step is to study the implication of these discovered opportunities on

the performance of both EH and DSA. The proposed approach is described in the

next section.

5.3 Framework of Enabling EH in CRNs

Due to the specific nature of EH (such as the dynamic availability of the harvesting

opportunities and the communication requirements of the SU units), traditional DSA

access protocols need to be modified in order to integrate EH along with efficient

spectrum allocation. In particular: QoS, the spectrum efficiency, and the available

EH opportunities need to be jointly optimized.


In order to give CRN the control over deciding whether to choose EH or DSA,

depending on the level of energy in the network and on the type of data to be transmit-

ted (whether it is a delay-sensitive data or not), two new parameters are introduced:

κi and z(t). κi is a controlling parameter that is set individually by each SU i in order

to have the flexibility to decide which mode of operation they prefer while z(t) is a

global parameter set by the CRBS to take the energy level in the overall CRN into

consideration while making the final spectrum allocation and EH decisions.

Choosing a suitable value for z(t) depends on the type of the CRN and the char-

acteristics of the supported applications. For example, if the supported applications

need to send delay-sensitive data (e.g. the readings of smart meters in Smart Grids

(SGs)), EH will not be a priority and the CRBS will set z(t) to have a high value

(e.g. z(t) = 0.9) which means that the overall aim of the network is to focus on

accessing the spectrum and transmitting as much data as possible. On the other

hand, if the type of supported applications can tolerate delay and the CRN prefers

to have their batteries to last longer (i.e. have a longer lifetime) rather than having

a high transmission rate, then EH mode will be favoured globally in the network by

setting z(t) to have a small value. A real-life scenario for this case is the CRNs that

are used for forest monitoring where sensors are deployed in the forest and transmit

information about the surrounding environment until their batteries are depleted.

After that, there will be a need to replace nodes that are not functioning anymore

due their depleted battery. The replacement cost can be avoided by enabling EH such

that nodes’ lifetime is increased.

Also, SUs will have some control over the decision making by selecting values for

κi,∀i ∈ M , that reflect their preference. For example, if SU i prefers to access the


spectrum, it will choose a small value for κi (e.g. κi = 0.1) and if it is interested

more in harvesting energy, whenever possible, it will choose a large value for κi (e.g.

κi = 0.9).

Let us also define a parameter called the level of battery threshold for SU i (LoBithr)

which represents the percentage of the available energy in SU i’s battery when it is

ordered by the CRBS to harvest energy instead of accessing the spectrum. LoBithr

depends on z(t) and κi and can be calculated by employing the following equation:

LoBithr% =

(1− 2z(t)(1− κi)) ∗ 100 z(t) < 0.5

100κi z(t) = 0.5

100(2κi(1− z(t))), z(t) > 0.5

(5.5)

Fig. 5.5 shows the relationship between z(t) and LoBithr for two SUs with different

κi values. SU1 sets κ1 to 0.1 which means that it prefers to transmit data over

harvesting energy and hence it will not be forced to switch to EH mode until its

battery level drops to 55% when z(t) = 0.25 or when the battery level is below 5%

when z(t) = 0.75. Additionally, SU2 sets κ2 to 0.9 which means that it prefers to

harvest energy and hence it will be forced to switch to EH mode once its battery

level drops to 95% when z(t) = 0.25 or when the battery level is below 45% when

z(t) = 0.75.

Based on the aforementioned parameters, the proposed algorithm that jointly op-

timizes the EH and DSA is provided in Algorithm 2. The algorithm starts with col-

lecting information about the activity of PUs (PAj ∀j), the activity of SUs (SAi ∀i),

the mutual distances between the SUs and the PUs (S(i, j) ∀i, j), and RoH. The

decision parameter of the algorithm is xij ∈ 0, 1, EH where 1 means that SU i is


0 0.2 0.4 0.6 0.8 10%

20%

40%

60%

80%

100%

z( t )

LoB

thr

i %

SU1,κ1=0.1

SU2,κ2=0.9

Figure 5.5: CRBS’s decision parameter vs. the energy level in the SUs’ batteries fordifferent values of κi

assigned channel j to transmit data in the next time slot, EH means that SU i will

harvest energy from channel j, and 0 means that SU i will not have any activity on

channel j.

The CRBS chooses a suitable z(t) depending on the type of the supported appli-

cations. Also, a value is set to Pfa and based on this value, EDthr is calculated. After

that, each SU i selects its κi and sends this value along with the sensing information

and its battery level to the CRBS. Next, if the average level of energy in the network

is lower than a threshold, the CRBS will direct SUs that are in the vicinity of an

active PU to harvest energy, provided that the level of energy in the network is lower

than LoBithr. The other active SUs that indicated interest in accessing the spectrum

instead of harvesting energy will be allocated the available channels. The channels

will be allocated by solving an optimization problem that minimizes the interference


Algorithm 2 Enabling-EH in DSA platform

1: procedure EH(S(i, j), PAj, SAi, RoH, ∀i ∈M, j ∈ Υ)2: CRBS chooses z(t)3: Define LoC1:=List of contending SUs to access the spectrum4: Initialize LoC1:=5: Set Pf and calculate EDthr using (5.4)6: for i := 1 to M do Select a suitable κ7: begin8: SU i chooses κi9: SU i senses 1 channel and sends sensing results, LoBi and κi to CRBS10: end For loop11: Define Υ1 := set of active channels12: Define Υ2 := set of inactive channels13: Define (LoB) := average level of Energy in the CRN14: (LoB) =

∑Mi=1 LoBi/M

15: for i := 1 to M do Decide the best operating mode16: begin17: Find LoBi

thr based on (5.5)

18: if S(i, j) < RoH & (LoB) ≤ LoBithr & j ∈ Υ1 then

19: [ Force SU i to switch to ch. j and operate in EH mode20: xij ← EH ]

