i

PERFORMANCE STUDY OF LICENSED USERS IN THE PRESENCE OF MALICIOUS NODES IN COGNITIVE RADIO ENVIRONMENT

by

Manya Gautam

A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in

Telecommunications Examination Committee: Professor Kazi M. Ahmed (Chairperson) Dr. R. M. A. P. Rajatheva Assoc. Prof. Tapio J. Erke Nationality: Nepali Previous Degree: Bachelor of Engineering in Electronics and Communication Tribhuvan University Kathmandu, Nepal Scholarship Donor: AIT Fellowship

Asian Institute of Technology School of Engineering and Technology

Thailand December 2008

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Acknowledgements

I would like to express my sincere gratitude to my advisor Professor Kazi M. Ahmed for his stimulating suggestions and encouragement that helped me in all the times to accomplish this thesis work. I would also like to thank the examination committee members Dr. R. M. A. P. Rajatheva and Assoc. Prof. Tapio J. Erke who monitored my work and provided me with valuable comments and suggestions. I further would wish to thank the rest of the faculty members and all the staff members in the Telecommunications field of study for their constant help during my study period.

My heartfelt thanks go to my parents for the immense faith they had on me. It would not have been possible for me to come this long way without their constant help and support. I am totally indebted for their love and inspiration which always helped me achieve my dreams.

A special thanks also goes to Miss Sonia Majid for her constant help and moral support whenever I needed.

Finally I would like to express a warm gratitude to all my friends for being with me and supporting me during my stay in AIT.

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Abstract

Cognitive Radio (CR) is an emerging wireless technology based on Software Defined Radio (SDR) that is aware of its environment and location and can make decisions on reusing the available frequency band dynamically. The efficiency of CR totally depends on its sensing ability so that it does not create interference to the primary or licensed user of that frequency. Various techniques have been proposed for spectrum sensing out of which cooperative sensing provides a much reliable result of the presence or absence of primary users (PUs) by combating the effect of fading or shadowing. Recent works on cooperative detection techniques have shown the improvements in the probability of detection and false alarm. However, the performance of cooperative sensing is severely degraded due to the presence of malicious secondary users (SUs). The security aspect of CR is a subject of research.

In this thesis the effect of malicious users in CR networks is studied in a cooperative sensing environment. The capacity degradation of the PUs due to the interference created by such malicious act of the SUs is evaluated by calculating the signal to interference ratio (SIR). Due to this SIR the capacity of the primary gets degraded. In order to upgrade the performance of the PU a power control algorithm, in which transmit power of the PU is increased to minimize the effect of the interference, is applied. However, the increase in this power level of the PU should not cause interference to other adjacent PUs.

A fixed-step power control technique is applied to the PU in which the power level of the PU is increased with a step size of 1 dB. Results show a significant increase of 4-5% in the capacity of the PU for small increment of 1 dB power level, still maintaining the SIR level of the neighboring PUs. However, for a larger increment the interference becomes prominent.

In order to protect the PU’s transmission from SUs a protected distance for the primary user can be selected within which the SUs are not allowed to transmit. The simulation is carried out to calculate the protection distance for the primary user. Results show that as the probability of detection is high the required protection distance is low and vice-versa. All the simulations are implemented in a MATLAB simulator.

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Table of Contents

Chapter Title Page Title Page i Acknowledgements ii

Abstract iii

Table of Contents iv

List of Abbreviations vi List of Symbols vii

List of Figures ix

List of Tables xi 1 Introduction 1

1.1 Background 1

1.2 Statement of Problem 2

1.3 Objectives 3

1.4 Limitations and Assumptions 3

1.5 Report Outline 3 2 Literature Review 4

2.1 Cognitive Radio 4

2.2 Functions of Cognitive Radio 5

2.3 Spectrum Detection 7

2.4 Impact of Malicious Users 16

2.5 Power Control Technique 18 3 Methodology 20

3.1 System Model 20

3.2 Channel Model 21

3.3 User Cooperation 21

3.4 Method to Detect Malicious Users 22

3.5 Capacity Calculation 22

3.6 Simulation Model 23

3.7 Simulation Parameters 26 4 Simulation Results and Discussion 27

4.1 Cooperative Sensing Technique 27

4.2 Maximum Capacity of PU With and Without Malicious Users 32 4.3 Performance of PU with Power Control 35

4.4 Protection Distance for PU 39

v

5 Conclusion and Recommendations 41

5.1 Conclusion 41

5.2 Recommendations 41 References 43

Appendix A 46

Appendix B 52

vi

List of Abbreviations

AF Amplify and Forward AWGN Additive White Gaussian Noise BS Base Station BWRC Berkeley Wireless Research Centre CDF Cumulative Distribution Function CORVUS Cognitive Radio Approach for Usage of Virtual Unlicensed Spectrum CR Cognitive Radio dB Decibels DF Decode and Forward DFS Dynamic Frequency Selection DSA Dynamic Spectrum Allocation FCC Federal Communications Commission FFT Fast Fourier Transform GCC Group Control Channel IID Independent and Identically Distributed LocDef Location-Based Defense LU Licensed User LV Location Verifiers MAC Medium Access Control MATLAB Matrix Laboratory PDA Personal Digital Assistant PDF Probability Density Function PSD Power Spectral Density PU Primary User PUE Primary User Emulation QoS Quality of Service RF Radio Frequency RSS Received Signal Strength SDR Software Defined Radio SIR Signal to Interference Ratio SNR Signal to Noise Ratio SSDF Spectrum Sensing Data Falsification SU Secondary User SUG Secondary User Group SUL Secondary User Link UWB Ultra Wide Band TDMA Time Division Multiple Access TP Transmit Power TPC Transmit Power Control TV Television UCC Universal Control Channel

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List of Symbols

Symbols Definition d Propagation distance d0 Reference distance close to the base station dk Distance between the kth cognitive radio and the primary e1[k] First quartile of the energy values e3[k] Third quartile of the energy values eiqr[k] Inter-quartile range el[k] Lower bound for the energy value eu[k] Upper bound for the energy value et Threshold for energy detector fk(t) N basis functions hk(t) Impulse response of matched filter hr Height of receiver ht Height of transmitter n Path loss exponent n(t) Additive white Gaussian noise

( )icp Probability of detection by user Ui under cooperation scheme ( )inp Probability of detection by user Ui under non-cooperation scheme

s(t) Transmit signal of primary user x(t) Signal received by cognitive radio user yk(t) Output of the matched filter

rdyΘ Received signal from relay to destination ysd Received signal from source to destination C Shannon capacity DN Designated Controller F(t) CDF of the received signal strength Gr Antenna gain of the receiver Gt Antenna gain of the transmitter L System loss N Number of samples of energy detection Pd Probability of detection Pd,t Probability of detection for a given threshold t Pf Probability of false alarm Pk Transmit power of kth cognitive radio Plow Received power due to primary user at the boundary of decidability Pm Probability of miss detection Pt Transmitted signal power Pupp Maximum power level when cognitive users are close to the primary user P* Critical power of detection scheme PL Path loss Qp Transmit power of primary user Qc Transmit power of secondary user

maxcQ Maximum value of Qc in dB

( )xS fα Spectral correlation function

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Symbols Definition Tc Time taken to vacate the band under cooperation scheme Tn Time taken to vacate the band under non-cooperation scheme T(y) Test statistic for energy detector U Number of cognitive radios W Bandwidth Xs Transmitted signal from source Xσ Zero-mean Gaussian distributed random variable with standard deviation σ � Tolerable false alarm probability ε1 Expected maximum error ρ Reference distance ratio ρ

’ Measured distance ratio ρ (θ) Normalized correlation of received signals λ Threshold for energy detector γ Signal to noise ratio �(�) Signal to noise plus interference ratio for link l η Path loss µn/c Agility gain

2zσ Variance of zero-mean additive white Gaussian noise

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List of Figures

Figure Title Page 1.1 Spectrum Utilization Measurement at BWRC 1

2.1 Spectrum Hole Concept 4

2.2 Basic Cognitive Cycle 6

2.3 Classification of Spectrum Sensing Techniques 7

2.4 Matched Filter Demodulator 8

2.5 Typical Block Diagram of Energy Detector 9

2.6 Position of SNRWALL 10

2.7 Cooperation in Cognitive Radio 13

2.8 Relaying Protocol 13

3.1 System Diagram 20

3.2 Simulation Model 23

3.3 Flow Diagram of Simulation Model 24

3.4 Flow Diagram of Cooperative Spectrum Detection Technique 25 4.1 Probability of detection without malicious nodes 28 4.2 Probability of false alarm without malicious nodes 28 4.3 Probability of detection with 10% Always Yes nodes 29 4.4 Probability of false alarm with 10% Always Yes nodes 29 4.5 Probability of detection with 10% Always No nodes 30 4.6 Probability of false alarm with 10% Always No nodes 30 4.7 Probability of detection without malicious nodes for different number of

secondary users (SUs) 31

4.8 Probability of detection with ‘Always No’ malicious nodes for different number of secondary users (SUs) 32

4.9 Probability of detection with ‘Always Yes’ malicious nodes for different number of secondary users (SUs) 32

4.10 Capacity of primary user (PU) with and without malicious users taking PU’s transmit power (TP) =30 dBm and SU’s TP=30 dBm for 50 cooperating

SUs 33

4.11 Capacity of primary user (PU) with and without malicious users taking PU’s transmit power (TP) =30 dBm and SU’s TP=20 dBm for 50 cooperating

SUs 34

4.12 Capacity of primary user (PU) with and without malicious users taking PU’s transmit power (TP) =30 dBm and SU’s TP=30 dBm for 50 cooperating

SUs 35

4.13 Capacity of primary user (PU) without malicious users applying power control with 1 dB step size for 50 cooperating SUs and 10 PUs 36

4.14 Capacity of primary user (PU) with malicious users applying power control with 1 dB step size for 50 cooperating SUs and 10 PUs 36

4.15 Capacity of primary user (PU) without malicious users applying power control with 2 dB step size for 50 cooperating SUs and 10 PUs 37

4.16 Capacity of primary user (PU) with malicious users applying power control with 2 dB step size for 50 cooperating SUs and 10 PUs 38

4.17 Protection distance for primary user (PU) with and without malicious users taking 50 SUs 39

x

4.18 Protection distance for primary user (PU) with and without malicious users taking 10 SUs 40

A.1 Probability of detection with and without malicious nodes for 50 cooperating SUs 46

A.2 Probability of false alarm with and without malicious nodes for 50 cooperating SUs 46

A.3 Probability of detection with 10% Always Yes nodes using malicious node detection scheme 47

