Bth 2013 atti

Spectrum Sensing Techniques

for 2-hop Cooperative

Cognitive Radio Networks:

Comparative Analysis

Atti Ur Rehman

Muhammad Asif

This thesis is presented as part of Degree of

Master of Science in Electrical Engineering

Blekinge Institute of Technology

September 2012

Blekinge Institute of Technology

School of Engineering

Department of Electrical Engineering

Supervisor: Maria Erman

Examiner: Dr. Sven Johansson

Spectrum Sensing Techniques for Cooperative Cognitive Radio Networks: Comparative Analysis

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Abstract

Spectrum sensing is an important aspect of cognitive radio systems. In order to efficiently utilize the

spectrum, the role of spectrum sensing is essential in cognitive radio networks. The transmitter

detection based techniques: energy detection, cyclostationary feature detection, and matched filter

detection, is most commonly used for the spectrum sensing. The Energy detection technique is

implemented in the 2-hop cooperative cognitive radio network in which Orthogonal Space Time

Block Coding (OSTBC) is applied with the Decode and Forward (DF) protocol at the cognitive relays.

The Energy detection technique is simplest and gives good results at the higher Signal to Noise Ratio

(SNR) values. However, at the low SNR values its performance degrades. Moreover, each transmitter

detection technique has a SNR threshold, below which it fails to work robustly. This thesis aims to

find the most reliable and accurate spectrum sensing technique in the 2-hop cooperative cognitive

radio network. Using Matlab simulations, a comparative analysis of three transmitter detection

techniques has been made in terms of higher probability of detection. In order to remove the

shortcomings faced by all the three techniques, the Fuzzy-combined logic sensing approach is also

implemented and compared with transmitter detection techniques.


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Acknowledgements

First of all, we would like to express our deepest gratitude and respect to our supervisor, Maria

Erman for her enthusiastic guidance and support to complete this thesis. We are also thankful and

appreciate the contributions of examiner Dr. Sven Johansson.

We are thankful to the faculty of Blekinge Institute of Technology. They delivered us the quality

education and helped us to improve the skills.

We are thankful to our parents and family for their love, and support throughout the education

career.

Atti Ur Rehman

Muhammad Asif

2012, Sweden


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Table of Contents

Chapter 1: Introduction and Background ....................................................................................... 11

1. Introduction .......................................................................................................................................................... 11

1.1 Background ......................................................................................................................................................... 12

1.1.1 Motivation .............................................................................................................................................. 14

1.2 Objectives ............................................................................................................................................................ 15

Chapter 2: Cognitive Radio and Cooperative MIMO Communication ......................................... 17

2.1 Cognitive Radio (CR) ........................................................................................................................................ 17

2.3 Cooperative MIMO Communication ............................................................................................................. 18

2.3.1 Dual hop Cooperative Communication............................................................................................. 19

2.4 Cooperative Diversity Relaying Protocols ..................................................................................................... 20

2.4.1 Amplify-Forward (AF).......................................................................................................................... 20

2.4.2 Decode-Forward (DF) .......................................................................................................................... 21

2.4.3 Decode Amplify and Forward (DAF) ................................................................................................ 22

2.4.4 Compress Forward (CF)....................................................................................................................... 22

Chapter 3: Cooperative Spectrum Sensing .................................................................................... 23

3.1 Introduction ........................................................................................................................................................ 23

3.2 Spectrum Sensing ............................................................................................................................................... 23

3.2.1 Cooperative Spectrum Sensing ............................................................................................................ 24

3.3 Spectrum Sensing Techniques ......................................................................................................................... 25

3.3.1 Matched Filter Detection ..................................................................................................................... 26

3.3.2 Energy Detection................................................................................................................................... 27

3.3.3 Cyclostationary Feature Detection ..................................................................................................... 28

3.4 Cooperative Models ........................................................................................................................................... 29

3.4.1 Soft Cooperation ................................................................................................................................... 29

3.4.2 Hard Cooperation.................................................................................................................................. 30

Chapter 4: System Model and Simulation Environment .............................................................. 33

4.1 Introduction ....................................................................................................................................................... 33

4.2 Alamouti Space Time Block Coding (STBCs) Schemes…………………………………....33

4.3 The Simulation Modeling………………………………………………………………….35

4.4 Implementation of the Energy Detection Method at the CC……………………………....39

4.5 Implementation of the Cyclostationary Feature Detection Method at the CC……………..41


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4.6 Implementation of the Matched Filter Detection Method at the CC……………………………….43

Chapter5: Results and Analysis…………………………………………………………………...……45

5.1 Simulation Results and Analysis…………………………………………………………………….45

5.1.1 Probability of Primary Detection…………………………………………………………….48

5.1.2 Receiver Operating Characteristic (ROC) Curves……………………………………………49

5.1.3 Probability of Detection vs Lamda…………………………………………………………..50

Chapter 6: Performance Evaluation of Fuzzy Combined Logic Spectrum Sensing approach....................53

6.1 Introduction……………………………………………………………………………………….53

6.2 Fuzzy Combined Logic sensing approach .................................................................................................... 55

Chapter 7: Conclusion and future work ........................................................................................ .58

Conclusion ................................................................................................................................................................. .58

Bibliography .............................................................................................................................................................. .59


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List of Abbreviations

OSA = Opportunistic Spectrum Access

CR = Cognitive Radio

ED = Energy Detection

MFD = Matched Filter Detection

CFD = Cyclostationary Feature Detection

SNR = Signal to Noise Ratio

CRN = Cognitive Radio Networks

CRs = Cognitive Relays

PU = Primary User

SU = Secondary User

CC = Cognitive Controller

MIMO = Multiple Inputs and Multiple Outputs

DF = Decode Forward

AF = Amplify Forward

DAF = Decode-Amplify Forward

SBC = Space Block Coding

MRC = Maximum Ratio Combining

ML = Maximum Likelihood

MRC = Maximum Ratio Combining


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Chapter 1 Introduction and Background