21: Else If S(i, j) > RoH & (LoB) ≥ LoBithr & SAi 6= 0,∀i

22: LoC1 = LoC1, SUi

23: end For loop24: Assign channels ∈ Υ2 to the SUs ∈ LoC1 by solving interference minimization

optimization problem25: xij ← 1 for successful contending SUs, phase 1 (Interweave)26: if all SUs ∈ LoC1 got a channel then27: goto 32.28: else [29: Define LoC2 := LoC1− SUs granted access in phase 130: Underlay assignment of channels ∈ Υ1 for the SUs ∈ LoC2 by solving interference

minimization optimization problem31: xij ← 1 for successful contending SUs, phase 2 ]32: Declaration of results :33: if SU iwas unsuccessful in accessing spectrum or HE then34: mode of operation: Silent35: Ensure: EijIo < γthr∀ allocated pairs(i, j)36: Declare results and exit

5.4. RESULTS AND INTERPRETATIONS 81

introduced from the SUs to the PUs. A hybrid interweave/underlay access scheme is

adopted and the DSA will be performed in two phases: in the first phase, channels

with non-active PUs will be assigned to SUs (interweave access scheme). The sec-

ond phase involves allocating the channels that have active PUs using an underlay

scheme. SUs that were neither directed to harvest energy nor allowed to access the

spectrum will remain silent in the next time slot. Finally the CRBS decisions will be

broadcasted to the SUs and the EH/DSA cycle will start again.

5.4 Results and Interpretations

SUs are considered to have limited storage capabilities and can store up to 1000 units

of energy. They require one unit of energy to transmit one packet of data. The

activity of the PUs is considered to follow a Bernoulli arrival process with parameter

λPU = 0.5. To study the worst case scenario, the SUs are assumed to be working in

the saturated mode, i.e. λSU = 1, so that all of the SUs will have a packet to transmit

in each time slot. This means that if a SU is harvesting energy in a time slot, a packet

will be prevented from transmission. By assuming the worst case scenario, we will

be able to capture the impact of EH on the performance of accessing the spectrum.

Without loss of generality, z(t) is set to 0.6 and Pfa is set to 5%. SUs are considered

to choose a similar value for κi, i.e., κi = κ ∀ SU i ∈M . RoH is set to 50 meters and

the simulation area is set to 1000 x 1000 m. The levels of SUs’ batteries are assumed

to be at 50% of the maximum storage capacity at the start of the simulations.

Figs. 5.6 and 5.7 show the average energy level in the SUs and the cumulative

dropped packets over time, respectively, for different values of κ. θSU in this simulation

is set to 50 and the number of available channels is set to 10. As these figures illustrate,


opting for EH mode, by setting a high value for κ, may increase the lifetime of the

network but it does not mean a better performance in terms of spectrum utilization

and data transmission. This is due to the fact that when SUs switch to EH mode,

they miss transmission opportunities. For instance, in Fig. 5.6, when κ is equal to

0.9, the CRN will maintain a 55% and 58% level of energy for hybrid and interweave

access schemes, respectively, after 900 time slots of simulation. However, this will

come with the cost of increased dropped packets which will be 58% and 60% after

900 time slots of simulation for hybrid and interweave access schemes, respectively,

as shown in Fig. 5.7. On the other hand, setting κ to 0.3 will substantially improve

the spectrum access performance and the cumulative dropped packets will decrease

to 42% and 39% in the case of hybrid and interweave access schemes, respectively.

Also, as shown in Fig. 5.6, the energy level in the network will be maintained at

15% and the hybrid scheme outperforms the interweave scheme in the first 500 time

slots, in terms of success rate. After that, the energy level in the CRN will be very

low and the CRBS will start forcing SUs to harvest energy or stay silent, to comply

with the minimum allowed level of energy in the network, and the system will fail in

maintaining a good spectrum efficiency that is comparable to the performance in the

first 500 time slots. Moreover, when the EH operating mode is not supported, the

energy in the network will be fully depleted after 900 time slots and Fig. 5.7 shows

that the packet drop rate will be 31% and 37% in the case of hybrid and interweave

access schemes, respectively.

In order to study the effect of the number of available channels on the performance

of the proposed algorithm, Figs. 5.8 and 5.9 provide the average energy level in

the SUs and the cumulative dropped packets, respectively, for different numbers of


0 100 200 300 400 500 600 700 800 9000%

10%

20%

30%

40%

50%

60%

Time−slots

Ene

rgy

leve

l %

EH using Hybrid access scheme, κ=0.3

EH using Interweave access scheme, κ=0.3No EHEH using Hybrid access scheme, κ=0.9

EH using Interweave access scheme, κ=0.9

Figure 5.6: The average energy level in the SUs over time for different values of κwhen θSU is 50 and number of available channels is 10 and z(t) = 0.6

channels and different values of κ. These results are taken after 100 time slots of

simulations and with θSU set to 50.

As the number of channels increases, both the opportunities to harvest energy

and to access the spectrum increase substantially. In Fig. 5.8, when κ = 0.9, the

energy level will be 59% for both of the studied access schemes when the number of

available channels is 80. Also, the cumulative dropped packets will be 9% in the same

case and this percentage represents the time slots in which the SUs chose to harvest

energy instead of transmitting data. On the other hand, when SUs prefer to access

the spectrum over harvesting energy (by setting κ to 0.3), the packet drop rate will


0 100 200 300 400 500 600 700 800 9000%

10%

20%

30%

40%

50%

60%

Time−slots

Cum

ulat

ive

Dro

pped

pac

kets

%

No EH, Hybrid scheme

No EH, Interweave scheme





Figure 5.7: The cumulative % of dropped packets over time for different values of κwhen θSU is 50 and number of available channels is 10 and z(t) = 0.6

be as low as 1% and 3.3%, when the number of channels is 80, in the case of hybrid

and interweave access schemes, respectively. The energy level will drop to 42% and

47% in the case of hybrid and interweave access schemes, respectively, as depicted in

Fig. 5.9. The performance of the CRN in the case of κ = 0.3 and the case of no EH is

the same since the system has many available spectrum access opportunities and all

of the focus will be on accessing the spectrum. Hence, no energy will be harvested.