A.4 Probability of false alarm with 10% Always Yes nodes using malicious node detection scheme 47

A.5 Capacity of primary user (PU) with and without malicious users taking PU’s transmit power, TP=30 dBm and SU’s TP=10 dBm for 50 cooperating SUs 48

A.6 Capacity of primary user (PU) without malicious users applying power control with step size of 3 dB for 50 cooperating SUs and 10 PUs 49

A.7 Capacity of primary user (PU) with malicious users applying power control with step size of 3 dB for 50 cooperating SUs and 10 PUs 49

A.8 Capacity of primary user (PU) without malicious users applying power control with step size of 4 dB for 50 cooperating SUs and 10 PUs 50

A.9 Capacity of primary user (PU) with malicious users applying power control with step size of 4 dB for 50 cooperating SUs and 10 PUs 51

B.1 Flow diagram for calculation of PU capacity 52

xi

List of Tables

Table Title Page

3.1 Simulation parameters for performance analysis of primary user (PU) in cooperative sensing environment 26

4.1 Simulated SIR of neighboring primary user (PU) after power control considering malicious and no malicious case (for step size of 1 dB) 37

4.2 Simulated SIR of neighboring primary user (PU) after power control considering malicious and no malicious case (for step size of 2 dB) 38

A.1 Simulated SIR of neighboring primary user (PU) after power control considering malicious and no malicious case (for step size of 3 dB) 50

A.2 Simulated SIR of neighboring primary user (PU) after power control considering malicious and no malicious case (for step size of 4 dB) 51

1.1 Background

The wireless communication technology is advancing at a faster pace providing network services anywhere and anytime. The a challenge of developing newer wireless systems and standards for many types of telecommunication traffic than just mobile voice telephone calls. Hence, newer systems and standards are being implemented to provide high speed data communication services along with voice calls demanding more radio resourcesfor spectrum leads to the scarcity of is increasing. Thus, an efficient utilization of radio spectrum is required.radio technology intends to fulfill this requirement of efficient radio The basic idea behind cognitive radio is the utilization of unused frequency bands of a primary or a licensed user by a secondary or an unlicensed user without interfering the primary users’ (PUs’) communication and providing the requfor the secondary users (SUs).

The study of spectrum usage has usage of spectrum makes the scenario more thoughtfulcan be seen that the radio resource usage is extremely high in but is very low in the 3-5 GHz range.which is defined as “a band of frequencies assigned to a PU, but, at a particular time and specific geographic location, the band is not being utilized by that user” (can be observed from the figureas a spectrum hole as it is not being utilized properly. Thus, spectrum utilization can be improved by allowing the SUcognitive radios (CRs) for efficient utilization of radio resource.

Figure 1.1 Spectrum Utilization Measurement at BWRC

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CHAPTER 1

INTRODUCTION

The wireless communication technology is advancing at a faster pace providing network services anywhere and anytime. The immensely successful cellular system hasa challenge of developing newer wireless systems and standards for many types of elecommunication traffic than just mobile voice telephone calls. Hence, newer systems and standards are being implemented to provide high speed data communication services

demanding more radio resources. However, this increasing demato the scarcity of the radio spectrum whose demand by wireless systems

. Thus, an efficient utilization of radio spectrum is required. The cognitive radio technology intends to fulfill this requirement of efficient radio spectrum utilization. The basic idea behind cognitive radio is the utilization of unused frequency bands of a primary or a licensed user by a secondary or an unlicensed user without interfering the

communication and providing the required Quality of Sfor the secondary users (SUs).

has however shown that the radio spectrum is not scarce. The makes the scenario more thoughtful as can be seen from

that the radio resource usage is extremely high in the frequency below 3 GHzGHz range. This leads to know a term called spectrum holes

“a band of frequencies assigned to a PU, but, at a particular time and specific geographic location, the band is not being utilized by that user” (Haykin,

ure that the frequency band from 3-5 GHz can be considered as a spectrum hole as it is not being utilized properly. Thus, spectrum utilization can be

SU to access these spectrum holes. This provides the basis for for efficient utilization of radio resource.

Spectrum Utilization Measurement at BWRC (Cabric et al., 2004a)

The wireless communication technology is advancing at a faster pace providing network system has put forward

a challenge of developing newer wireless systems and standards for many types of elecommunication traffic than just mobile voice telephone calls. Hence, newer systems and standards are being implemented to provide high speed data communication services

increasing demand by wireless systems

The cognitive spectrum utilization.

The basic idea behind cognitive radio is the utilization of unused frequency bands of a primary or a licensed user by a secondary or an unlicensed user without interfering the

Service (QoS)

spectrum is not scarce. The Figure 1.1. It

the frequency below 3 GHz a term called spectrum holes

“a band of frequencies assigned to a PU, but, at a particular time and Haykin, 2005). It

5 GHz can be considered as a spectrum hole as it is not being utilized properly. Thus, spectrum utilization can be

. This provides the basis for

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In order to access the spectrum holes and increase the spectrum utilization a cognitive radio user has to be aware of the primary or the licensed users. The SUs can change its operating environment and automatically adjusts itself to maintain desired communications whenever it senses the spectrum holes. Frequency of operation, transmitter power and modulation are few of the operating parameters that can automatically be adjusted in a cognitive radio system. Since SUs can use the spectrum band of the PU only when the PU is not active, this leads to a challenge in detecting the PU efficiently so that the SU does not interfere with the PU’s communication.

Cognitive radio has indeed helped in efficient utilization of spectrum by letting the secondary users access the vacant primary users’ spectrum. However, these secondary users have to vacate the occupied band whenever the primary users need it as primary users will be having the higher priority to access the spectrum. Thus, secondary users have to be intelligent enough to detect these primary users in order not to interfere the primary users’ communication. This process of detecting primary users is called spectrum sensing. Sensing should be performed efficiently since the robustness of cognitive system depends basically on the efficiency of the detection method. This leads to the development of efficient detection techniques. Different techniques are proposed for the detection of the primary users. Transmitter detection which includes matched filter detection, energy detection and cyclostationary detection are such proposed techniques in which each cognitive user detects the presence or absence of the primary user and makes its decision individually. However, in the situations such as multipath fading and shadowing the signal strength might be severely degraded which results in wrong decision of spectrum occupancy by some of the secondary users. This might also result when some of the secondary users are hidden from the primary users. This problem called the hidden node problem can thus be solved by implementing cooperative detection between secondary users than individual detection techniques. Thus, cooperative detection is an efficient spectrum sensing method in which information from multiple cognitive radio users is included for primary user detection.

1.2 Statement of Problem

Cooperative detection technique is more advantageous than non-cooperative detection in the situation of weak signals arriving at the SUs which makes the detection decision either wrong or it takes too long to make the decision about the PUs. In (Cabric et al., 2004b) a centralized cooperative network called CORVUS is proposed in which the cognitive users form a group and they use group control channel to communicate with each other and a universal control channel to communicate between different groups. The access point collects the sensing information from all the users and this information is then used to alert the cognitive user about the primary user presence. In (Ganesan and Li, 2007) the benefits of cooperation in reducing the detection time thereby increasing the agility i.e. the ability to quickly detect the spectrum is shown first considering the case of two user network and then considering multiple user network. However, there exists a problem of some malicious users in the cooperative scenario of cognitive radio that degrades the performance of the cooperative sensing. The malicious users are present in the cognitive radio network which provide false information to the cooperating users and obstruct the chance of utilizing the spectrum holes by other legitimate cognitive users and even the primary user. In (Mishra et al., 2006) such trust issues in cooperative sensing are raised and the technique to identify such malicious users is given in (Kaligineedi et al., 2008). Because of such malicious users the frequency band might also not be free for the primary user itself. This results in the interference to the primary user affecting its overall

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performance. Thus, it is interesting to study the impact of malicious users in the cooperative scenario and enhance the performance of the primary users in order to have a robust and reliable cooperative detection technique.

1.3 Objectives

The overall objective of this thesis is to study the robustness and reliability issue in cognitive radio network due to malicious users. The specific objectives are given as follows:

1. To study existing detection techniques for the cognitive radio network. 2. To analyze the performance of cooperative detection method. 3. To examine the impact of malicious users in the performance of the primary user as

well as cooperative detection method. 4. To develop and implement the simulation model for the impact of malicious users

on primary user and cooperative detection method. 5. To implement the power control technique for enhancing the performance of

primary user. 1.4 Limitations and Assumptions

1. All the cognitive radios use energy detectors and can distinguish between primary user’s signal from a secondary user’s signal.

2. The channel model is comprised of Log-normal shadowing. 3. The shadowing components are assumed to be independent of each other. 4. A perfect channel conditions is assumed for the control channels for sending the

sensing data to an access point. 1.5 Report Outline

Chapter 1 covers the introduction of this thesis with some background towards the motivation of the thesis which is followed by statement of problem, specific objectives and limitations.

Chapter 2 provides the insight view of the related terminologies and explains some related work in the literature review for the problem proposed. The methodology to fulfill the set objective for the simulation is explained in Chapter 3. The obtained simulation results and discussion are described in Chapter 4 which is followed by conclusions and recommendations for further studies in Chapter 5. Finally the references for further reading and appendix are presented.

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CHAPTER 2

LITERATURE REVIEW

2.1 Cognitive Radio

Cognitive Radio (CR) is a new and emerging technique for wireless communication which is capable of altering and adapting its transmitter and receiver parameters to communicate efficiently without causing interference to the licensed users. The idea of CR was first proposed in the article by Joseph Mitola III and Gerald Q. Maguire, Jr. (1999). Mitola described it as a particular extension of Software Defined Radio (SDR) with learning and reasoning capabilities that works in application layer and higher (Mitola III, 2000). After the pioneering work of Mitola the FCC has played a vital role in the development of CRs. According to FCC “Cognitive Radio is a wireless node or network that is capable of dynamically sensing and locating unused spectrum segments and communicating by using the unused spectrum segments in ways that cause no harmful interference to the primary users of the spectrum” (FCC, 2003). A cognitive radio technology can be aware of its operating environment and can automatically adjust itself to maintain its communication. Frequency of operation, transmitter power and modulation are some of the operating parameters that can be adjusted in a cognitive radio system. The important feature of CR is dynamic spectrum allocation (DSA) whose ability is to adapt their operation by sensing the radio environment around it in an opportunistic manner (Akyildiz et al., 2006).