1 Introduction

With the increasing demand in higher data rates, there is a shortage of radio spectrum for the

upcoming modern technologies in wireless communication. The spectrum management policies are

responsible for the scarcity of the spectrum. Opportunistic Spectrum Access (OSA) is a new

approach to deal with the shortage of the spectrum problem. Cognitive Radio (CR) is an important

component of the OSA; it can significantly improve the efficient utilization of the radio

electromagnetic spectrum. Cognitive radio (CR) is an intelligent wireless communication technology,

with the following characteristics: it uses different techniques to become aware of the

surroundings, have abilities to learn from the outer environment and changes the parameters

transmitted data and the receiver to achieve the goal of effective communication without any

interference between the primary user and the secondary user [2,3]. Spectrum sensing is an

essential function of the CR. The major challenge for cognitive radio systems is to avoid interference

with the licensed user. For this, it is necessary for the CR to make faster, and more accurate and

reliable sensing of the Primary user. Cooperative spectrum sensing is used to achieve sensing

“reliability”, but this introduces a cooperative overhead. This problem which can be solved by

improving the spectrum sensing efficiency and the role of spectrum-sensing techniques is very vital.

The transmitter detection based techniques, Energy Detection (ED), Cyclostationary Feature

Detection (CFD), and Matched Filter Detection (MFD), are widely used for spectrum sensing. It has

been seen that each transmitter detection technique has a Signal to Noise Ratio (SNR) threshold,

below which these techniques fail to work robustly. The Energy detection technique is implemented

in [2], is the simplest and most efficient at higher SNR, but it cannot differentiate between the PU

and the secondary user signal at lower SNR. To make spectrum sensing more reliable it is necessary

to find which detection technique gives a more reliable and accurate decision under conditions of

low SNR. This thesis aims to find the most reliable and accurate spectrum sensing technique under

the conditions of Low SNR for the Cooperative Cognitive Radio Networks (CRN).


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To come up with this, Performance comparison of three transmitter detection techniques, Energy

Detection (ED), Cyclostationary Feature Detection (CFD), and Matched Filter (MFD) detection is

performed for the dual-hop Cooperative cognitive radio networks.

Where the CR user receives the Primary User signal and then transmits the received signal towards

the Cognitive Controller (CC). At the cognitive controller, the final decision about the PU presence

or absence is made by implementing all three detection techniques in parallel.

The Fuzzy-combined logic approach is implemented at the CC, to make the decision more reliable,

and efficient. In order to find which spectrum sensing technique is more robust at low SNR values

for the 2-hop Cooperative cognitive radio network, simulations and results validation is carried out

by using MATLAB.

The next sections of chapter one provides the background and related work. Chapter two briefly

explains about the cognitive radios networks, and cooperative Multiple Input and multiple output

(MIMO) communication. Chapter three is related to the spectrum sensing and different sensing

techniques. Chapter four is about the 2-hop Cooperative CRN system model. In chapter five the

comparisons between different techniques has been made with simulation results. The Fuzzy logic

sensing approach is discussed in chapter six. Finally, the conclusion is drawn in chapter seven, and

the future work is also suggested in chapter seven.

1.1 Background

This section explains the problem definition, motivation and related work for this thesis. The

Orthogonal Space Time Block Coding (OSTBC) is implemented in the Cooperative cognitive radio

networks by using dual-hop Decode and Forward (DF) relaying protocols in [2], and Amplify and

Forward (AF) in [3]. The Primary User (PU) signal is transmitted by using OSTBC; the signal reaches

the destination by following the two paths, by the direct and the indirect path. Through the indirect

path, the signal is first received at relays in the first hop and then in the second hop, the DF protocol

forwards the decoded data towards the Cognitive Controller (CC) [2].


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The 2-hop cooperative cognitive network model is shown in Figure 1.1 [2, 3].

Figure 1.1- Dual-hop Cooperative Cognitive radio network.

The Cognitive controller is used to make the final decision about the presence or absence of the PU

signal, and gives feedback to relays (secondary users) [2,3]. The energy detection method is

implemented at the CC for the detection of the PU spectrum. This technique is the simplest and

gives good performance at high SNR, but at low SNR its performance degrades and it cannot

differentiate between the PU and the SU signals [4,5]. The performance of energy detection

technique is a question mark at low SNR, as the results shown in Figure 1.2 indicates, i.e. at low SNR,

the performance of the energy detector degrades.


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Figure 1.2- Performance of the energy detection technique in the 2-hop cooperative

Cognitive Radio network.

1.1.1 Motivation

Taking the above problem into consideration, the performance analysis of different transmitter

detection techniques is being made. The detection performance of the matched filter detection and

the cyclostationary feature detection under the cooperative cognitive radio network has been

missed and their performance caliber has been not tested before.

Therefore, the motivation of this thesis can be stated in one sentence:

To find the most reliable and efficient spectrum sensing technique under the conditions of Lower

SNR for the Cooperative CRN by using the 2-hop DF relaying protocol.

The hypothetical question designed to further our research is as follows:

Which Transmitter detection- based spectrum sensing technique is more efficient in terms of higher

probability of detection at the low SNR in the 2-hop cooperative cognitive radio network?

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Pro

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Pd

)Probability of Detection vs SNR: Performance Analysis over 2-hop CRN

Energy Detection

-0.2

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0.8


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Many spectrum-sensing algorithms have been proposed for spectrum sensing. The three most

common transmitter detection techniques, ED, CFD, and MFD, are used for the spectrum sensing in

[6,7]. These three primary detection techniques are compared under the conditions of low SNR, and

it is concluded that, the CFD is most suitable under low SNR in [5],[7],[8]. To improve the local

spectrum sensing, two stages of spectrum sensing has been implemented, where the first stage,

coarse sensing is implemented by using energy detection and the second stage called fine sensing is

performed with the cyclostationary feature detection technique [9]. The Fuzzy logic based spectrum

sensing approach for local spectrum sensing gives better results at low SNR as compared with all the

transmitter detection techniques in [10]. The Fuzzy decision scheme for the cooperative sensing has

also shown better performance in centralized CRN as compared with local individual decision [11].