While this might be useful in the short term, since it will increase SUs’ chances to

access the spectrum and increase the number of successfully transmitted packets, the

energy level in the CRN will drop and the network will eventually die if SUs do not

start harvesting energy before the occurrence of this event.


10 20 30 40 50 60 70 8042

44

46

48

50

52

54

56

58

60

Number of Channels

Ene

rgy

Leve

l %

No EH, Hybrid schemeNo EH, Interweave schemeEH using Hybrid access scheme, κ=0.3EH using Interweave access scheme, κ=0.3EH using Hybrid access scheme, κ=0.9EH using Interweave access scheme, κ=0.9

Figure 5.8: The average energy level in the SUs versus the number of channels in thesystem for different values of κ at time slot 100 when number of SUs is50 and z(t) = 0.6

Fig. 5.10 presents the cumulative harvested energy units versus θSU in the CRN

after 200 time slots of simulations when the number of available channels is set to 30.

As shown in Fig. 5.10, when the number of SUs increases, the cumulative har-

vested energy will grow due to the increased opportunities of SUs to be in the vicinity

of an active PU. The performance of both hybrid and interweave-only access schemes

is similar in the case of κ = 0.9 since all of the efforts are directed towards harvesting

energy. When κ = 0.3, however, the hybrid system will not harvest any energy units

since all SUs are in the mode of accessing the spectrum. On the other hand, using an

interweave access scheme will introduce some balance between energy harvesting and


10 20 30 40 50 60 70 801

2

3

4

5

6

7

8

9

10

Number of Channels

Cum

ulat

ive

Dro

pped

pac

kets

%

No EH, Hybrid schemeNo EH, Interweave schemeEH using Hybrid access scheme, κ=0.3EH using Interweave access scheme, κ=0.3EH using Hybrid access scheme, κ=0.9EH using Interweave access scheme, κ=0.9

Figure 5.9: The cumulative % of lost packets versus the number of channels in thesystem for different values of κ at time slot 100 when number of SUs is50 and z(t) = 0.6

spectrum access since the SUs will be directed to harvest energy instead of accessing

the spectrum in underlay mode in cases where there are many active PUs and the

number of vacant channels is small.


Enabling EH for CRNs in the context of DSA has the potential advantage of a pro-

longed lifetime without requiring external power cables or periodic battery replace-

ments. This chapter studied enabling EH for CRNs in the context of DSA, and a

novel algorithm that jointly considers the spectrum efficiency and energy efficiency


Figure 5.10: The Cumulative harvested energy units for different number of SUs attime slot 200 and number of available channels is 30 and z(t) = 0.6

is proposed. This algorithm provides SUs with a degree of freedom in selecting their

operating mode while keeping the CRBS in charge of making the final decisions about

the operating modes of the SUs such that a certain level of energy in the whole net-

work is maintained. The impact of EH on the performance of the DSA algorithm has

been studied for both the hybrid and the interweave access schemes. CRNs that adopt

the proposed algorithm will have the potential advantage of a prolonged lifetime while

being able to dynamically access the underutilized spectrum.

88

Chapter 6

Power Allocation for Cognitive Radio Networks

As discussed in the previous chapter, power is an important resource and to increase

the lifetime of CRNs, EH should be enabled. In order to simplify the problem of

integrating EH in the context of DSA, it was assumed that the transmission power

budget in the CRN is not constrained. However, power allocation among SUs needs

to be optimized especially when the allowed transmission power level is limited.

Moreover, in order to reduce the high complexity of obtaining an optimal solution

for the multi-objective problem of jointly optimizing spectrum allocation and power

allocation in a multichannel multiuser scenario, the problem is split into two stages:

first, allocate the spectrum (based on the different constraints that were discussed

in the previous chapters) and then allocate the power to SUs that were successful in

reserving a channel. This approach is proven to provide a near optimal solution while

avoiding the complexity of solving combinatorial optimization problems which have

complexity that grows exponentially with the input size [103].

In this chapter, two optimal algorithms are proposed to optimize the power allo-

cation among SUs that were successful in accessing the spectrum. The objective is

to maximize the Spectral Efficiency (SE) while respecting the power budget, along


with other constraints. The scenario in which the CRN has multiple SUs that are

interfering with several PUs is addressed. Consequently, the power budget should

be allocated to the SUs subject to different power constraints so that the hybrid in-

terweave/underlay access scheme is adopted, which means that SUs can access the

active and non-active PU bands. Hence, different SUs will have different power and

interference limits depending on PUs’ activity and on which SUs will be allocated to

transmit on the same channel simultaneously. Moreover, since the complexity of the

optimal algorithms can be high, a suboptimal discrete Cap-Limited Heuristic (CLH)

algorithm is proposed. The CLH algorithm considers assigning power to the SUs from

a discrete set of power levels as will be discussed later in this chapter.

The remainder of this chapter is organized into the following sections. Section

6.1 provides the problem formulation, while the proposed suboptimal algorithm is

discussed in 6.2. Simulation results and interpretations are given in section 6.3 and

the conclusions are presented in Section 6.4.