Since cognitive radios (CRs) are allowed to make use of band of frequencies of the primary users, the problem of so called scarcity of spectrum can be solved by its implementation and the spectrum utilization is improved. Figure 2.1 shows the concept of spectrum holes in which cognitive or secondary users are allowed to access the free bands. It also shows how SUs move to another spectrum hole, when the PUs reappears, in order to avoid interference with them. Thus, we can observe that knowing the availability of the spectrum hole only is not enough to decide the usage of the unused spectrums. Many factors like frequency selection, modulation schemes, and power level should be considered to sense the variation in radio environment so as to avoid possible interference to other users. CR promises all these functions and helps to utilize the spectrum band effectively.

Figure 2.1 Spectrum Hole Concept (Akyildiz et al., 2006)

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Recently UWB technology is also gaining popularity and acting as a competitor for CR which can also be used to share spectrum with other users due to its low transmit power. The disadvantage being the requirement of large bandwidth, UWB is not suitable for those systems having lower bandwidth. Thus, cognitive radio technology proves its efficiency in that respect.

The Federal Communications Commission (FCC) has identified the following features that CRs can incorporate to enable a more efficient and flexible usage of free spectrum (FCC, 2005):

� Frequency Agility – It is the ability of the CR to change its operating frequency to optimize its use in adapting to the environment.

� Dynamic Frequency Selection (DFS) – The radio is able to detect signals from the transmitters to choose an optimal operating environment.

� Adaptive Modulation – This feature allows the CR to be able to modify the transmission characteristics and waveforms so that it can exploit the opportunities for using the spectrum.

� Transmit Power Control (TPC) – This allows CR to transmit at full power when necessary as well as to lower the power level when higher power limits are not required.

� Location Awareness – The CR is able to determine its location as well as the location of other devices operating in the same spectrum to optimize transmission parameters for increasing spectrum re-use.

� Negotiated Use – The cognitive radio may have some mechanism to enable the sharing of spectrum under prearranged agreements between a licensee and a third party or on an ad-hoc or real-time basis.

CR is an autonomous technology which can learn from outside environment and adapt to the variations such as transmit power, modulation schemes and carrier frequency, in order to provide reliable communication and efficient utilization of spectrum. This is achieved by changing the software rather than changing the hardware itself since CR is built upon software defined radio (SDR). SDR also coined by Mitola is the basis of CR. An SDR is a radio communication system which can tune to any frequency band. It can also tune to receive any modulation across a wide frequency band by means of programmable hardware which is controlled by software. The change in hardware itself is not required. On the other hand CR is an SDR that is capable of sensing its environment, tracking the necessary changes and reacting upon its findings i.e. CR can dynamically and automatically adjust its operating parameters. Thus, we can say that a CR is a redefined SDR (Jondral, 2005). Hence, the software-basis of CR makes it face the security problem of unreliable software same as SDR. The software usage is also prone to malicious users. Piracy and hacking of the software can make the cognitive system an insecure and unreliable way for communication.

2.2 Functions of Cognitive Radio

A cognitive cycle comprising of the major tasks performed by a CR is as depicted in Figure 2.2. The main functions of cognitive radio are Spectrum Sensing, Spectrum Management, Spectrum Mobility and Spectrum Sharing (Akyildiz et al. , 2006).

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Figure 2.2 Basic Cognitive Cycle (Akyildiz et al., 2006)

2.2.1 Spectrum Sensing

The major function of cognitive radio is spectrum sensing which scans and detects unused spectrum for their use without causing any interference to other users. Cognitive radio is aware of its surrounding. This function of the cognitive radio enables it to adjust to the environment by detecting the spectrum holes. For detecting the spectrum holes it is important to detect the primary users that are present in that particular band. Different spectrum sensing method can be classified as transmitter detection, cooperative detection and interference-based detection.

2.2.2 Spectrum Management

Another function of cognitive radio is to analyze the spectrum and decide on the allocation of the free spectrum in order to meet the requirements of the user. Spectrum analysis and spectrum decision forms the basis for spectrum management. It is necessary to analyze the spectrum holes as they have different behavior at different times and locations. Spectrum available at one time instant and in one location may not be available at other time and location. So the analysis of the spectrum should be proper and continuous. Spectrum decisions are made based on the analysis done to allocate the available holes for data transmission maintaining the required QoS for the SUs.

2.2.3 Spectrum Mobility

This becomes important when the CR user changes its frequency of operation (Akyildiz et al., 2006). Spectrum mobility has its function when the frequency channel becomes worse or when the primary user reappears in the particular frequency band. Under these conditions the secondary users need to vacate the frequency band or move to some other band of frequency. This gives rise to a condition termed as spectrum handoff. The function of spectrum mobility is to make sure that the transition from one frequency to other is made smoothly without significant delay and performance degradation of the ongoing

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communication. Therefore, mobility management protocols should have the knowledge of the duration of a spectrum handoff.

2.2.4 Spectrum Sharing

This process contains five major steps viz., spectrum sensing, spectrum allocation, spectrum access, transmitter-receiver handshake and spectrum mobility (Akyildiz et al., 2006). This functionality of CR helps in sharing the available spectrum between coexisting CR users. All the tasks of spectrum sensing, spectrum allocation, spectrum access, receiver handshake and spectrum mobility should be well coordinated in order to provide the fair spectrum sharing among coexisting users.

2.3 Spectrum Detection

The spectrum scanning helps to detect the spectrum holes that can be utilized for secondary users’ communication purpose. The detection of spectrum holes should be performed efficiently since efficient detection helps the cognitive radio to adapt to its environment. This process of detecting the spectrum holes is called spectrum sensing which can also be called spectrum detection. The robustness of cognitive system depends basically on how efficient is the detection method as all other procedure follows this. Generally, we can classify the detection techniques as non-cooperative and cooperative. More specifically, transmitter detection and interference based detection can be considered to be non-cooperative detection technique. The various detection techniques are shown in Figure 2.3.

Figure 2.3 Classification of Spectrum Sensing Techniques (Akyildiz et al., 2006)

2.3.1 Transmitter Detection

Transmitter detection is based on the detection of weak signals from a primary transmitter. The cognitive radio should determine if the signal from a primary transmitter is locally present or not. It can then be defined as a binary signal detection problem modeled as a hypothesis testing problem as follows:

H0: x (t) = n (t) H1: x (t) = s (t) + n (t)

( 2.1 )

where x(t) is the signal received by the cognitive radio user, s(t) is the transmitted signal of the primary user and n(t) is the additive white Gaussian noise (AWGN). H0 denotes a null hypothesis stating the absence of primary user in a spectrum band and H1 denotes an

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alternative hypothesis stating the presence of primary user. Three techniques are generally used for the transmitter detection namely; matched filter detection, energy detection and cyclostationary feature detection.

2.3.1.1 Matched Filter Detection

A typical matched filter type demodulator is shown in Figure 2.4 (Proakis, 1995). In the figure a bank of N linear filters are used whose impulse responses are

( ) ( )k kh t f T t= −

( 2.2 )

where {fk (t)} are the N basis functions and hk (t) = 0 outside of the interval 0≤ t ≤ T. The outputs of these filters are

0( ) ( ) ( )

t

k ky t r h t dτ τ τ= −∫

=0

( ) ( )t

kr f T t dτ τ τ− +∫ , k=1, 2,..N

( 2.3 )

When the outputs of the filters are sampled at t=T, we obtain

0

( ) ( ) ( )t

k ky T r f dτ τ τ= ∫

= rk, k=1, 2,…N

( 2.4 )

A filter whose impulse response is h(t)=s(T-t) , where s(t) is assumed to be confined to the time interval 0≤ t ≤ T, is called the matched filter to the signal s(t) (Proakis, 1995) .

Figure 2.4 Matched Filter Demodulator

(Proakis, 1995)

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When the information of the primary user signal is known to the secondary user and a stationary Gaussian noise is considered, then matched filter is the optimal detector since it maximizes the received signal-to-noise ratio (SNR). Since matched filter outputs the time auto-correlation function of the transmitted primary signal given by (Proakis, 1995):

0

( ) ( ) ( )t

y t s s T t dτ τ τ= − +∫

( 2.5 )

with impulse response h(t) = s(T-t) to the signal s(t) confined to the time interval 0≤ t≤ T, this detection method needs a priori knowledge of the PU signal which include modulation type, pulse shaping, packet format etc. This leads to a significant drawback of matched filter which is that a cognitive radio requires a dedicated receiver for every primary user in order to have a prior knowledge of the primary user signal.

2.3.1.2 Energy Detection

When we have no deterministic knowledge of the primary signal and only know about the average power of the signal then the energy detector, also known as radiometer, is considered to be an optimal detector. This detection simply relies on detecting the total received energy and hence gives less importance to the signal structure. It is assumed that noise is a zero mean Gaussian process. Typical block diagram of energy detector is as shown in Figure 2.5 which consists of a band pass filter to reject the noise and interfering signals, a square-law device and an integrator.

Figure 2.5 Typical Block Diagram of Energy Detector (Ghozzi et al., 2006)

For measuring the energy of the received signal, the output signal of band pass filter with bandwidth W is first squared and then integrated over the observation interval T. The output of the integrator is then compared with a threshold, λ to decide whether a licensed user is present or absent. The threshold is dependent on the estimated noise power. If the output of the integrator is greater than the threshold, λ it is decided that H1 is true i.e. the primary signal is present in the band otherwise the primary signal is not present in the band. The PU is detected by formulating a binary hypothesis testing problem which states

0

1

( ) ( ), ( )

( ) ( ) ( ), ( )

x k n k for H PU absent

x k s k n k for H PU present

== +

( 2.6 )

where s(k) is the transmitted PU signal, n(k) is the AWGN and x(k) is the received signal. The test statistics for the energy detector is given by (Peh and Liang, 2007) as

2

1

1| ( ) |

N

k

u x kN =

= ∑ ( 2.7 )

where N is the number samplessuch that

u x k

Although energy detector is simple to implement there are various drawbacks. Since the threshold is set depending upon thenoise power will cause a significant error in detecting the primary user which in turn would lead to a performance loss of the energy detector. Also at low SNRs, the energy detector does not work properly in detectifor the SNR value below SNR

where ( /10)10 xα = . The position2.6 below:

Figure

2.3.1.3 Cyclostationary Feature Detection

The above discussed types of detection techniques are optimal for the signal of stationary nature. When the signal is not stationary typeconsidered to be the optimal searched signal, the above defined hypotheses changes to the following hypotheses testing problem:

H0: x (t) is of stationary type and the band is regarded asH1: x (t) is of cyclostationary type and the band is said to be occupied

Thus, the hypotheses testing problem is changed to testing only the cyclostationarity in the signal. The modulated signals are con

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samples. This test statistic is then compared with the threshold