1.2 Objectives

The main aims and objectives of this thesis can be stated as follows:

To increase the spectrum sensing performance in the 2-hop cooperative cognitive radio

networks.

To evaluate spectrum sensing algorithms and techniques in terms of performance and

complexity.

To find an efficient spectrum sensing technique under the conditions of low SNR.

To achieve the maximum spatial diversity in cooperative cognitive radio networks by

implementing OSTBC, using the dual-hop DF protocol.


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Chapter 2

Cognitive Radio and Cooperative MIMO Communication

2.1 Cognitive Radio (CR)

“Cognitive radio is a radio of an intelligent wireless communication system that senses and

is aware of its surrounding environment and capable to use or share the spectrum in an

opportunistic manner without interfering the licensed users” [16].

Cognitive Radio (CR) technology is an intelligent wireless communication network with the following

characteristics: It uses different techniques to become aware of the surroundings, have the abilities

to learn from the outer environment and can change the parameters of the transmitted and

received data to achieve the goal of effective communication without interference [1,2]. The most

important function of a CR is spectrum sensing, where the secondary user (unlicensed user) can

utilize the available spectrum of the licensed user (primary user) with the condition that, the SU will

vacant the spectrum for the primary user [3]. For the detection of presence or absence of the

licensed user, two schemes are used: centralized and distributed schemes. In a centralized scheme,

a signal is sent to the common controller which makes the decision and informs the secondary user

[3]. On the other hand in distributed scheme, all the secondary users make their individual decisions

and share them in the neighborhood, so that the unlicensed user is aware of the status [3]. The basic

operation of the CR [12] is, to learn from the surroundings and check the status of the spectrum of

the primary user being used or not. In the spectrum sensing process, first, information, is gathered

from the radio environment, then the cognitive controller analyzes the information to make a

decision about the presence or absence of the primary user [3], as shown in Fig.2.1 [3].


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2.2 Cooperative MIMO Communication

Multiple Input Multiple Output (MIMO) is a wireless technology, in which both the radio

transmitters and receivers have multiple antennas for digital data transmission. Input and output

are the wireless medium through which electromagnetic waves are transmitted. Nowadays, in

wireless communication efficient use of bandwidth, and higher and reliable data rates, are major

problems. It is difficult to obtain reliable and accurate data at the receiver end due to fading,

interference and others factors. The use of multiple antennas at both ends of the wireless link is

helpful to get reliable data. Different copies of the same data transmitted by using multiple

antennas play an important role for the efficient use of bandwidth, and reliable data transmission.

Due to limitations of size, and hardware complexity in wireless devices, it is difficult to employ more

than one antenna on each mobile terminal [13],[2]. Transmit diversity can easily be employed by

using a virtual MIMO systems, through cooperative communication, a single antenna mobile can

gain benefits of the MIMO system [2]. The basic motivation behind cooperative communication is a

virtual MIMO scenario. In cooperative networks, each node assists, and cooperate, with each other

to achieve the transmit the diversity through a virtual MIMO system [2,3].

Spectrum Sensing

Spectrum Decision

Spectrum Analyzing

Radio Enviroment

Figure 2.1- Cognitive radio process.

Tx Rx MIMO

Figure 2.2- Concept of Multiple Input Multiple output (MIMO).


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Space Time Codes (STC) is the coding scheme, conventionally implemented at the virtual MIMO

transmitting antennas. Space Time Trellis Codes (STTC) [13] and Space Time Block codes (STBC)

[14,13] are two types of the STC. STBC is most widely used as an encoding scheme for the

transmitting antennas due to its low complexity, and good diversity gain [2], as compared with

STTC. To overcome fading problems, transmit diversity is used. It can be achieved by transmitting

independent copies of the same data through multiple antennas. In cooperative communication,

multiple transmitting antennas, and relays, assist and cooperate with each other to transmit the

data from the sender to the receiver [3,2].

2.2.1 Dual-hop Cooperative Communication

In a dual-hop communication network, the communication between the sender and the receiver is

completed in 2 hops. In the first hop, the transmitter broadcasts the information into air by using

the virtual MIMO environment towards all the neighboring nodes (Relays); in the second hop, the

relay applies some protocols applications on the data to make a decision about the licensed user

presence or absence [13,3,2]. At the destination, multiple copies of the same data arrive, one from

the direct path and the other from indirect path in the second hop. The receiver combines the

received signal from the source and the relay (the secondary user) by using some receiver diversity

schemes (Maximum Ratio combining, Maximum Gain) to achieve the reliability and spatial diversity.

R1

Rn

R2

DS

Source Destination

Relays

Direct Path

Figure 2.3- Cooperative multi-hop relaying communication.


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The logic behind the dual-hop transmission is Time Division Duplex (TDD) [3],[2]. In time slot one,

the information reaches the relays and the destination, while in the second time slot, after

implementation of relaying protocols, the relays further transmit the data towards the destination

[2]. The receiver extracts the data by using Maximum Ratio combining (MRC) [15] . The process of

the dual-hop communication is shown in Fig. 2.4 [2].

Relay Secondary UserPrimary User

Direct Path

1st hop 2nd hop

Figure 2.4- dual-hop Cooperative communication.