The outcome of the spectrum allocation (xij) is assumed to be available at the CRBS

and will be used as an input to the power allocation algorithms. The maximum

achievable throughput can be evaluated using Shannon’s formula:

Throughput = ∆f log2

(1 +

P iSU

σ2AWGN + Eji

PUI0

)(6.1)

where σAWGN is the variance of the Additive White Gaussian Noise (AWGN), and

EjiPUI0 is the interference introduced by PU j that is occupying channel j to SU i,∀i


transmitting on channel j. EjiPUI0 is defined as follows:

EjiPUI0 =

GjoS(i, j)−η exp

(12

(σij

log 1010

)2)P jPU , PAj = 1

0, PAj = 0(6.2)

After calculating the achievable throughput of each SU, the total achievable through-

put can be defined as the sum of the transmission rates of all active SUs [104]. The

objective is to optimize the power allocation in such a way that maximizes the total

achievable throughput. The algorithm is supposed to protect PUs’ communications

and the total allocated power should be equal to or less than the total available power

budget. Moreover, the allocated power to any SU cannot be higher than the max-

imum allowed transmission power. The optimization problem can be formulated as

follows:

maxP

M∑i=1

Υ∑j=1

xij(t) log

(1 +

Pij

σ2AWGN + Eji

PUI0

)(6.3)

such that:

M∑i=1

EijI0xij(t) ≤ Interference Threshold ∀ j ∈ Υ, t ∈ ψ (6.4)

0 ≤j=Υ∑j=1

Pij(t) ≤ PmaxSU ∀ i ∈M, j ∈ Υ, t ∈ ψ (6.5)

M∑i=1

Υ∑j=1

Pij ≤ PT , ∀ t ∈ ψ (6.6)

where PmaxSU is the maximum allowed transmission power of SUs, and PT is the

total available power budget in the CRN.

6.2. THE PROPOSED POWER ALLOCATION ALGORITHMS 91

6.2 The proposed Power Allocation Algorithms

6.2.1 Optimal Power Allocation

The problem described in section 6.1 can be solved using a Primal-Dual approach. In

addition, two algorithms are proposed to solve this problem: Equally-Treated (ET)

and Extra-Caution-Measure (ECM) algorithms. Both ET and ECM algorithms follow

the same procedure except in defining the interference threshold where ET treats all

SUs equally regardless of PUs’ activity. On the other hand, the ECM algorithm takes

an extra caution measure to protect the primary network by enforcing a more strict

interference threshold for the channels that have active PUs. Interference level for

both of the algorithms is given as follows:

Interference threshold =

γthr ∀ ch. j ∈ Υ, ET algorithm

γthr1+PAj

∀ ch. j ∈ Υ, ECM algorithm(6.7)

where PAj ∈ 0, 1 represents the activity of PU j in channel j. The problem

described in (6.3) - (6.6) can be solved by finding the Lagrangian and solving for the

Lagrangian multipliers by invoking the primal-dual concept. The Lagrangian for the

optimization problem being considered is written as:

G(%,$, ξ,ϕ) =M∑i=1

Υ∑j=1

xij log

(1 +

P ∗ij

EjiPUI0+ σ2

AWGN

)+

Υ∑j=1

ϕjP∗ij

+Υ∑j=1

%j

(γthr −

M∑i=1

EijI0

)+$

(PT −

M∑i=1

Υ∑j=1

P ∗ij

)+

M∑i=1

ξi(Pmax − P ∗ij)

(6.8)

where %,$, ξ,ϕ are the Lagrange multipliers. Similar to the previous problem of


spectrum allocation, the lagrangian of the power allocation is a convex optimization

problem. The KKT conditions are given as follows.

P ∗ij(t) ≥ 0, %j ≥ 0, $ ≥ 0, ξi ≥ 0, ϕj ≥ 0, ∀i, j, t (6.9)

%j

(γthr −

M∑i=1

EijI0

)= 0, ∀j ∈ Υ (6.10)

$

(PT −

M∑i=1

Υ∑j=1

P ∗ij

)= 0 (6.11)

ξi(Pmax − P ∗ij) = 0, ∀i ∈M (6.12)

ϕjP∗ij = 0, ∀j ∈ Υ (6.13)

∂G

∂P ∗ij=0

0 =1

EjiPUI0+ σ2

AWGN + P ∗ij−$ −

M∑i=1

ξi +M∑i=1

ϕi

−Υ∑j=1

%jGjoS(i, j)−η exp

(1

2

(σij

log 10

10

)2) (6.14)

After mathematically manipulating (6.14), we get:

P ∗ij =1

$ + ξi − ϕi +Υ∑j=1

%jœj

− EjiPUI0 − σ2

AWGN(6.15)

where:


œj = GjoS(i, j)−η exp

(1

2

(σij

log 10

10

)2)

(6.16)

Since the allocated power cannot be less than zero, we have:

1

$ + ξi − ϕi +Υ∑j=1

%jœj

≥ EjiPUI0+ σ2

AWGN(6.17)

In case of 1

$+ξi−ϕi+Υ∑j=1

%jœj

> EjiPUI0+σ2

AWGN , we will have ϕi = 0. Consequently:

P ∗ij =1

$ + ξi +Υ∑j=1

%jœj

− EjiPUI0 − σ2

AWGN(6.18)

Similarly, the maximum limit of P ∗ij is Pmax. Hence, when 1


%jœj

>

Pmax + EjiPUI0+ σ2

AWGN , we will have ϕi = 0. Consequently, P ∗ij = Pmax. To sum-

marize, the optimal P ∗ij can be calculated by computing the Lagrangian parameters

as follows:

P ∗ij =

0 1


%jœj

≤ Æ

1

$+ξi+Υ∑j=1

%jœj

− EjiPUI0 − σ2

AWGN Æ < 1

$+ξi+Υ∑j=1

%jœj

≤ Pmax + Æ

Pmax1

$+ξi+Υ∑j=1

%jœj

> Pmax + Æ

(6.19)

where:

Æ = EjiPUI0+ σ2

AWGN(6.20)


or simply:

P ∗ij =

1

$ + ξi +Υ∑j=1

%jœj

−Æ

+

∧ Pmax (6.21)

Where [a]+ means max(0, a) and a ∧ b means min(a, b). Since the problem has three

sets of lagrangian parameters, the complexity of the optimal solution is still high.

Hence, we propose a suboptimal CLH power allocation algorithm in the next subsec-

tion.

6.2.2 Cap-Limited Heuristic (CLH) Algorithm

In the CLH Algorithm, there will be no need to solve for the Lagrangian parameters.

Instead, the main idea of the CLH algorithm is to define discrete levels of transmitted

power TP1, TP2, ..., Pmax, such that each SU can be allocated one of these power

levels depending on different aspects, as explained in Algorithm 3.