1

2

10

1| ( ) |

HN

kH

u x kN

λ=

>=<

∑

Although energy detector is simple to implement there are various drawbacks. Since the threshold is set depending upon the estimated noise power, inaccuracy in estimating the noise power will cause a significant error in detecting the primary user which in turn would lead to a performance loss of the energy detector. Also at low SNRs, the energy detector

detecting the weak signals. The signal detection is impossible for the SNR value below SNRWALL (Sahai and Cabric, 2005) which is given by

2

10

110log ( )WALLSNR

αα

−=

. The position of SNRWALL for the energy detector is as shown in

Figure 2.6 Position of SNRWALL (Sahai and Cabric, 2005)

Cyclostationary Feature Detection

The above discussed types of detection techniques are optimal for the signal of stationary nature. When the signal is not stationary type, cyclostationary feature

the optimal detection technique. When this technique is selected for the searched signal, the above defined hypotheses changes to the following hypotheses testing

H0: x (t) is of stationary type and the band is regarded as free H1: x (t) is of cyclostationary type and the band is said to be occupied

Thus, the hypotheses testing problem is changed to testing only the cyclostationarity in the The modulated signals are considered to be cyclostationary as their mean and

. This test statistic is then compared with the threshold �

( 2.8 )

Although energy detector is simple to implement there are various drawbacks. Since the in estimating the

noise power will cause a significant error in detecting the primary user which in turn would lead to a performance loss of the energy detector. Also at low SNRs, the energy detector

he signal detection is impossible is given by

( 2.9 )

for the energy detector is as shown in Figure

The above discussed types of detection techniques are optimal for the signal of stationary feature detection is

detection technique. When this technique is selected for the searched signal, the above defined hypotheses changes to the following hypotheses testing

( 2.10 )

Thus, the hypotheses testing problem is changed to testing only the cyclostationarity in the their mean and

11

autocorrelation are periodic. These traits are detected by evaluating a spectral correlation function. Hence, the cyclostationary feature detector determines the presence or absence of the signal having spectral correlation at a spectral frequency f and cyclic frequency α. The spectral correlation function is defined as (Cabric et al., 2004a):

/ 2*

/2

1 1( ) lim lim ( , / 2) ( , / 2)

t

X T TT t

t

S f X t f X t f dtt T

α α α∆

→∞ ∆ →∞−∆

= + −∆ ∫

( 2.11 )

where finite time Fourier Transform is given by

/ 22

/ 2

( , ) ( )t T

j vuT

t T

X t v x u e duπ+

−

−

= ∫

( 2.12 )

The benefit of the spectral correlation function is that it differentiates the noise energy from the modulated signal energy since noise is a wide-sense stationary signal without correlation whereas modulated signals are cyclostationary with spectral correlation (Akyildiz et al. , 2006). Also spectral correlation function can be maintained even in low SNR while energy detector is inefficient in low SNR. Therefore, a cyclostationary feature detector can perform better than the energy detector since the noise variance has no impact upon cyclostationary feature detection as in the energy detection. However, the computational complexity of the cyclostationary feature detector is much higher than the energy detector.

2.3.2 Interference Based Detection

This detection method takes into account the interference present in the system which can be controlled at the transmitter by changing the radiated power, the out-of-band emissions and location of individual transmitters (Akyildiz et al. , 2006). But interference actually occurs at the receiver side causing a progressive degradation of the signal and increasing the noise floor due to the presence of various sources of interference. Therefore, a new model to measure interference, known as interference temperature was proposed by the FCC in 2003. The interference temperature acts as a threshold value that can be tolerated by the primary user which helps in quantifying and managing the sources of interference in a radio environment (Haykin , 2005). The CR must be able to approximate the interference temperature which can be tolerated by the primary user. The interference temperature thus provides an accurate measure for the acceptable level of RF interference in a particular band of frequency. For this detection method, it can be considered that the primary user can tolerate interference for certain time units xt∆ such that the secondary user can take the

measurement of the interference temperature within that particular time unit and proceed for the detection of the primary user (Cabric et al., 2004b). After xt∆ time unit all the

secondary users must vacate the frequency band of the primary user. The cognitive radio user should have it’s transmit power at a certain level such that it does not raise the noise floor of the primary users beyond a specific value. Any transmission in a band is not allowed if it increases the noise floor beyond the interference temperature limit and if the interference temperature is not beyond the limit in a frequency band then that frequency band is made available for the use of secondary users (Haykin , 2005).

12

2.3.3 Cooperative Detection

In the case of non-cooperative detection method like transmitter detection, the SUs detect the PU individually through their local observations of the received signal strength. However, the signal strength may be severely degraded due to multipath fading and shadowing which results in wrong decisions on spectrum occupancy by some of the SUs that cause interference to the primary users. Also the transmitter detection methods cannot avoid the hidden terminal problem which takes place when a CR cannot detect all of the radios with which it might interfere because some of the radios are hidden from it (Akyildiz et al., 2006).

Therefore, individual or non-cooperative detection is not enough and cooperative detection between users is required to combine the sensing results to have minimum probability of interference to the primary users. Each user measures the local received signal. The local signals are then exchanged between all the cognitive radio users which provide a global decision on the primary user being present or not. Thus, cooperative detection is a spectrum sensing method in which information from numerous SUs is included for PU detection. Hence, cooperative sensing provides more precise result since the uncertainty associated with individual detection can be minimized.

Cooperative communication has its root in (Cover and Gamal, 1979) who worked on the information theoretic properties of relay channel. In (Cabric et al., 2004) a cognitive radio approach for usage of virtual unlicensed spectrum (CORVUS) is proposed which is a centralized cooperative approach in which secondary users form a group called secondary user group (SUG) and use a group control channel (GCC) to communicate with each other using secondary user link (SUL) and a universal control channel (UCC) to communicate with different groups.

In (Ganesan and Li, 2005) it is shown that cooperative sensing allows the agility gain by reducing the detection time. In (Ganesan and Li, 2007a) the benefits of cooperation in CR, by considering a simple two-user network, is shown which is extended for a multi user case in (Ganesan and Li, 2007b). Here the two cognitive users are allowed to cooperate with one acting as a relay for the other. However, in a multi-user case, the cognitive users first have to be able to choose good partners. In the presence of multiple users, the users are grouped into fixed pairs in which one user act as a relay for the other based on the power level (Ganesan and Li, 2007b). The SUs are divided into two sets. If a user set Uk∈U(Plow , *P ), these users are allowed to search for the relay whereas if a user Uk ∈U( *P , Pupp), these users does not need help from other users and are able to detect the presence of primary by themselves. Here it is defined that Plow is the received power due to primary user at the boundary of decidability, Pupp is the maximum power level reached when cognitive users are very close to the primary user and *P is the critical power of the detection scheme. The users in the range of (*P , Pupp) have a higher possibility of being optimal relay users (Sun et al., 2006). The critical power can be determined by fixing a threshold probability of detection p* such that if the detection probability of a cognitive user is greater than p* without cooperation then it does not search for any relay user. In (Ganesan and Li, 2007b) the value for P* is uniquely determined by

*

1*1P pα + =

( 2.13 )

which yields

13

**

*

ln( / )( )

ln

pP

p

α= ( 2.14 )

where α is the tolerable false alarm probability.

Various cooperative protocols are discussed by (Laneman et al., 2004). A fixed relay protocol is also discussed where the relay either amplifies or decodes and retransmits the received signal. These are called the Amplify and Forward (AF) and Decode and Forward (DF) respectively. The AF scheme is used for calculating the agility gain in (Ganesan and Li , 2005).

2.3.3.1 Agility Gain for two user network

The agility gain for a two user cooperative sensing scenario as shown in Figure 2.7 is discussed in (Ganesan and Li, 2005). The two users U1 and U2 operate in a fixed TDMA mode and the relaying protocol operates in amplify and forward mode as shown in Figure 2.8.

Figure 2.7 Cooperation in Cognitive Radio

(Ganesan and Li, 2005)

Figure 2.8 Relaying Protocol

(Ganesan and Li, 2005) It is shown that the average detection time is decreased which implies an increase in agility. Suppose the number of slots taken by U1 in a non-cooperative network to detect the presence of primary is nτ which is equal to k. Then,

14

(1) ( 1) (1)Pr{ } (1 ) kn n nk p pτ −= = −

( 2.15 )

Where ( )i

np is the probability of detection by user Ui under non-cooperation and

1

1

1(1) Pnp α +=

( 2.16 )

2

1

1(2) Pnp α +=

( 2.17 )

Thus, the total time taken by both the users to vacate the band is given by (1) (2)

(1) (2) (1) (2)

22

n n

nn n n n

p p

Tp p p p

+−=

+ −

( 2.18 )

and the total time taken to vacate the band under cooperation scheme is given by (1) (2)

(1) (2) (1) (2)

22

c n

cc n n n

p p

Tp p p p

+−=

+ −

( 2.19 )

Hence, the agility gain is given by

/ (2) nn c

c

T

Tµ =

( 2.20 )

2.3.3.2 Agility Gain for multi-user network

It is assumed that there are m cognitive users operating in a single carrier. Also, mΤ is the set of all possible permutations to arrange the operation of cognitive users and S is any order which is an element ofmΤ . Then for any given order mS∈ Τ , it is given by (Ganesan and Li , 2005) that

( ) ( )1 / /c nT m S T m S< <

( 2.21 )

where ( )/nT m S and ( )/cT m S are the time taken to detect the primary user when all the m

users are under non-cooperative and cooperative protocol respectively. The average detection time in case of cooperation and non-cooperation are given respectively by

( ) ( )1/

!m

c cS

T m T m Sm ∈Τ

= ∑

( 2.22 )

( ) ( )1/

!m

c cS

T m T m Sm ∈Τ

= ∑

( 2.23 )

And the agility gain in this case will be

15

( )

( )n

mc

T m

T mµ =

( 2.24 )

Also the probability of detection under the cooperation and non-cooperation schemes are given by

(1) (2) (1) (2)c n c np p p p p= + −

( 2.25 )

And (1) (2) (1) (2)' n n n np p p p p= + − ( 2.26 )

2.3.3.3 AND/OR Fusion Rule

In (Peh and Liang, 2007) the minimum achievable probability of false alarm for a given targeted probability of detection and also the maximum achievable probability of detection for a targeted probability of false alarm is found in a cooperative network. Cooperative sensing using AND and OR fusion scheme with energy detector are used to present the results. The local sensing results are collected at the fusion center where data fusion takes place and the final sensing result is determined. The test statistics for energy detector is given by

2

1

1( ) | ( ) |

N

n

T y y nN =

= ∑

( 2.27 )

where T(y) is a random variable with a chi-square distribution of the probability density function (PDF) and 2N degrees of freedom. Then for a set threshold ε, the probability of false alarm is given by

( ) 2 2

0 0( ( ) ) / 20 2

0

1Pr( ( ) | ) (( 1) )

2T y

fu

P T y H e Q Nµ σ

ε

εε εσπσ

∞− −= > = = −∫

( 2.28 )

where H0 is the hypothesis for inactive primary and H1 is the hypothesis for active primary user. Also the probability of detection for the threshold ε and the primary user’s signal to

noise ratio received at the secondary user 2

2s

u

σγσ

= is given by

( ) 1 2

Pr( ( ) | ) (( 1) )2 1d

u

NP T y H Q

εε ε γσ γ

= > = − −+

( 2.29 )

The probability of false alarm for a targeted detection probability dP is also given by (Peh and Liang, 2007) as

( )( )12 1 dfP Q Q P Nγ γ−= + +

( 2.30 )

and for a targeted false alarm probability, fP , the probability of detection using energy detector is given by

( )( )11

2 1fdP Q Q P Nγ

γ−

= − +

( 2.31 )

The cooperative sensing is then done by fusing these individual sensing data of the secondary users using either OR fusion rule or AND fusion rule. In OR fusion rule the

16

final decision of primary user is made when at least 1 out of k secondary users detect the primary and the Pd and Pf will respectively be

,

1

1 (1 )k

d d ii

P P=

= − −∏

( 2.32 )

,

1

1 (1 )k

f f ii

P P=

= − −∏

( 2.33 )

,d iP and ,f iP being the probability of detection and false alarm of the i th secondary user.