2.3 Cooperative Diversity Relaying Protocols

The basic concept behind the cooperative relaying scheme is that the source (originator) transmits

the information to both the relays and the destination [16]. The received information at the relaying

station, is further processed to achieve reliability and spatial diversity between the source and the

destination [2]. Cooperative relaying schemes can be further subdivided into: Decode Forward (DF)

and Amplify Forward (AF) schemes.

2.3.1 Amplify-Forward (AF)

In the amplify-forward strategy, the relaying station simply amplifies the received information from

the originator, and then further transmits it towards the destination, without decoding

(regenerating) [16,3,2]. If the non-regenerative relays are placed between the source and the

destination, the communication process will certainly be completed in the dual-hop process [16].


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Consider a signal received at the relaying station, and is the received signal at the destination,

then AF just amplify the with β [16,3]. The process of AF is given in Eq 2.1.

, (2.1)

where the additive noise and β is is the AF amplification factor. The coefficients of the β depend

on variance and the Raleigh fading as given in Eq 2.2.

√

| | , (2.2)

where h is the variance and fading coefficient between the originator and the relay and is the

variance.

2.4.2 Decode-Forward (DF)

The relays with the regeneration capability of data, use the Decode and Forward (DF) protocol for

the modeling of a wireless communication system in the fading environment [2]. In the DF strategy,

the relaying station, first decodes the data being transmitting from the source, then further

forwards the data after re-encoding it [3]. The DF protocol decodes the received signal by using the

specific encoding schemes. It is observed that decoding at the relaying station, it takes more time

for the data reach the destination [17].

Fixed-Decode-Forward (FDF) and Adaptive-Decode–Forward (ADF) are further two types of the DF

protocol. In the case of FDF, it is necessary for the relaying protocol to forward the data regardless

of it being well decoded or not, which leads to performance degradation due to decoding errors,

while in the case of ADF [17,2] , the source does not transmit the information to the relay, if the

source-relay link is busy. In such a situation, where the link is busy, the user directly transmits the

information towards the destination, and relay forwards the data in the second time slot or remains

silent [3]. The received signal at the relay can be expressed in Eq 2.4.


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(2.3)

where is the input signal, is the channel coefficient, and is the AWGN. The DF relaying

protocol decodes the PU signal where , , then transmits the towards

the destination [3]. The transmitted signal by the DF relay is denoted by and is given in 2.5:

√

(2.4)

where and are the average transmitted powers from the relay and the source, respectively [3].

2.4.3 Decode-Amplify and Forward (DAF)

The DAF relaying protocol is implemented at the relay [18,8], where it is required that a relay

should have both the regenerative and no regenerative capabilities in a system. The DAF protocol

uses the Hybrid-Decode-Amplify Forward (HDAF protocol), which can regenerate the signal by using

the ADF [2], and amplify the decoding signal that helps to overcome the fading, and noise problems

in the signal [14]. Research shows that, with the increasing numbers of relays between the

transmitter and the destination, the diversity gain also increases [19,2].

2.4.4 Compress-Forward (CF)

Compress and Forward (CF) is the inverse of the AF relaying protocol [20,2]. In the CF protocol, the

relays compress the data before transmitting it towards the destination in the second time slot,

while the AF protocol relays amplify the data before further transmission [18]. In the CF protocol,

compression of the signal helps in bandwidth saving, with faster data transmission is being achieved

[2].


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Chapter 3

Cooperative Spectrum Sensing

3.1 Introduction

In wireless communication, demand of the radio spectrum is increasing. The resources of the radio

spectrum are not enough to catch the increasing demands of the users. The recent research shows

that, 80% to 85% of the total spectrum is remains unutilized, while only 15%-20% of the spectrum is

in use for the maximum period of time. Because the licensed user do not utilize all the available

spectrum at any given time. Hence, it is possible to find the unoccupied frequency spectrum band

that is not utilized by the licensed user at any certain time. Spectrum sensing is the most important

function of the CR. It is very important for the efficient spectrum sensing to determine either the PU

is present or absent. The CR achieve the spectrum intelligence from the environment and it can

adapt the new parameters according to the situation [3]. There are two types of the radio spectrum

i.e. licensed spectrum and the unlicensed spectrum. The licensed spectrum is a specific band of the

radio spectrum, which is sold to a user for the specific service. These specific bands of the spectrum

are always reserved. The unlicensed spectrum bands are not reserved and there are some open

spaces among the licensed frequency bands those can be used by many different unlicensed users

[16,3].

3.2 Spectrum Sensing

The process of determining the free (unused) spectrum of the primary user without making any

interference and disturbing the rights of licensed user is called as spectrum sensing. It is the key

function of the CR which is used to sense the unused spectrum (spectrum holes). These spectrum

holes are also referred as black holes or white spaces [21,3,2]. Due to the scarcity of the spectrum, it

is require to fully utilize the available spectrum. Spectrum sensing can be further divided into some

methods. The cooperative spectrum sensing is one of them.


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3.2.1 Cooperative spectrum sensing

The spectrum sensing is a main aspect of the CR which helps to prevent the interference between

the licensed and the unlicensed user. It can sense the available spectrum for the efficient utilization

[32]. However, performance of the spectrum detection degrades due to the multi path fading,

receiver uncertainty, and the shadowing issues [32,3]. To reduce the effect of these issues, the

cooperative spectrum sensing is an effective method for the improvement in the detection

performance. It can be achieved by exploiting the cooperative diversity and the spatial diversity [32].

The Cooperative spectrum sensing also helps to reduce the probability of false alarm and the

probability of miss-detection [3].

The Hidden terminal problems can also be mitigating by the cooperative spectrum sensing [3]. It

also decreases the spectrum timings. In the wireless networks, the dual-hop communication process

is shown in Fig.3.1. The two hop relaying system is place between the source (PU) and the

destination (SU). Multi-hops transmission helps to achieve the cooperative diversity [3].