The algorithm will take xij, interference thresholds, and other parameters related

to the surrounding environment, as input. Then the possible discrete transmissions

are set by the CRBS and the available power budget is distributed equally among

the SUs that are scheduled to transmit data in the next time slot. This initial power

distribution will be refined for each SU individually depending on the introduced

interference level. If the allocated power is higher than PmaxSU , it will be reduced to

PmaxSU . Next, in the scenario where the introduced interference level is lower than

γthr, the allocated power will be increased to the next level. This process will be

repeated until the introduced interference is almost equal to γthr. On the other hand,

the allocated power for SUs that introduce interference levels higher than γthr will be

reduced until the interference constraint is satisfied.


Algorithm 3 CLH-power allocation algorithm

1: procedure CLH(xij, γthr, PT , σAWGN , σij, EijI0, EjiPUI0)

2: TP ← 10mW3: Set a value to Pmax

SU

4: Define LoP :=Level of transmission Power5: LoP:= TP, 2TP, 3TP, ..., Pmax

SU 6: Find ZA := List of SUs gained access @ TS (TSk+1w + 1)7: ZA := A1

SU , A2SU , A

3SU , ..., A

zaSU

8: Initialize PowiSU =⌊PT ∗100za

⌋∗ TP, ∀ i ∈ ZA

9: for i := 1 to za do limit powSU10: begin11: powiSU = minpowiSU , Pmax

SU 12: end13: Initialize ind := zeros(za, 1), decision := zeros(za, 1)14: for i := 1 to za do Effect of γthr on powSU15: begin16: while EijIo < γthr & powiSU < Pmax

SU & ind(i) < 10 do17: begin18: powiSU ← powiSU + TP19: Update EijIo20: ind(i)← ind(i) + 121: end22: if ind(i) = 0 then23: goto budgetCheck.24: while EijIo > γthr & ind(i) < 10 do25: begin26: powiSU ← powiSU − TP27: Update EijIo28: ind(i)← ind(i) + 129: end30: end31: budgetCheck :32: Define pow sum :=

∑zai=1(powiSU)

33: Define MFactor := PTpow sum

34: if MFactor < 1 then35: begin36: for i := 1 to za do scale down powSU37: powiSU = powiSU ∗MFactor38: end39: Declare results and exit


After that, the algorithm will perform a budget check which ensures that the

budget constraint is fulfilled. If not, the allocated power to all of the SUs will

be down-scaled such that each SU will be allocated a power level that is propor-

tional to its original allocated power level, while satisfying the total power con-

straint. The computational complexity of Algorithm 3 is lower than or equal to

O(ZA log (LoP)) +O(logM). On the other hand, the complexity of solving the orig-

inal optimization problem is (O(3MΥ)3 ). It is clear that the complexity of the

proposed solution is lower than the optimal solution while providing near optimal

allocation results.


The system is considered to have 10 channels each with a bandwidth of 6 MHz. Also,

the CRN is considered to have 20 SUs and PmaxSU is set to 100mW . The packet delivery

ratio is set to 95%. In order to capture the impact of interference introduced from

SUs to PUs on SE, the AWGN variance (σ2AWGN) is set to a very small value (10−7W ).

The proposed algorithms are compared to each other while using one of the following

access schemes: interweave, underlay, or hybrid interweave/underlay.

The three main parameters that have an impact on the outcome of the power

allocation algorithms are: γthr, total power budget, and SUs’ maximum transmission

power. Figs. 6.1 and 6.2 show the total transmission power and the spectral effi-

ciency, respectively, versus γthr for the three algorithms when the maximum allowed

budget is set to 4W . As noted in Fig. 6.1, the total transmitted power in the CLH

algorithm is almost similar to the total transmitted power in the ET algorithm. Also,

as γthr increases, the total transmitted power increases due to the fact that the PUs


can withstand a higher level of interference. The maximum transmitted power does

not exceed the level of 1.2W , however, even though the allowed budget is higher than

this. This is due to the constraint of the maximum allowed transmission power per

SU which is set to 100mW . In this simulated case, twelve SUs are active. The hybrid

access scheme outperforms both the underlay and the interweave access schemes for

all of the optimal and suboptimal algorithms. This comes with the cost of extra

transmitted power, however. One of the tactics the CLH algorithm uses to overcome

the sub-optimality is to direct more power to the interweave access, as shown in

Fig. 6.1, since there will be no interference from the PUs and therefore the spec-

tral efficiency will be higher.

Also, the performance in terms of spectral efficiency is almost the same for the

CLH and ET algorithms, as shown in Fig. 6.2. Moreover, the achieved spectral

efficiency for these two algorithms is a bit higher than the achieved spectral efficiency

in case of the ECM algorithm when γthr is less than 1.5mW and the performance

of the three algorithms is the same when γthr is larger than 1.5mW . Hence, we can

conclude that the CLH algorithm is a promising solution for power allocation that

can achieve a near optimal efficiency level with less time and computational-costs.

Figs. 6.3, 6.4, and 6.5 show the assigned power to each channel (power profile)

when the available power budget is varying for CLH, ET, and ECM algorithms re-

spectively. In the time slot under investigation, γthr is chosen to be 0.4mW and

channels 3, 4, 6, 7, 10 happen to have active PUs. As illustrated, both the CLH and

ECM algorithms tend to assign lower power to SUs that are using an underlay access

scheme (e.g. SUs that are assigned one of the following channels 3, 4, 6, 7, 10). The


0.5 1 1.5 2 2.5 3 3.5 4

x 10−3

0

0.2

0.4

0.6

0.8

1

1.2

γthr

(W)

Tot

al T

rans

mitt

ed P

ower

(W

)

CLH−Hybrid SchemeCLH−Underlay SchemeCLH−Interweave Scheme ET−Hybrid SchemeET−Underlay SchemeET−Interweave SchemeECM−Hybrid SchemeECM−Underlay SchemeECM−Interweave Scheme

Figure 6.1: Total transmitted power versus γthr when the total available power budgetis 4W and σ2

AWGN = 10−7

CLH algorithm succeeds, however, in assigning higher power to some SUs that use

an interweave scheme to access the spectrum (e.g. SUs accessing channels 5 and 8).