Similarly in AND fusion rule, the final decision for the presence of primary is made when all the k secondary users detect the primary users and the Pd and Pf in this case will respectively be

,

1

( )k

d d ii

P P=

= ∏

( 2.34 )

,

1

( )k

f f ii

P P=

= ∏

( 2.35 )

Similar approach for collaborative detection of signal under shadowing or fading environment using the OR fusion rule is explained in (Ghasemi and Sousa, 2005). Here also the increase in probability of detection due to collaborative sensing is shown than the individual sensing.

2.4 Impact of Malicious Users

Collaborative sensing scheme outperforms individual sensing of primary users in situation of fading or shadowing. However, CR built upon the SDR platform faces the same problem as SDR. Since CRs are reconfigurable and based on software applications it is possible that the malicious users obstruct the spectrum sensing procedure. The security aspects of spectrum sensing has to be dealt beforehand in order to obtain full benefits from the CR technology. The two security threats to cooperative sensing in CR networks have been discussed in literature viz., incumbent emulation and spectrum sensing data falsification (Chen et al., 2008a).

2.4.1.1 Incumbent Emulation (IE) Attacks

In this attack, a malicious SU tries to achieve higher priority over other SUs by transmitting signals whose characteristics emulate the characteristics of incumbent or PU causing the legitimate SUs to erroneously identify the attacker as a PU. Because of the programmability of CR, a malicious user can easily modify its emission parameters such as modulation, frequency, power, etc. to resemble like a PU.

A PUE attack is classified by (Chen et al., 2006) as a Selfish PUE attack and a Malicious PUE attack. In selfish attack the objective of attacker is to maximize its own usage of spectrum resources by preventing other secondary users from accessing the band whereas in malicious attack the objective of the attacker is to prevent the legitimate secondary users from detecting and using the empty licensed bands causing denial of service. Thus, a transmitter verification scheme called LocDef (Localization-based Defense) is proposed in (Chen et al., 2008b) in which it is verified whether a given signal is of a legitimate primary user or a cognitive user by utilizing the signal characteristics and location of the signal transmitter. Here the primary user is assumed to be the TV transmitters and receivers whose transmitter output power is very large and the secondary users are

17

assumed to be handheld CR devices which form a mobile ad hoc network. With these assumptions the PUE attackers are detected by using the signal power level and the signal source’s location since the location of the mobile CR user and the power level will be different than the primary TV transmitters. The received signal strength (RSS) is utilized to estimate the location. For TV systems using tall towers, the two-ray ground reflection model is used in which the RSS is represented as follows:

2 2

4t r

t t r

h hRSS PG G

d L=

( 2.36 )

where Pt is the transmitted signal power, Gt and Gr are the antenna gains of the transmitter and receiver respectively, ht is the height of the transmitter, hr is the height of the receiver, d is the propagation distance and L is other system loss.

It is assumed that the location verifiers, which can be a dedicated node or a secondary user or a fixed/mobile base station, performing the location verification test are divided into two types; one or more master LVs and slave LVs. The location verification scheme for two LVs LV1 and LV2 with RSS R1 and R2 and positions (x1, y1) and (x2, y2) can be dealt by the master LV. The master LV calculates the reference distance ratio with the coordinate of the first TV tower (u1, v1) as

2 21 1 1 1

2 22 1 2 1

( ) ( )

( ) ( )

x u y v

x u y vρ

− + −=

− + −

( 2.37 )

The master LV also calculates the measured distance ratio using the RSS measurements given by

1 2

4

2 1

'd R

d Rρ = =

( 2.38 )

where d1 and d2 are the respective distances between LV1 and the signal source and LV2 and the signal source respectively. Finally, the master LV checks for

11

' [ , (1 ) ](1 )

ρρ ε ρε

∈ ++

, where ε1 (≥ 0) is the expected maximum error. If this condition is

not satisfied the location verification test for the TV tower fails. However, this detection technique is applied only for TV transmitter and receiver acting as PU.

2.4.1.2 Spectrum Sensing Data Falsification (SSDF)

The next security threat to the cooperative sensing scheme is the transmission of false spectrum sensing information by malicious SUs. Any malicious cooperating SU may transmit false local spectrum sensing data to a final decision fusion node which causes the fusion node to make wrong decision. This kind of attack is referred to as Spectrum Sensing Data Falsification (SSDF) attack in (Chen et al., 2008a).

The issues where SUs always report the presence or absence of PU is raised in (Mishra et al., 2006) followed by (Kaligineedi et al., 2008). The technique to determine such malicious users who always sends the same information of the presence or absence of the PUs is given in (Kaligineedi et al., 2008). As given in (Mishra et al., 2006), for the case when α fraction (α ε [0, 1]) of N cognitive users always reports the presence of PU the detection threshold is set at βN where β> α in such a way that a PU is declared present

18

only if βN cognitive users detect it. When this fraction starts reporting the absence of primary users, the number of real users in the system is reduced to N (1- α). The resulting probability of detection for a given threshold t is given by

( )1

(1 ) (1 ),

0

1 (1 ( )) ( )N

N i N id t i

i

P F t F tβ

α α−

− − −

=

= − −∑

( 2.39 )

where F(t) is the CDF of the received signal strength.

2.5 Power Control Technique

Power control technique is an important and simple method which helps in the resource management in mobile communication system. It is an effective way of increasing the system capacity which is based on maintaining the SIR of the system. A similar power control approach which is based on managing the interference is given by (Lin and Cruz, 2004). The logarithmic relation between instantaneous data rate on a link and signal to interference plus noise ratio given by Shannon capacity formula is used to model the power vector �. The relation is defined by

2( ) log (1 ( ))lR P W lγ= +

ur ( 2.40 )

where �= (P(1),…….,P(L)) is the transmission power vector for all transmitters in the system, W is the available bandwidth and �(�) is the signal to noise plus interference ratio at the receiver of link l. Adaptive power control is also an important functionality for cognitive radios to avoid interference to any primary users. Various power control approaches are formulated for cognitive radios. Power control based on spectrum sensing information is proposed in (Hamdi et al., 2007a) where the coexistence of the PU and SU is considered and the interference to the PU is minimized by controlling the transmit power of SU by using spectrum sensing information. The proposed algorithm follows the steps of calculating probability of miss detection which can be estimated as

1

11 ( )

N

m ii

P I YN =

= − ∑ ( 2.41 )

where Yi denotes the energy collected by the CR in time slot i and N is the total number of time slots. After calculating Pm, distance (d) or path loss (η ) is derived as

2

10log( ),

10 log( ) 10 log( )p

d dB

Q

αη

γσ

−= −

= −

( 2.42 )

where Qp is the transmit power of primary user and � is the path loss exponent. Finally Qc is calculated which is given by

max 2

2

( ) 10log( ),

( 10log( ) ) 10log( ),cQ g dB

g d dBα

η σσ−

= − ∆ +

= − − ∆ +

( 2.43 )

where maxcQ denote the maximum value of Qc in dB. A simple fixed adaptive fixed-step

power control algorithm for mobile cellular systems is proposed in (Sung et al., 2000). A fixed-step power control algorithm based on SIR is proposed in which each mobile adjusts its transmit power ( 1)n

iP + in the (n+1)th step according to the following rules:

19

1

( 1) 1

,

,

,

n ni i i

n n ni i i i

ni

P if

P P if

P otherwise

δ δ γδ δγ

−

+ −

Γ <= Γ >

( 2.44 )

where � > 1. An SIR target region [ 1iδ γ− , iδγ ] is defined for each mobile. If the SIR is

lower than the region, the base station will inform the mobile to raise the power to the next higher level. If the SIR is above the region, the power will be adjusted downwards by one level.

20

CHAPTER 3

METHODOLOGY

The main concern of this thesis is to study the reliability issue in cognitive radio technology by studying the impact of malicious secondary users in cooperative detection technique. A robust way of detecting the PU (primary user) is through user cooperation which minimizes the chance of missing the PU. When the secondary users (SUs) behave maliciously, its transmission will not be stopped even when the PU reappears. This definitely creates interference to the PU’s transmission due to the reduction in the SIR. To study this impact of malicious SUs, at first a cooperative scenario is considered without any malicious SUs and its performance is evaluated by simulations. The simulations are performed in MATLAB. The performance of the PU is then studied when cognitive users act maliciously. The impact on the capacity of primary user is then analyzed when cognitive users behave maliciously causing interference to the PU.

3.1 System Model

The general system diagram for cooperation can be shown in Figure 3.1. A group of N cognitive users and a primary transmitter are considered. A log-normal shadowing channel is assumed between primary transmitter and the cognitive radios. The components for log-normal shadow are assumed to be independent from one user to the other. All the SUs detect the primary with the help of energy detector individually and the detected decisions are sent to a designated controller. The designated controller can either be a cognitive radio itself or a base station which makes the final decision regarding the presence of primary user.

Figure 3.1 System Diagram

To analyze the robustness issue we first consider a simple cooperation model assuming no malicious users and study the performance parameters. In the next step we include the malicious users’ case and study the performance parameters. There can be M number of

21

primary users and N number of secondary users in actual environment. Out of these N secondary users, K number of users can act maliciously.