Implementation of the dual-hop relaying system helps to achieve the diversity gain. The Cooperative

spectrum sensing improves the detection performance and it can be deployed in three ways:

centralized, distributed and relay-assisted [32,3].

Relay

Primary

UserSecondary

User

1st hop 2

nd hop

Direct Link

Figure 3.1- Dual-hop cooperative spectrum sensing.


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3.3 Spectrum Sensing Techniques

Recently, there is a great concern about the spectrum sensing to make more effective interactivity

between the cognitive radio and the environment. The transmitter detection is one of the major

categories of the spectrum sensing schemes. In this scheme the frequency of the primary user is

determined. Mathematically, the transmitter detection schemes representation by a binary

hypothesis is given as [10]

( ) { ( )

( ) ( ) (3.1)

In Eq 3.1, ( ) is the input signal which is transmitted by the PU, and ( ) is the received signal by

the CR. Where ( ) is Additive White Gaussian Noise (AWGN), indicates the absence of the PU,

and indicates the presence of the PU. The three most commonly used transmitter detection

techniques are: Matched filter, Cyclostationary detection and energy detection. Each technique has

its own pros and cons.

Sensing

Techniques

CoherrentNon-

CoherrentNarroeband Wideband

Matched

Filter

Detection

Cyclostationa

ry Feature

Detection

Energy

Detection

Wavelet

Detection

Compressed

Sensing

Figure 3.2- spectrum sensing techniques flow chart [32].


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3.3.1 Matched Filter

Matched Filter Detection (MFD) is a spectrum sensing method. It is one of the optimal techniques in

the field of signal processing. It is known as optimal spectrum detection technique. The MFD is

used for the PU spectrum when the transmitted signal from the source is already known [33]. When

an unknown signal is matched with the known signal, if the unknown signal template is similar to the

known signal template then it assumed that the PU is present in the spectrum. If the template of the

unknown signal and the known signal is not similar then it is consider that spectrum is free and the

SU can use the spectrum. The MFD is an optimal linear filter that is used for maximizing the SNR in

the additive stochastic noise environment. The output SNR is maximized by convolution of the

transmitted signal (unknown signal) with a filter whose impulse response ( ) is time shifted

version of the known (reference) signal [34]. The Operation of the MFD can be expressed as:

( ) ∑ ( ) ( ) , (3.2)

where ( ) is received signal, is the unknown signal, is the impulse response of the MFD which

match with the known signal for maximizing the output SNR [34].

The whole process of the MFD is shown in Fig.3.3.

AWGNChanal

X(t)AWGNSignal

Matched Filter

Threshold

H1 Matched

H0 no Matched

Figure 3.3- A Block diagram of Matched filter detection [10].

Advantages of Matched Filter

Matched Filters have many useful applications in digital communication and radars, where

the objective of detecting a signal in the noisy environment is achieved.

MFD achieved the certain probability of detection and false alarm probability in a very fast

way.


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Limitations of Matched Filter

In a cognitive radio network, the transmitted signal characteristics are usually unknown.

Therefore, the MFD performance decrease which leads to unwanted signal detection [33].

It requires a specific sensing receiver for different types of the primary user’s signals.

Large power consumption due to the execution of different receiver algorithms for the

detection [33].

3.3.2 Energy Detector

Energy detection (ED) is the most optimal choice for the spectrum sensing where it is difficult for the

CR to get the adequate information about the licensed user waveform. The ED is the most suitable

choice when the CR has information about the power of the random Gaussian noise. The basic

approach behind this technique is the power estimation of the licensed user (primary user) signal.

In this technique, energy of the desired transmitted signal is detected then this detected energy is

compared with a threshold value. The threshold is a pre-defined value. If the detected energy is

below than threshold value then it is pretended that the licensed user is not present and the

spectrum is free. Oppositely, if the detected energy is above the threshold value then it is assume

that the spectrum is not free.

AWGNChanal X(t)

AWGNSignal

EnergyDetector

ThresholdComparing

≥ λ Hı

≤ λ H0

DecisionMaking

Figure 3.4- A Block Diagram of the Energy Detection [10].


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Limitations of Energy detector

The require time to achieve the desire probability of detection may be higher.

The detection performance depends on the uncertainty of the noise.

It is impossible to make distinguish between different primary users because energy

detector is not able to differentiate between the sources of the received energy [33].

It cannot be used for the detection of spread signals [32].

The computation of the threshold value used for detection is highly susceptible for the

variation of the noise levels which leads to a low SNR environment [32].

3.3.3 Cyclostationary Detection

The most recent research shows that, the CFD is the most suitable choice as compared with the ED

and the MFD techniques. It is suggested by the many researches as the most suitable option [10]. As

the MFD technique requires the prior knowledge about the licensed user’s wave but for the ED it is

not necessary to have a prior knowledge of the primary user wave [10] . The ED technique is

simplest but it is highly sensitive with the changing noise levels [22]. The primary modulated

waveforms with the patterns are also characterized as Cyclostationary feature like pulse trains,

hoping sequences, and the sine waves. The cognitive radio can detect any specific modulated

random signal in a stochastic noisy environment by exploiting the mean and the auto correlation

periodic characteristics of the primary waveform [10].

This technique is more effective in an environment where the levels of noise are uncertain. The

noise uncertainty is because of the spectral correlation function of the AWGN channel is zero due to

the stationary property [2]. The absence or presence of the PU signal can be identified by

calculating the spectral correlation of the PU signal at the Cyclostationary detector [2]. The output

of the CFD is compared with the predefined threshold value to determine the presence or absence

of the PU’s signal. The process of the CFD is shown in Fig. 3.5.


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Limitations of the Cyclostationary detection

The CFD is more robust to uncertain levels of noise and gives much better performance in low SNR

regions [32]. However, this technique has its own limitations:

High computational complexity.