Both the power budget and γthr are acting as the decision constraints for the alloca-

tion of power in each channel except for channel 8 in the CLH and ECM algorithms

(Figs. 6.3 and 6.4), where the limit on the SUs’ maximum transmission power is the

decision maker.

To study the impact of γthr on the power profile, γthr was set to 1.5mW in Figs.

6.12, 6.13, and 6.8 for the CLH, ET, and ECM algorithms, respectively. Comparing


0.5 1 1.5 2 2.5 3 3.5 4

x 10−3

0

2

4

6

8

10

12

14

γthr

(W)

Spe

ctra

l Effi

cien

cy (

bit/s

/Hz)

CLH−Hybrid SchemeCLH−Underlay SchemeCLH−Interweave Scheme ET−Hybrid SchemeET−Underlay SchemeET−Interweave SchemeECM−Hybrid SchemeECM−Underlay SchemeECM−Interweave Scheme

5 10 15

x 10−4

12

12.5

13

13.5

4 6 8 10

x 10−4

1.1

1.2

1.3

1.4

1.5

1.6

Figure 6.2: The achieved spectral efficiency versus γthr when the total available powerbudget is 4W and σ2

AWGN = 10−7

the power profiles of the three algorithms with each other, it can be noted that they

behave similarly when γthr is high. Also, under this scenario, SUs that are assigned to

a channel with good quality (e.g. channel 8) will be allocated less power as compared

to the scenario where γthr is set to 0.4mW . This is due to the fact that γthr is high

and hence some SUs will be able to transmit higher power (as compared to the case

of γthr = 0.4mW ) even if they produce some interference to the PUs. The power


1 2 3 4 5 6 7 8 9 100

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

Channel Number

Ass

igne

d P

ower

(W

)

total Power Budget = 0.1Wtotal Power Budget = 0.3Wtotal Power Budget = 0.5Wtotal Power Budget = 0.6Wtotal Power Budget = 0.7Wtotal Power Budget = 0.9Wtotal Power Budget = 1.1W

Figure 6.3: The assigned power for each channel in the case of the CLH algorithmwhen γthr is set to 0.4mW

deducted from the SUs with good channels will be assigned to the other SUs with

bad channels and this will increase the level of fairness among SUs. In addition,

the constraints of total power budget and maximum transmission power will be the

dominant factors in making the decisions about power allocation.

Figs. 6.9 and 6.10 illustrate the total transmitted power and the spectral efficiency,

respectively, versus the total available power budget when γthr is set to 0.4mW . As


1 2 3 4 5 6 7 8 9 100

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

Channel Number

Ass

igne

d P

ower

(W

)


Figure 6.4: The assigned power for each channel in the case of the ET algorithm whenγthr is set to 0.4mW

depicted, when the available power budget is higher than 0.8W , the total transmitted

power does not increase since SUs cannot cause high interference levels to the PUs

in order to fulfil the tight interference condition and hence γthr will be the major

decision maker regarding the power allocation. In terms of spectral efficiency, the

CLH algorithm will slightly outperform the other two algorithms since it allocates

higher power to SUs accessing the spectrum using an interweave scheme.

As γthr is increased, the transmitted power will increase linearly with the increase

of the available budget up to 1.2W where the maximum transmission power will be


1 2 3 4 5 6 7 8 9 100

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

Channel Number

Ass

igne

d P

ower

(W

)


Figure 6.5: The assigned power for each channel in the case of the ECM algorithmwhen γthr is set to 0.4mW

reached and no higher power can be allocated to the SUs.

To summarize, when the acceptable level of interference (γthr) is high, the domi-

nant parameter that affects the power allocation will be the available power budget.

On the other hand, assuming that the CRN has high power budget, then the power

allocation will depend only on the maximum allowed transmission power and on γthr.

If the constraints on the power budget and the interference levels are loose, then the

only parameter that will play a role in making decisions about power allocation will

be the maximum allowed transmission power.


1 2 3 4 5 6 7 8 9 100

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

Channel Number

Ass

igne

d P

ower

(W

)


Figure 6.6: The assigned power for each channel in the case of the CLH algorithmwhen γthr is set to 1.5mW

Finally, to study the impact of AWGN on the SE, Figs. 6.11, 6.12, and 6.13

illustrate the achieved SE for the CLH, ET, and ECM algorithms, respectively, versus

γthr for different values of σAWGN . As depicted, when σAWGN is high, the achieved

SE is low as compared to the cases when σAWGN is low. This is due to the fact that

when AWGN is high, SUs will not have enough room to add much interference to

the PUs and the interference constraint will be tighter. In addition, higher AWGN

means lower SINR and consequently a lower SE is achieved.


1 2 3 4 5 6 7 8 9 100

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

Channel Number

Ass

igne

d P

ower

(W

)


Figure 6.7: The assigned power for each channel in the case of the ET algorithm whenγthr is set to 1.5mW


In this chapter, different optimization algorithms were proposed to distribute the

available power budget among the active SUs in such a way that the spectral effi-

ciency is maximized. As demonstrated by extensive simulations, the proposed subop-

timal CLH algorithm provides a near optimal performance and does not require high

computational cost as compared to the cost of solving the Lagrangian dual problem.