3.2 Channel Model

The channel model used for the simulation is taken as a log normal shadowing channel between primary transmitter and cognitive radio users. The log-distance path loss model for a primary transmitter and secondary receiver is given by (Rappaport, 2002)

0

0

( )[ ] ( ) 10 log( )d

PL d dB PL d nd

= +

( 3.1 )

where d is the transmitter-receiver separation distance, n is the path loss exponent and d0 is the reference distance determined from measurements close to the transmitter. However equation (3.1) does not consider the variation in signal due to environmental effect at different location having same distance between transmitter and receiver. Thus the log-normally distributed value is given by

0

0

( )[ ] ( ) 10 log( )d

PL d dB PL d n Xd σ= + +

( 3.2 )

where Xσ is a zero-mean Gaussian distributed random variable with standard deviation σ which is taken to be 4 dB (Kaligineedi et al., 2008).

The value of PL(d0) is based on the free space loss assumption from the transmitter to the distance d0. The path loss for the free space model when the antenna gains are assumed to be unity is given by (Rappaport, 2002)

( )2

2 2[ ] 10 log[ ]

4PL dB

d

λπ

= − ( 3.3 )

Thus using path loss exponent value of 2 and frequency of 800MHz the log normal shadowing loss is calculated to be

( )[ ] 20 log( ) 30.5PL d dB d Xσ= + + ( 3.4 )

3.3 User Cooperation

The cooperative sensing is the process of making a final decision for the network based on the sensing data collected from various distributed secondary users (Peh and Liang, 2007). Each user detects the PU using energy detector and the final decision is made based on the average combination rule. If e [u; k] represents the outputs of the energy detectors at various nodes at time instant k for u = 1, 2, 3… U, then e[u; k] is given by formulating a binary hypothesis testing problem which states

12

10 1

12

10 0

10log ( | [ ; ] [ ] [ ; ] | ; ( )

[ ; ]

10 log ( | [ ; ] | ) ; ( )

k

k

k

k

T T

l T

T T

l T

h u l s l z u l for H PU present

e u k

z u l for H PU absent

+ −

=

+ −

=

+

=

∑

∑

( 3.5 )

where T is the length of the sensing interval, Tk is the time instant at which the kth sensing interval starts, h[u;l] is the channel gain between the primary transmitter and the uth cognitive user, s[l] is the primary user transmitted signal and z[u; l] is the zero mean

22

additive white Gaussian noise with variance 2zσ . The average combination based detection

scheme is given by (Kaligineedi et al., 2008) as

1

0

1

1( ) [ ; ]

HU

Tu

H

e u k eU =

><∑

( 3.6 )

where eT is the threshold for the energy detector. The probability of detection and the probability of false alarm are calculated for this cooperative scenario.

3.4 Method to Detect Malicious Users

Malicious users make a distinguishing effect on the performance of cooperative sensing system. CRs become malicious due to device malfunctioning or even due to selfish nature. Thus, it is necessary to identify the malicious cognitive users interfering primary as well as secondary users. CRs can simply be malicious by acting as an ‘Always Yes’ or an ‘Always No’ nodes. An ‘Always Yes’ CR would always report the presence of the PU which is due to its energy level being greater than the threshold and the ‘Always No’ CR would always report the absence of the PU with its energy level being lower than the threshold. Thus the identification of malicious CRs is based on the energy values that differ in distribution from the energy values of legitimate CRs. An outlier detection technique is applied to identify such malicious users (Kaligineedi et al., 2008). In this method the upper and lower bound for the energy values e [u; k] is calculated at time instant k as follows:

[ ] [ ] [ ][ ] [ ] [ ]

3

1

3

3

u iqr

l iqr

e k e k e k

e k e k e k

= +

= −

( 3.7 )

where e1[k] and e3[k] represent the first and the third quartile of the values of the energy and eiqr[k] = e3[k] - e1[k] is the inter-quartile range. If a particular value of the energy lies in the range given by eu[k] and el[k] it is legitimate cognitive user else it is an outlier and its value is omitted while making the final decision.

3.5 Capacity Calculation

After the cooperation of cognitive users is dealt with, the robustness and reliability issue is studied by analyzing the malicious behavior of the cooperating users. When K out of N users are malicious its effect in user cooperation are seen. Not only that, the maliciously acting user also causes interference to the primary user. The capacity of primary gets degraded due to such malicious users’ interference. The capacity calculation is done by using the Shannon’s capacity theorem given by (Rappaport, 2002)

C = B log2 (1+S/N) ( 3.8 ) The interference caused by each cognitive user can be calculated as given by (Hamdi et al., 2007b)

'k k k kI P P d α−= = ( 3.9 )

Where 'kP is the received power at the primary receiver from the kth cognitive radio, dk is

the distance between the kth cognitive radio and the primary and Pk is the transmit power at the kth cognitive radio. For N number of primary users present in a cell area the cell capacity in bits/sec/Hz/cell is calculated from the Shannon’s theorem considering the SIR as

23

2

1

log (1 ( ) )N

ji

C SIR i=

= +∑ ( 3.10 )

where, C = Capacity (bits/sec/Hz/cell)

SIR(i)j = Signal to interference ratio of N users in j cell

3.6 Simulation Model

The simplified simulation model for this work is shown in Figure 3.2. Each block in the simulation model is described as follows. For the simulation randomly distributed SUs are generated for a cell area with a PU transmitter. The received signal for each SU is calculated to detect the transmission of the PU individually and final detection decision is taken by the access point taking into consideration of the individual detection. If PU is detected the SU does not transmit into the frequency band and if PU is not detected the SU transmits in that band. In this cooperative environment, the interference to the PU is occurred when the SU does not detect the PU signal, when the PU is actually present, which may be due to the PU’s signal attenuation and added noise. For this a tolerance limit of 10% is set such that if the received signal level lies between ±10% of the transmitted signal of the PU, it is still considered to be the PU signal. The SIR and the capacity of the PU for this cooperative scenario are then calculated.

For the simulation of cooperative scenario with malicious nodes, the SUs who always reports the absence of PU i.e. ‘Always No’ nodes as described in section 3.4, are considered. When such malicious nodes are present the primary user will get interfered because the transmission of SU will not stop. The interference created due to such malicious SUs is then calculated. This is followed by the calculation of the SIR and the capacity of PU. In order to improve the degraded capacity of the PU, a power control scheme is applied to the PU. The PU is allowed to increase its power level only when it does not cause interference to the neighboring PUs. The interference level and SIR is again calculated and compared with the simple cooperative system without malicious users. The PU satisfying the SIR condition is allowed to transmit with the increased power level. The simulation is run for 5000 times for the users lying in an area of 1 square km with a power increment of 1 dB. Initially all the transmission are assumed to be equal and fixed to 30 dBm.

Figure 3.2 Simulation Model

24

The simplified flow diagram for the simulation of the performance of primary users is as shown in Figure 3.3. The capacity calculation portion is elaborated in Figure B.1 in Appendix B. The detection algorithm for the cooperative spectrum sensing with and without malicious secondary users is shown in detail in Figure 3.4.

Figure 3.3 Flow Diagram of Simulation Model

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Figure 3.4 Flow Diagram of Cooperative Spectrum Detection Technique

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3.7 Simulation Parameters

The simulation parameters used for the performance analysis of PU in the cooperative sensing technique is listed in the table below.

Table 3.1 Simulation parameters for performance analysis of primary user (PU) in a cognitive radio environment

No. of Simulation 10000 No. of PU transmitter 1

No. of PU receiver 10 Carrier Frequency (Mishra et al., 2006) 800 MHz

PU Transmit Power 30dBm

Mean received SNR of SU -10 dB

SU Transmit Power 10dBm, 20dBm, 30dBm

Antenna Gain (3GPP TR 25.942, 2007) 0 dB Area of the cell 1 km

Propagation Model Path loss Model with distance

PL(d)[dB] = 20 log(d) + 30.5

Log normal shadowing standard deviation

4 dB

Path loss exponent 2

No. of SU 50 Threshold for energy detector (dB) -1, -0.8, -0.6, -0.4, -0.2, 0, 0.2, 0.4,

0.6, 0.8, 1 Sensing time interval, T 50 (sec)

With these input parameters the simulation is carried out for different cases that will be described in the following chapters.

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CHAPTER 4

SIMULATION RESULTS AND DISCUSSION

The cooperative sensing system with and without the malicious secondary users have significant effects in the primary user system. This chapter basically presents the simulation results taking into consideration the cases of the presence and absence of malicious users as described in chapter 3. The simulations are done in MATLAB simulator on the basis of the parameters given by (Kaligineedi et al., 2008) for the validation purpose. After the validation, the results for calculation of capacity of the primary user with and without malicious SUs are presented. The results of the implementation of power control are also presented in this chapter.

4.1 Cooperative Sensing Technique

Spectrum detection is the most important task of CR system. When the signal undergoes deep fade, the individual detection scheme might not correctly detect the primary user. Thus, cooperative detection provides an efficient and robust detection technique. However, the reconfigurability and software basis of CRs make it prone to malicious behavior. The performance of cooperative sensing method will be severely degraded because of the malicious users. The results of such cooperative scenario are presented. � At first the results for simple cooperative sensing scenario without any malicious users

is presented. � This is followed by the results for the simulation of cooperative sensing environment

when malicious cognitive users are present. 4.1.1 User Cooperation without Malicious Nodes

The performance of cognitive users in cooperation without any malicious nodes is studied and simulated taking the parameters given by (Kaligineedi et al., 2008) as listed in table 3.1. The probability of detection and the probability of false alarm were calculated with a varying threshold for 10000 simulations. The obtained result is shown in Figure 4.1 and Figure 4.2. The simulated result is similar to the results presented by (Kaligineedi et al., 2008). The difference in the results could be due to the parameters, which are not given in the paper and are assumed.

The probability of detection and probability of false alarm is observed for the varying threshold level. It can be seen from Figure 4.1 and Figure 4.2 and also is quite obvious that as the threshold is low there is high probability of detecting the signal along with higher probability of false alarm. As the threshold is increased the probability of detection is decreased also decreasing the probability of false alarm. The nature of probability of false alarm is as shown in Figure 4.2.