Long sensing time

AWGNChanal X(t)

AWGNSignal

PeakSearch

ThresholdComparing

≥ λ Hı

≤ λ H0

DecisionMaking

Figure 3.5- A Block Diagram of the Cyclostationary Detection [10].

3.4 Cooperative Models

Cognitive Radio users cooperate with each other’s to achieve the optimal detection performance.

The cooperation modeling in the cooperative spectrum sensing is basically concerned with the

cooperation behavior of the CR users [32]. There are different approaches which are used for

modeling of the cooperation between the CR users. In the 2-hop digital relaying system, the

cognitive relays cooperate with the transmitter for the successful transmission of the data stream

towards the destination. The cooperative spectrum sensing can be deployed in two ways: the soft

cooperation and the hard cooperation. The soft cooperation model is implemented in this thesis.

3.4.1 Soft Cooperation

For the licensed spectrum sharing, it is very important for the SU to sense the free holes in the PU

spectrum. The role of the CR is very important for sensing the presence or absence of the PU. In the

soft cooperation technique, each cognitive relay does not make individual decision about the

spectrum presence or absence. Each cognitive relay sends the received data bit directly to the CC.

the role of the CC is like a Central Processing Unit (CPU).


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2

The CPU is a final authority for the decision making about the PU spectrum. It gives feedback to

each cognitive relay about the presence or absence of the PU. Each cognitive relay helps the SU for

searching the decision about the presence or absence of the PU. The process of the soft cooperation

is demonstrated in Fig.3.6 [2].

3.4.2 Hard Cooperation

Unlike the soft cooperation, in the hard cooperation technique each cognitive relay makes its own

individual decision based on the received data from the source. Then each CR forwards the

individual decision towards the CPU. Where the CPU is responsible for making the final decision

based on the received individual decisions. It gives feedback to relays about the presence or absence

of the PU. It is obvious from the research that, the soft cooperation gives much better performance.

It improves the 30-40% overall performance in terms of the spectrum detection and the probability

of false alarming. The phenomenon of hard cooperation is shown in Fig. 3.7 [2,3] .

CPU

Cognitive

Relay 1Cognitive

Relay 2

Cognitive

Relay N

Cognitive

Controller makes

final decision

Figure 3.6- Soft Cooperation Cooperative Spectrum Sensing.


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2

CPU

Cognitive

Relay 1Cognitive

Relay 2

Cognitive

Relay N

Cognitive

Controller makes

final decision

Local

Decision 1Local

Decision2Local

Decision N

Figure 3.7- Hard Cooperation Cooperative spectrum sensing.


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Chapter 4

System Model and Simulation Environment

4-1 Introduction

In the recent time, the cooperative communication has got the more attention. In the cooperative

CRN, the cognitive relays gather the spectrum information from the licensed user. After decoding or

amplifying the received data , these cognitive relays further transmits the data towards the

destination. The cognitive relays and Alamouti OSTBC based spectrum sensing is introduced in the

cooperative cognitive radio networks [3,2]. The maximum spatial diversity can be achieved through

the OSTBC implementation in the cooperative cognitive radio network. It helps to overcome the

fading, the shadowing and the propagation effects.

The energy detection technique is implemented at the cognitive controller for the spectrum sensing

in [3,2]. It is the simplest sensing technique and can be easily implement. It gives good sensing result

at higher SNR values but its Performance degrades at low SNR values. Because it cannot

differentiate between the original signal and the noise. In this thesis, all the three transmitter

detection techniques i.e. energy detection, matched filter detection, and cyclostationary detection

has been implemented at the CC. All three sensing techniques operate in the parallel to make their

individual decision about the spectrum availability. The main focus is on the comparative analysis of

these techniques at low SNR values. After making the final decision, the CC gives feedback to the

cognitive relays about the PU spectrum availability or unviability. If the PU is absent then the SU

share the spectrum without making any interference with the PU.

4.2 Alamouti Space Time Block Coding (STBCs) Schemes

Alamouti coding schemes are most commonly used for transferring the symbols through the

multiple transmitting and the receiving antennas. The Alamouti 2 x 2 STBC scheme [3,2] is used in

our proposed CRN. This scheme is known for achieving the code rate of 1. In this coding scheme,

two transmitting and two receiving antennas are used for achieving the spatial diversity [14,15].


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The Alamouti 2 x 2 coding scheme is shown in Fig. 4.1 & 4.2 [2] .

Figure 4.1- Alamouti 2 x 2 OSTBC Scheme Mechanism.

𝐴 = 1 2

2

1

Time Slot

𝑇 𝐴

Figure 4.2- Alamouti 2 x 2 OSTBC transmissions Matrix.

In Fig.4.2, two transmitting and two receiving antennas are used for the transmission. The process of

the data stream transmission is completed in the 2-hops. The OSTBC is used for the transmission to

achieve the maximum spatial diversity. In Fig. 4.3, the BER performance for the different scenario is


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2

Studied. It is clear that, the BER performance of the STBC transmission is much better as compared

with the Maximum Ratio combining (MRC) and the different antennas.

Figure 4.3- Performance comparison of the Alamouti OSTBC with the other Diversity Schemes

4.3 The Simulation Model

The simulation model implemented in this thesis, describes the coding and the implementation of all

spectrum sensing techniques over the dual-hop OSTBC transmission. The cooperative cognitive

radio network model used consists of a PU, cognitive relays, and the CC. The CC is actually a fusion

center; its responsibility is to make the final decision about the availability or absence of the PU

spectrum. At the CC, three different transmitter detection techniques i.e. energy detection,

matched filter detection and cyclostationary feature detection are implemented in the parallel.

The channel used between the PU and the SU is the multi path Raleigh Fading Channel. The PU

signal reached at the SU is distorted by the noise. The Additive White Gaussian Noise (AWGN) is

assumed with mean = 0 and variance =

[16 2,3]. The model is shown in figure 4.4 and 4.5.