1 2 3 4 5 6 7 8 9 100

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

Channel Number

Ass

igne

d P

ower

(W

)


Figure 6.8: The assigned power for each channel in the case of the ECM algorithmwhen γthr is set to 1.5mW


0.5 1 1.50

0.2

0.4

0.6

0.8

1

1.2

Power Budget (W)

Tot

al T

rans

mitt

ed P

ower

(W

)

CLH−Hybred SchemeCLH−Underlay SchemeCLH−Interweave SchemeET−Hybred SchemeET−Underlay SchemeET−Interweave SchemeECM−Hybred SchemeECM−Underlay SchemeECM−Interweave Scheme

Figure 6.9: The total transmitted power vs. the available power budget when γthr isset to 0.4mW


Figure 6.10: The spectral efficiency vs. the available power budget when γthr is setto 0.4mW


0.5 1 1.5 2 2.5 3 3.5 4

x 10−3

0

2

4

6

8

10

12

14

γthr

(W)

Spe

ctra

l Effi

cien

cy (

bit/s

/Hz)

CLH−Hybrid Scheme, No=10−4

CLH−Hybrid Scheme, No=10−5

CLH−Hybrid Scheme, No= 10−7

CLH−Underlay Scheme, No=10−4

CLH−Underlay Scheme, No=10−5

CLH−Underlay Scheme, No10−7

CLH−Interweave Scheme, No=10−4



Figure 6.11: The achieved spectral efficiency vs. γthr for the CLH algorithm whenthe total available power budget is 4W and σ2

AWGN is varying


0.5 1 1.5 2 2.5 3 3.5 4

x 10−3

0

2

4

6

8

10

12

14

γthr

(W)

Spe

ctra

l Effi

cien

cy (

bit/s

/Hz)

ET−Hybrid Scheme, No=10−4

ET−Hybrid Scheme, No=10−5

ET−Hybrid Scheme, No= 10−7

ET−Underlay Scheme, No=10−4

ET−Underlay Scheme, No=10−5

ET−Underlay Scheme, No10−7

ET−Interweave Scheme, No=10−4



Figure 6.12: The achieved spectral efficiency vs. γthr for the ET algorithm when thetotal available power budget is 4W and σ2

AWGN is varying


0.5 1 1.5 2 2.5 3 3.5 4

x 10−3

0

2

4

6

8

10

12

14

γthr

(W)

Spe

ctra

l Effi

cien

cy (

bit/s

/Hz)

ECM−Hybrid Scheme, No=10−4

ECM−Hybrid Scheme, No=10−5

ECM−Hybrid Scheme, No= 10−7

ECM−Underlay Scheme, No=10−4

ECM−Underlay Scheme, No=10−5

ECM−Underlay Scheme, No10−7

ECM−Interweave Scheme, No=10−4



Figure 6.13: The achieved spectral efficiency vs. γthr for the ECM algorithm whenthe total available power budget is 4W and σ2

AWGN is varying

111

Chapter 7

Conclusions and Future Work

7.1 Conclusions

CR-MAC protocols are very important for deploying CRNs in real life. Many chal-

lenges need to be addressed during the design phase. In this thesis, various algorithms

have been developed to facilitate CR-MAC functionalities and to provide efficient per-

formance for CRNs. For the different algorithms, the optimal solution of the problem

was investigated and low complexity efficient algorithms were developed. Further-

more, extensive simulations to study the impact of different scenarios and parameters

were conducted. Specifically, the following algorithms were developed.

1. Efficient DSA: Optimizing the spectrum resources while protecting PUs’ com-

munication was the goal of the proposed DSA algorithm and a multiuser mul-

ticarrier system was considered. The proposed algorithm integrated both inter-

weave and underlay spectrum access schemes and it jointly took into account

the geographical locations of the nodes, the correlated shadow fading, the in-

terference between the primary and the secondary networks, the interference

7.1. CONCLUSIONS 112

between SUs that are transmitting on the same channel, and the communi-

cations activity of the users. Moreover, a PAH-DSA algorithm that jointly

takes into consideration all of the aforementioned issues, while requiring low

computational- and time-costs, was developed. The algorithm outperforms tra-

ditional spectrum allocation algorithm.

However, when the number of SUs is much larger than the number of available

channels, the performance of the proposed algorithm degrades. This is due to

the fact that PAH-DSA algorithm puts the protection of PUs’ communications

as the first and foremost priority that comes even before satisfying the needs of

SUs to access the spectrum. A suggested approach to be used in such a scenario

is grouping SUs into different sets where each set can have only a portion of the

active SUs. After that, the algorithm might be used to allocate the available

WSs to one set of nodes only at a given time and then give access to the next

set and so on.

2. DSA algorithm with mobility-support: In chapter 4, a MAC protocol that ad-

dresses the mobility of the nodes during the design phase of the algorithm was

proposed. Different from the traditional resource allocation algorithms, the

proposed algorithm supports SUs’ mobility and jointly takes into consideration

the nodes’ activity, the interference levels, the connectivity, the PER regions

and the correlated shadowing. It also adopts an interweave/underlay hybrid

approach as an access scheme, while favouring the interweave scheme. The al-

gorithm outperforms other classical DSA protocols while being suitable to be

implemented practically by simplifying the optimization problem and reducing

the computational cost.

7.1. CONCLUSIONS 113

Nonetheless, when the speed is high, the computational cost will be high as well

since the network is very dynamic and the optimization round N should have

small value in order to update the optimization parameters more frequently.

3. Integrating EH in the context of DSA: Chapter 5 addressed enabling EH for

CRNs in the context of DSA. A novel algorithm that jointly considers the

spectrum efficiency and energy efficiency was proposed. This algorithm provides

SUs with a degree of freedom in selecting their operating mode while keeping

the CRBS in charge of making the final decisions about the operating modes

such that a certain level of energy in the whole network is maintained. The

impact of EH on the performance of the DSA algorithm was studied for both

the hybrid and the interweave access schemes.

CRNs that adopt the proposed algorithm will have the potential advantages of

a prolonged lifetime while being able to dynamically access the underutilized

spectrum. Such functionality is very helpful in CRNs that do not have access to

any external source of power other than their batteries, such as networks used

for forests monitoring.

4. Allocate the available power budgets fairly among the active SUs: Different

optimization algorithms to distribute the available power budget among the ac-

tive SUs, in such a way that maximizes the spectral efficiency, were proposed

in Chapter 6. As demonstrated by extensive simulations, the proposed subopti-

mal CLH algorithm provides a near optimal performance and does not require

high computational cost as compared to the cost of solving the Lagrangian dual

problem. By using the proposed algorithm, the SUs will be able to balance

their performance in term of SE and EE.