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Figure 4.1 Probability of detection without malicious nodes

Figure 4.2 Probability of false alarm without malicious nodes 4.1.2 Cooperation with Malicious Nodes

The presence of malicious nodes definitely has a prominent effect on the performance of cooperative sensing technique. As described in chapter 3 cognitive radios can act maliciously by reporting the wrong information of the presence of PU. The ‘Always Yes’ or ‘Always No’ reporting cognitive radios are considered which are detected by the difference in their energy values. The ‘Always Yes’ users which gives value that is twice of the threshold and the ‘Always No’ users which gives value that is half of the threshold

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on a linear scale are identified by calculating the upper and lower bound of the energy values as given in equations (3.7). Any energy value that is not lying in the range of the upper and lower limits are considered to be malicious and they are not used in the decision making process. For this thesis work the ‘Always Yes’ users is considered to give the energy value that is twice of the threshold and the ‘Always No’ users is considered to give the energy value that is half of the threshold on a linear scale. Taking 10% ‘Always Yes’ users as in (Kaligineedi et al., 2008) we see that the probability of detection as well as probability of false alarm increases than that without malicious users. The simulated result also shows the increase in the respective probabilities as shown in Figure 4.3 and Figure 4.4. The probability of detection never becomes zero in such case though it reduces as the threshold is increased.

Figure 4.3 Probability of detection with 10% Always Yes nodes

Figure 4.4 Probability of false alarm with 10% Always Yes nodes

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However, the presence of ‘Always No’ users, who always reports the absence of PU, decreases both the probabilities. The result for probability of detection and probability of false alarm for 10% ‘Always No’ malicious users is shown in Figure 4.5 and Figure 4.6. The probability of detection with the malicious nodes decreases drastically for a threshold value of 0.1 dB. At this threshold level the probability is highest with a value of 1 when no malicious nodes are present.

Figure 4.5 Probability of detection with 10% Always No nodes

Figure 4.6 Probability of false alarm with 10% Always No nodes

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When the malicious nodes are identified the energy values provided by such users are neglected in the final decision making process. This gives a reliable final decision since the malicious users are already removed in the decision. The simulation results for identifying the malicious nodes and omitting its values in the final decision is shown in Figure A.3 and Figure A.4 in Appendix A. It is clear from the results that by applying this technique the probability of detection as well as the probability of false alarm becomes close to that of a simple cooperative system without malicious nodes.

The results presented above are taken for larger number of secondary users in cooperation. When the number of cooperating secondary users decreases, the probability of correctly detecting the primary user also decreases. The simulation result when only 10 SUs cooperate to detect the PU is given in Figure 4.7. It can be seen from the figure that even though no malicious SUs are present, the detection probability decreases with the decrease in the number of cooperating users. These phenomena can be observed in Figure 4.7. As the detection probability decreases there is much chance that the PUs is interfered which is dealt in the later sections.

Figure 4.7 Probability of detection without malicious nodes for different number of secondary users (SUs)

The probability reduces more in the presence of malicious secondary nodes. Taking 10% of ‘Always No’ nodes out of 10 SUs, it can be observed that the probability of detection decreases more than in the presence of 50 SUs. As observed from Figure 4.8 the probability starts decreasing from the 0 dB threshold value. However, for 50 SUs the probability of detection is decreased for a threshold of -0.5 dB.

Also, taking 10% ‘Always Yes’ nodes out of 10 SUs, the probability of detection still decreases as the number of total cooperating users decreases. However, for this case as 10% users are always detecting the primary users the probability of detection never becomes zero. This can be seen from Figure 4.9 that the probability tries to stabilize when the threshold is 0.7 dB.

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Figure 4.8 Probability of detection with ‘Always No’ malicious nodes for different number of secondary users (SUs)

Figure 4.9 Probability of detection with ‘Always Yes’ malicious nodes for different number of secondary users (SUs)

4.2 Maximum Capacity of PU with and without Malicious Users

As analyzed from section 4.1 the probability of detection as well as the probability of the false alarm is greatly affected due to the presence of malicious users. The overall effect can be seen from Figure A.1 and Figure A.2 in Appendix A. As the probability of detection decreases there is a greater chance of causing interference to the PU. With the view of fulfilling the objective to study the performance of PU in the presence and absence of malicious secondary users, the capacity of the primary user is then calculated. The

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simulation carried out for 5000 simulations shows gradual decrease of capacity for varying threshold value. As the threshold for the energy detection increases, the detection probability decreases leading to a higher possibility of interference which further decreases the capacity.

Figure 4.10 shows the capacity variation of the PU when the transmit power of the cognitive user is 30 dBm, which is equal to the transmit power of the PU. As can be seen from Figure 4.10 the capacity is highest when the threshold is lowest i.e. for a threshold of -1 dB the maximum capacity of 13 bits/sec/Hz/cell is attained by the PU. This is because at lower threshold the probability of detection is also maximum leading to less interference. As the threshold goes on increasing the detection probability decreases due to increasing interference value. As the interference gets higher the SIR becomes lower which further decreases the capacity. In the presence of malicious users the interference becomes more severe.

Taking 10% of the SUs to be malicious that are always reporting the absence of PU i.e. ‘Always No’ nodes as in (Kaligineedi et al., 2008), it is seen that the capacity gradually decreases for higher threshold values because the energy received by the SUs will be lower than the threshold. It can be seen from Figure 4.10 that the capacity gets more degraded in the presence of malicious users. Due to the presence of malicious users the capacity decreases to a value of 11.4 bits/sec/Hz/cell at the lowest threshold of -1 dB leading to approximately 12% decrease than that without the malicious users. As the threshold increases, the total energy of the signal received by the SUs becomes lower than the threshold which results in a lower detection probability and further higher interference. The capacity attains its lowest value of 2 bits/sec/Hz/cell at the highest threshold value of 1 dB without malicious users. The presence of malicious users leads to reduction in capacity to 1.8 bits/sec/Hz/cell. This shows that only 0.2 bits/sec/Hz/cell decrease in the capacity value at the highest threshold level. This decrement is because as the threshold level is increased the detection probability is also decreased for both the cases and hence the capacity attains its lower value.

Figure 4.10 Capacity of primary user (PU) with and without malicious users taking PU’s transmit power (TP) =30 dBm and SU’s TP=30 dBm for 50 cooperating SUs

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When the transmission power of the SU is lower than the transmission power of primary user the capacity still decreases which is shown in Figure 4.11. For this the simulation is again carried out for 5000 times taking the same 30 dBm power level for PU and reduced power level of 20 dBm for SU. The capacity still decreases with the increasing threshold value in the presence of malicious SUs. This implies that even if the transmission power of the cognitive radio is less than the PU, the capacity significantly decreases whenever malicious users are present. The capacity is further checked taking SUs TP of 10 dBm which is shown in Figure A.5 in Appendix A. This further shows the decrease in capacity. However, for the lower threshold the capacity with malicious users becomes closer to that without malicious users and then it decreases. As the power of SU is decreased the capacity of PU gets better however, as we go on decreasing the power of the cognitive users, its performance will be degraded because of the decrease in the capacity.

Figure 4.11 Capacity of primary user (PU) with and without malicious users taking PU’s transmit power (TP) =30 dBm and SU’s TP=20 dBm for 50 cooperating SUs

The capacity of the primary user in the presence and absence of malicious secondary users is also simulated considering different number of primary users. The result shows that as the number of users varies from 1 to 10 the capacity of the primary system increases to its maximum value of 13 bits/sec/Hz/cell as shown in Figure 4.12. However, when there are 10% malicious cognitive users the capacity decreases to 11.41 bits/sec/Hz/cell which can be seen from Figure 4.12. Here, in the simulation, the TP of primary users and secondary users are both assumed to be equal to 30 dBm.

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Figure 4.12 Capacity of primary user (PU) with and without malicious users taking PU’s transmit power (TP) =30 dBm and SU’s TP=30 dBm for 50 cooperating SUs

and 10 PUs

4.3 Performance of PU with Power Control

As seen from section 4.2 the capacity of primary users gets severely degraded due to the presence of malicious secondary users. It is also seen that when the transmit power for the secondary users is lowered the capacity of the primary user gets better but only in a less amount. This further leads to a degraded performance for the secondary user itself. As the focus of this thesis work is to study the performance of PU the performance degradation for secondary users is not shown. The degraded capacity of PU due to the presence of malicious users can be raised by applying power control algorithm. However, the increased power should not cause interference to other existing primary users. Thus, in this section the results for power scaling with small increment are presented. The transmit power of the PU is increased at a step size of 1 dB (keeping the previous power of 30 dBm fixed). The result shows that this small scale power increment can lead to significant increase in the PU system capacity maintaining the interference level.

Figure 4.13 and Figure 4.14 shows the performance improvement of the primary user in terms of capacity with the small scale of 1 dB increase in its power level. This increase in power level increases the interference level as well. Hence, the power level can be increased only if the interference level to the adjacent primary system is maintained. The results show that for 1 dB increase the interference level is still maintained while obtaining the capacity gain of 4% when no malicious users are present and a gain of 5% when malicious users are present for 10 primary users. The SIR value for this case is shown in Table 4.1. The SIR is maintained for this case as the simple cooperative environment without malicious users even with the increase with the power level. Thus, further increase in the power level is carried out. The simulation is again run when the power level is increased by 2 dB i.e. PU’s transmit power is 32 dBm and SU’s power level is fixed to 30

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dBm. The capacity gain is shown in Figure 4.15 and Figure 4.16 and the SIR value for the 2 dB increment is tabulated in Table 4.2.

Figure 4.13 Capacity of primary user (PU) without malicious users applying power control with 1 dB step size for 50 cooperating SUs and 10 PUs

Figure 4.14 Capacity of primary user (PU) with malicious users applying power control with 1 dB step size for 50 cooperating SUs and 10 PUs

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Table 4.1 Simulated SIR of neighboring primary user (PU) after power control considering malicious and no malicious case (for step size of 1 dB)

Without power control With power control

SIR for simple cooperation without malicious users (dB)

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SIR with malicious users (dB)

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20 15.21 20.10 15.17 16.98 15.20 17.02 15.15

Figure 4.15 Capacity of primary user (PU) without malicious users applying power control with 2 dB step size for 50 cooperating SUs and 10 PUs

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Figure 4.16 Capacity of primary user (PU) with malicious users applying power control with 2 dB step size for 50 cooperating SUs and 10 PUs

Table 4.2 Simulated SIR of neighboring primary user (PU) after power control considering malicious and no malicious case (for step size of 2 dB)

Without power control With power control

SIR for simple cooperation without malicious users (dB)

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20 15.21 19.95 15.39 16.98 15.20 16.93 15.0

As seen from the results in Figure 4.15 and Figure 4.16, for 2 dB step size the capacity of the primary user is increased by 11-12% in the presence and absence of malicious users maintaining the SIR condition shown in Table 4.2. However, more results can be obtained

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with step size of 3 dB and more which further shows the increase in the system capacity. This result can be referred to Figure A.6 and Figure A.7 in Appendix A. The SIR value is also tabulated in Table A.1 in Appendix A. These data show that increasing the power level, the capacity gain can be achieved but this also increases the interference to the neighboring primary users. The results from the Appendix show that for a step size of 3 dB the SIR starts getting degraded which becomes more severe for the step size of 4 dB. Thus, we can only increase the power level by a small amount. Even this small increase in power level can bring a significant improvement in the system capacity for the primary users even in the presence of malicious users. Thus, power control technique can be applied to improve the system capacity of the primary user when the secondary users behave maliciously. However, the SIR constraint should be maintained.