0 2 4 6 8 10 12 14 16 18 2010

-5

10-4

10-3

10-2

10-1

Eb/No, dB

Bit E

rro

r R

ate

(B

ER

)

BER for BPSK modulation with 2Tx, 2Rx Alamouti OSTBC

nTx=1,nRx=1

nTx=1,nRx=2, MRC

nTx=2, nRx=1, Alamouti OSTBc

nTx=2, nRx=2, Alamouti OSTBC


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2

In the system model shown in Fig. 4.4, the 2 x 2 Alamouti OSTBC scheme is implemented with the DF

protocol. The distance between the PU the and the CRs is , and similarly is the distance

between the CRs and the SU. The channel mean power between the two hops is given in Eq 4.1 &

4.2 respectively. The power of channel is suffered from the path loss effect over any wireless links

[16].

( ) ( )

( ) , (4.2)

Figure 4.4- Proposed 2-hop cooperative cognitive radio network simulation model: Implementation of

the transmitter detection techniques at the cognitive controller.


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2

where is the channel mean power between the PU and the CRs and similarly is for the CRs

and the SU [8,16]. The channel mean power for the direct path between the PU and the SU is

[16]. In eq. 4.1,

( ) is the distance adapting element between the PU and the CC, and represents

the exponential path loss during the propagation [16,2]. It has exponential decaying property and it

varies relatively within cognitive radio network model [29,3].

Figure 4.5- A Process Flow Diagram of the 2-hop Cooperative CRN System Model.


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2

In eq 4.1 & 4.2, is the Raleigh fading coefficients between the PU and the CRs, similarly is

between CRs and the SU respectively [15]. The transmitted data reached at the receiver is distorted

from the propagation of radio signals. Thus, the estimation (decoding) of the received data is highly

complicated, produced more errors due to the fading, shadowing and the propagation factors

[3,21].

In order to get more reliable and error free data at the receiver side, transmission of the data

between the PU and the SU is completed in the 2-hop communication process by using the TDD

scheme. In the 1st hop and time slot one, the encoded data is transmitted in the air. The data

reached at the receiver through the direct path and the indirect path which consist of the cognitive

relays [3] . During the 2nd hop and time slot 2, the data received by the cognitive relays is estimated

by using the ML decoding and further transmitted towards the CC. The CC is responsible for the final

decision making about the availability or non-availability of the licensed user spectrum. For the

detection purpose, three different transmitter detection techniques are implemented. The entire

spectrum sensing techniques operates in the parallel to come up with more reliable result about the

availability of the spectrum. The signal received through both the direct and the indirect path at the

destination is given by

(4.3)

, (4.4)

where , are the fading channal coefficients, is the transmitted siganl and is the AWGN.

In the proposed system model, two transmitters and two receivers are assumed at the transmitter

and the receiver side respectively. The cognitive relays used have the half duplex features while the

antennas have full duplex control [3,16,2].


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4.5 Implementation of the Energy Detection Method at the CC

The energy detection method is implemented at the cognitive controller. There are numbers of

steps involve in the Implementation process of the energy detector at the CC. It uses the BPF in

order to control the noise power and for the normalization of the variance [3]. The BPF used have

the carrier frequency , and bandwidth ( ). To calculate the energy ( ) of the received signal at

the CC, first it sends the signal through the BPF, then to square the bandwidth of the desired signal.

In the last step the output is sent through the squaring device which forwards the output to an

integrator device for the purpose of integration over the time period 𝑇 [16,3,2]. The number of

samples for the individual component of the received signal is represented by

𝑇 , (4.5)

where is the bandwidth and 𝑇 is the time period taken by the number of samples to arrive at the

destination [3,2,16]. The received signal at the CC, is actually a test static which is compared with

the predefined threshold value λ to make the decision about the presence or absence of the PU

spectrum [3]. The probability of detection and probability of false are given by [2,3].

(√ √ ) (4.6)

(

)

( ) (4.7)

where , represents the probability of detection and probability of false alarm respectively, is

representation of the Marcum-Q function [26,3,2] , and is the upper incomplete gamma function

[3] having degree of freedom of (.) [2]. The can be defined as

( ) ∫

(4.8)

The energy detection method in described in Figure 4.6 [2,3]. The process flow of this sensing

technique is described in Figure 4.7.


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2

Figure 4.6. A Block Diagram of Energy Detector at the CC.

Figure 4.7. A Process Flow Diagram for the Energy Detector at the CC in 2-hop CRN.


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2

4.5 Implementation of the Cyclostationary Feature Detection Method at the CC

The cyclostationary feature detection technique is more optimized technique which can

differentiate between the noise and the user signal. This technique is implemented in the parallel

with the energy detector and the matched filter at the CC. Implementation process of the

cyclstationary technique completed in the few steps. In order to measure the energy ( ) around the

related signal, the BPF is used. The Fourier transforms FFT of the output signal is computed [34].

Then this output is passed through the correlation block that correlates the signal. The feature

detection block is used to detect the features of the received signal like symbols rates, modulation

type, periodicity, and the clock rates [34].

The implementation process is described in flow diagram shown the Figure 4.8. The probability of

detection and the probability of false alarm of the cyclostationary feature technique is given

in Eq 4.8 & 4.9 [27].

(√

) (4.9)

( )

, (4.10)

where is the given SNR, and is the average energy of received prefix signal, and are numbers

of the samples. The cyclstationary feature technique is described in block diagram in figure 4.9

[10,5,8].


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2

Figure 4.8- A Process Flow Diagram of Cyclostationary Technique at the CC.


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01

2

Figure 4.9 - A Block Diagram of the Cyclostationary Feature Detection Technique.