7.2. FUTURE WORK 114

7.2 Future Work

The research conducted in this thesis has expanded the horizons for a large number

of challenging issues to be addressed. For instance, the following issues are still open

for research:

7.2.1 Impact of imperfect sensing on CR-MAC protocols

In this work, perfect spectrum sensing with zero probability of false alarm or mis-

detection was assumed. The sensing performance might be imperfect, however. Con-

sequently, the impact of errors in the physical layer functionalities on the performance

of DSA, EH, and power allocation algorithms should be investigated.

In addition, using Multi-Input Multi-Output (MIMO) systems opens a new hori-

zon for solving imperfect channel sensing issue. Nonetheless, this requires using multi-

array antennas which might increase the level of interference introduced to the PUs

and consequently have a negative impact on the performance of the developed algo-

rithms.

7.2.2 Mobility Modeling

A RWPM model was considered to characterise SUs’ mobility, allowing SUs to move

in two dimensions. Even though this is a satisfactory modeling in theory, the practical

aspects of mobility should be considered. For instance, real road maps can be used as

the paths for SUs’ mobility. The performance of the proposed algorithms may vary

due to the change in mobility patterns.


7.2.3 Spectrum Maintenance

SUs were assumed to evacuate the channels and wait for the next round of spec-

trum optimization whenever the interference conditions are not satisfied. However,

considering switching from one channel to another within the same time slot (or

optimization round) is an interesting topic that needs to be further investigated.

7.2.4 Harvesting Strategies and EH Capabilities

Splitting the time between EH, sensing, cooperation, and data transmission is another

research challenge. This is of great importance for CRN with cooperative relaying

capabilities since relaying the detected data requires utilizing the harvested energy,

the time for sensing, and the time for transmitting SUs’ own data. In this thesis,

cooperative relaying was not considered and it was assumed that SUs that have an

opportunity to harvest energy from a nearby active PU will be able to collect one

unit of energy that is enough to transmit one packet of their own data.

Also, the rechargeable battery was assumed to remain very efficient in storing and

discharging energy over time. Even though the EH circuit designs are witnessing a

good advancement in harvesting high amounts of energy, further detailed modeling of

the actual capabilities of EH circuits and batteries should be taken into consideration.

This may have some implications on the performance of EH algorithms and on the

actual lifetime of the CRNs.

7.2.5 Multi-cell Layout with Relaying and Cooperation

Considering the scenario of multi-cell CRN will allow spatial frequency reuse especially

when the cells size is small. On the other hand, co-channel interference might be very


high if the cells’ size is very small. In addition, a management structure between the

different CRBSs is needed along with efficient handoff techniques to enable mobile

SUs from moving from one cell to another. Also, enabling multihopping will allow

communications between SUs that are not directly connected. Cooperative relaying

techniques can be used for this purpose. However, managing relaying and cooperation

among SUs in the context of multi-cell setting needs further investigation and analysis.

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Appendices

134

Appendix A

Derivation of mobile SUs’ connectivity Probability

The Cartesian coordinates of the waypoint that a node i chooses in period a of

mobility can be represented by a random variable Cia. Based on this, the trace of

mobility can be characterized by the random process that selects a random waypoint

Cia for each period of mobility a as follows:

Ciai∈M = Ci

0, Ci1, C

i2, ... (A.1)

Each node moves independently from the other nodes. Moreover, the waypoints are

Independent and Identically Distributed (IID). When a node i, located at point Cia−1,

follows the RWPM, it will choose speed νia at time a−1, to move from waypoint Cia−1

to waypoint Cia, and a pause time T ip,a to stay at waypoint Ci

a, as shown in Fig. 4.1.

The complete movement process of this node will be:

(Ca, νa, Tp,a)ia∈A = (C1, ν1, Tp,1)i, (C2, ν2, Tp,2)

i... (A.2)

where A is the set of the mobility time periods. Before being able to find the im-

pact of mobility on the connectivity of the nodes, we first need to find the Probability

135

Density Function (pdf) of the Euclidean distance between mobile nodes, fs(s). It is

given by [105]:

(A.3)

fs(s) =s

9π

((6q2 + (36s2 − 12)q − 36s2 + 24)π

+ (−12q2 + (−72s2 + 24)q + 72s2 − 48) arcsins

2

+ ((−s5 + 7s3 − 15s)q − s5 + 16s3 + 12s)√

4− s2

)where

q =ETp

ETp+ ET, s = S/ro (A.4)

where s is the normalized mutual Euclidian distance, ETp = (Pp/(1−Pp))EL is

the expected pause time between two consecutive waypoints, Pp is the pause prob-

ability, and ET is the expected movement time from one waypoint to the next

destination, as defined below:

ET =ln(νmax/νmin)

νmax − νminEL (A.5)

where EL is the expected value of transition length and is equal to:

EL =1

15

[2a+ a

√2]

+1

3

[a ln(√

2 + 1)]

(A.6)

In order to find the probability of connectivity between a pair of nodes i and j,

let Λ(i, j) be the event of having a direct communication link between them. The

136

conditional probability of having a link given the Euclidian distance is defined as [85]:

Pr(Λ(i, j)|S(i, j)) = P (β(i, j) ≤ βthr|S(i, j))

= Q

(10η

σijlog10

S (i, j)

r0

dB

) (A.7)

where β(i, j) is the signal attenuation between the nodes and r0 is the maximum

transmission range.

Let rt be the maximum transmission range for a SU. In order to calculate the

connectivity of a mobile node, it is necessary to calculate Pr(s ≤ rt). This can be

done using (A.3) as follows:

Pr(s ≤ rt) =

∫ rt

%=0

fs(%)d% (A.8)

Finally, the probability of connectivity of SU i to the network can be obtained

using (4.1).

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