4.4 Protection Distance for PU

As seen from section 4.1 the capacity of the primary system is affected by the interference caused by the secondary system. Thus, a protection distance for the primary user can be set by analyzing the secondary user’s position and the corresponding interference that cause the SIR reduction of the primary user. Within this distance the SUs are not allowed to transmit which provides a better security for primary user’s transmission. The simulation is again run taking the transmit power of both the PU and SU to be 30 dBm to obtain the minimum protection distance which can be seen in Figure 4.17. As shown in the figure the protection distance varies as the threshold for the detection is changed. For the lower threshold the cognitive users have higher probability of detection and thus, a lesser chance of interfering with the primary user. In this case a lower protection distance is enough and within this region secondary users are not allowed to transmit. As it can be seen from Figure 4.17 the minimum distance of no transmission range is only 70 meters when the threshold is lower and around 140 meters when the threshold is higher.

Figure 4.17 Protection distance for primary user (PU) with and without malicious users taking 50 SUs

When the number of cooperating SUs is varied there is a significant change in the results. As the number of SUs is decreased the probability of detection is also decreased which is already observed in Figure 4.7 even when there is no malicious nodes. This requires a

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larger protection distance for the PU in order the interference from the SUs is minimized. The simulation is again run taking 10 numbers of SUs. From the results shown in Figure 4.18 we can observe that the protection distance needs to be increased by approximately double the value for the increasing threshold set.

Figure 4.18 Protection distance for primary user (PU) with and without malicious users taking 10 SUs

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CHAPTER 5

CONCLUSION AND RECOMMENDATIONS

5.1 Conclusion

In this thesis work, we studied the impact of malicious nodes in the performance of primary user in a cognitive radio environment. The probability of detection decreases as the threshold is varied when ‘Always No’ nodes are present. The analysis shows that the detection probability at 0.4 dB threshold tends to zero while it is still higher with probability above 0.8 when no such malicious users are present. This definitely leads to interference to the primary user degrading its capacity. As seen from the results, the capacity of the primary system is decreased by 12-13% because of such malicious users when both the primary and secondary are transmitting at the same power level taken to be 30 dBm. Power control technique based on signal to interference ratio is one of the popular and simple techniques to enhance the degraded performance of a system. Thus, power control technique is applied to the primary user system to enhance its capacity. However, the power increment should be such that the interference to neighboring primary users should be minimized.

The results show that there is a significant increase in the capacity of primary user when fixed step power control technique is applied while maintaining the interference level to the neighboring primary users. The results show that the system capacity is increased by approximately 4-5% for 1 dB increment and further it is increased by 10-11% for a 2 dB increase still maintaining the SIR condition. However, when the step size reaches 4 dB the interference to the adjacent primary users becomes higher causing reduction in the SIR. Thus, power control algorithm with a fixed step size has to be implemented considering the SIR in order to increase the primary users’ capacity even in the presence of malicious cognitive users.

The primary user’s transmission can also be secured by limiting the transmission boundary for cognitive radios. A protection region can be set for the primary user so that secondary users are not allowed to transmit within that region. It can be seen from the obtained results that the protection region is dependent upon the probability of detection which further depends upon the threshold set. When the detection threshold is lower there is a high probability of detection which leads to lower chance of interference. When the threshold is higher the probability of detecting the primary user becomes lower and so a higher chance of interference. At the lowest threshold level of -1 dB the protection distance is only 70 meters whereas this increases to 134 meters at the highest threshold level of 1 dB. The protection distance further increases when the number of cooperating secondary users is less. When there are only 10 cooperating SUs the protection distance required is raised up to 153 meters for the threshold of -1 dB. This increases to 300 meters for 1 dB threshold. Thus, the results show that approximately double the distance is required for securing the primary user as the threshold is varied for both the cases i.e. when malicious users are absent and present.

5.2 Recommendations

The following recommendations are provided for future improvements and modifications which are made on the basis of this thesis work:

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� The main focus of this thesis work is to study the impact of malicious users on the primary users which are the high priority users, so the impact on secondary users is not considered. However, the secondary users will also be equally affected due to the presence of such malicious nodes. Thus, further study can also be done in this regard to maintain the QoS for the secondary user’s communication.

� The channel model is assumed here to be log-normal shadowing which can also be modeled with fading.

� Multiple-cell scenario considering the intra-cell interference can be implemented in the future.

� To make the system robust and reliable even in the presence of malicious users, the primary system should incorporate some technique to nullify the interfering signal. Study can be made on smart antennas and antenna beam steering technique can be implemented in the primary system to nullify the interfering signal.

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REFERENCES

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46

APPENDIX A

FURTHER RESULTS FOR CAPACITY ANALYSIS OF PRIMARY US ER

A.1 Cooperative Sensing With and Without Malicious Users

The overall probability of detection and probability of false alarm are observed to be affected due to the presence of malicious users. The presence of ‘Always Yes’ nodes increase both the probabilities whereas the presence of ‘Always No’ nodes decreases both the probabilities. These summarized results can be shown in Figure A.1 and Figure A.2.

Figure A.1 Probability of detection with and without malicious nodes for 50

cooperating SUs

Figure A.2 Probability of false alarm with and without malicious nodes for 50

cooperating SUs

A.2 Cooperative Sensing with Malicious Nodes Detection Schemes

As described in chapter 3 the malicious cognitive radios are detected by calculating the difference in their energy values with predetermined lower and upper values of the signal energy. Any energy value that is not lying in the range of the upper and lower limits are considered to be malicious and they are not used in the decision making process. The ‘Always Yes’ users gives the value that is twice of the threshold and the ‘Always No’ users gives the value that is half of the threshold on a linear scale. When these energy values for malicious nodes are determined they are omitted in further calculations. In this thesis work 10% of the users are considered to be malicious which are identified using the malicious node detection scheme. It can be seen that by the removal of those outlying energy values of malicious users the probability of detection and the probability of false alarm becomes close to that of a simple cooperative system without malicious nodes. The results can be seen as shown in Figure A.3 and Figure A.4 respectively.

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Figure A.3 Probability of detection with 10% Always Yes nodes using malicious node detection scheme

Figure A.4 Probability of false alarm with 10% Always Yes nodes using malicious node detection scheme

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Probability of detection (Pd) with 10% Always Yes users using malicious node detection scheme

Pd with 10% always no nodes with malicious node detection(Kaligineedi et. al.)Pd with 10% always no nodes with malicious node detection (Simulated)

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48

A.3 Performance of PU With and Without Malicious Users

The capacity of the primary user is also studied when the power level of secondary users is made lower. As described in chapter 4 the capacity of the primary user degrades in the presence of the malicious user. Even when the power of the SUs is made lower the capacity tends to decrease. Figure below shows the further decrease in the capacity of primary user when the SU’s transmit power is lowered to 10 dBm. However, making the cognitive radio’s power level low also decreases its capacity.

Figure A.5 Capacity of primary user (PU) with and without malicious users taking PU’s transmit power, TP=30 dBm and SU’s TP=10 dBm for 50 cooperating SUs

A.4 Performance of Primary User with Power Control

In order to enhance the primary users’ performance power control is incorporated. A fixed-step power increment algorithm is applied. The results of power increment up to 2 dB are shown in chapter 4. Figure A.6 and Figure A.7 shows the capacity enhancement due to power increment of 3 dB. It is clear from these figures that the capacity can be regained by implementing the power control algorithm. However, the power cannot be increased much because of the interference created to the neighboring users. The SIR of the users should be maintained. The SIR value for the 3 dB increment is given in Table A.1 which shows a reduction in the SIR value. The results for 4 dB increment makes the scenario clear that even if the capacity is gained the SIR to the neighboring users is reduced greatly and thus there should be a limiting value of increasing the power. The results of 4 dB power increment are also shown in Figure A.8 and Figure A.9 and the SIR value to the corresponding neighboring primary users is tabulated in Table A.2.

0123456789

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capacity without malicious users

capacity with malicious users

49

Figure A.6 Capacity of primary user (PU) without malicious users applying power control with step size of 3 dB for 50 cooperating SUs and 10 PUs

Figure A.7 Capacity of primary user (PU) with malicious users applying power control with step size of 3 dB for 50 cooperating SUs and 10 PUs

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Table A.1 Simulated SIR of neighboring primary user (PU) after power control considering malicious and no malicious case (for step size of 3 dB)

Without power control With power control

SIR for simple cooperation without malicious users (dB)

SIR with malicious users (dB)

SIR for simple cooperation without malicious users (dB)

SIR with malicious users (dB)

21.20 17.42 19.62 15.55 27.23 21.45 24.34 18.58 22.23 16.45 19.65 15.58 26.13 20.35 24.7 18.48 22.95 17.17 18.31 16.30 21.08 15.30 20.51 14.44 18.24 14.46 15.66 13.59 21.02 16.24 18.45 14.37

20 15.21 19.42 13.34 16.98 15.20 16.40 13.33

Figure A.8 Capacity of primary user (PU) without malicious users applying power control with step size of 4 dB for 50 cooperating SUs and 10 PUs

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51

Figure A.9 Capacity of primary user (PU) with malicious users applying power control with step size of 4 dB for 50 cooperating SUs and 10 PUs

Table A.2 Simulated SIR of neighboring primary user (PU) after power control

considering malicious and no malicious case (for step size of 4 dB)

Without power control With power control

SIR for simple cooperation without malicious users (dB)

SIR with malicious users (dB)

SIR for simple cooperation without malicious users (dB)

SIR with malicious users (dB)

21.20 17.42 15.62 12.55 27.23 21.45 20.34 13.58 22.23 16.45 17.65 10.58 26.13 20.35 19.7 13.48 22.95 17.17 16.31 10.3 21.08 15.30 16.51 10.59 18.24 14.46 12.66 8.14 21.02 16.24 16.45 11.37

20 15.21 15.42 9.34 16.98 15.20 16.4 9.33

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52

APPENDIX B

CAPACITY CALCULATION

B.1 Capacity Calculation of PU

The elaborated flow diagram for the calculation of the capacity of the primary user in the simulation is given in Figure B.1.

Figure B.1 Flow diagram for calculation of PU capacity