4.6 Implementation of the Matched Filter Detection Method at the CC

The matched filter detection is implemented at the CC. In this case the SU already have information

about the PU. This technique has been discussed in details in chapter three. Here the only

implementation process is discussed. In the first step, the received signal at the CC as input is passed

through the BPF to measure the energy around the related band and then the output is convolved

with the match filter which has the same impulse response as the reference signal [34].

The outcomes from the matched filter are then compared with the threshold value to make decision

about the presence or absence of the PU. The probability of detection and probability of false alarm

for the matched filter discussed is given in Eq 4.10 & 4.11 [8].

(

√ ) (4.10)

(

√ ) (4.11)

The decision statistics for the Matched filter is given by

∑ ( ) ( ) (4.12)

where ( ), is the deterministic signal which is received at the CC, the energy of this signal is

∑ ( ) [5]. The probability of the false alarm and the probability of detection are denoted

by and respectively.

The implementation process of this technique is described in flow diagram in Figure 4.8. The block

diagram of this technique is illustrated in Figure 4.10 [10.8.5].


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Figure 4.10. A Block Diagram of the Matched Filter at the CC.

Figure 4.11. A Process Flow Diagram of Matched Filter at the CC.


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Chapter 5

Results and Analysis

5.1 Simulation Results and Analysis

In order to analyze the comparative performance of all three transmitter detection techniques, an

extensive simulation is performed in Matlab by using the system model described in section 4.3.

There are three performance matrices that are used to make the comparison i.e. “Probability of

primary detection”, “Receiver Operating Characteristic (ROC) Curves”, and “Probability of detection

vs Lamda”. The effects of changing the position and changing the numbers of the cognitive relays

between the PU and the SU are also analyzed based on the detection performance matrices. The

main focus is on the performance analysis of spectrum sensing over the 2-hop cooperative cognitive

radio networks.

5.1.1 Probability of Primary Detection

Figure 5.1, 5.2, 5.3 and 5.4 shows the “Probability of primary detection” as a function of SNR for all

three transmitter detection techniques: energy detection, cyclostationary feature detection, and the

matched filter detection. The performance is analyzed in four different scenarios i.e. by changing the

position, and number of cognitive relays between the PU and the SU.

Figure 5.1 shows that, the performance of all three techniques is 100% at higher SNR values of 20dB

and above. At low SNR values, the performance of the cyclostationary feature detection technique

is best as compared with the others two techniques. It shows good results over the entire range of

40dB to -40dB and its performance does not affected by the SNR values. The energy detection and

matched filter detection techniques starts working at -18dB. The performance of the ED over the

MFD is better over the range of 10dB to -20dB. But the MFD results are reliable because the ED

cannot differentiate between the actual signal and the noise signal at weaker SNR values. Figure 5.2

indicates that, by changing the position of the relays, the detection performance is also affected. It is

clear that, when the relays are in the middle of the PU and the SU, the detection performance

increases.


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Figure 5.1. Probability of primary detection: comparative analysis of the ED, MFD, and CFD. Cognitive relays

are assumed to be kept near the PU. The threshold is set to λ=40 and the numbers of relays used here are 4.

Figure 5.2- Probability of primary detection: comparative analysis of the FD, MFD, and CFD. Cognitive relays

are assumed to be in the middle of the PU and the SU. The threshold is set to λ=40 and the numbers of relays,

used are 4.

-40 -30 -20 -10 0 10 20 30 4010

-10

10-8

10-6

10-4

10-2

100

SNR(dB)

Pro

ba

bili

ty o

f D

ete

ctio

n (

Pd

)Performance comparison of Transmitter detection techniques: over 2-hop CRN

Matched Filter Detection

Energy Detection

Cyclostationary feature detection

-0.2

0

0.2

0.4

0.6

0.8

-40 -30 -20 -10 0 10 20 30 4010

-5

10-4

10-3

10-2

10-1

100

SNR(dB)

Pro

ba

bili

ty o

f D

ete

ctio

n (

Pd

)

Performance comparison of Transmitter detection techniques: over 2-hop CRN


Energy Detection


-0.2

0

0.2

0.4

0.6

0.8


47

2

01

2

In Fig 5.3 & 5.4, the relays are placed near the SU and effect of changing the number of the relays is

studied. It is clear from the results shown in Fig 5.4, by decreasing the numbers of the relays, the

performance also decreases.

The CFD is best detection technique for the 2-hop CRN in terms of probability of detection over the

entire range of SNR. At the higher SNR values, the ED is the best choice as it is simplest. The

detection performance can be increases by increasing the numbers of the cognitive relays.

Figure 5.3- Probability of primary detection: comparative analysis of the FD, MFD, and CFD. Cognitive Relays

are assumed to be placed near the SU. The threshold is set to λ=40, and the numbers of relays used here are 4.

-40 -30 -20 -10 0 10 20 30 4010

-5

10-4

10-3

10-2

10-1

100

SNR(dB)

Pro

ba

bili

ty o

f D

ete

ctio

n (

Pd

)

Performance comparison of Transmitter detection techniques: over 2-hop CRN


Energy Detection


-0.2

0

0.2

0.4

0.6

0.8


48

2

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2

Figure 5.4- Probability of primary detection: comparative analysis of the FD, MFD, and CFD.

Cognitive relays are assumed to be placed near the SU. The threshold is set to λ=40, and the

numbers of relay used is 1.

5.1.2 Receiver Operating Characteristic (ROC) Curves

Receiver operating curves (ROC) presents the graphical summaries of the detection techniques

performance. To examine the effect of varying the probability of false alarm on the probability of

detection for a fixed SNR value, a performance comparison of all three techniques, energy

detection, Cyclostationary feature detection, and matched filter detection has been made. Fig.5.5,

5.6, 5.7, and 5.8 shows the performance comparison of these techniques based on the ROC for a

fixed SNR values.

It is seen that, the Cyclostationary feature detection has superior detection of probability above the

ED, and the MFD. At the low SNR of -15dB, the CFD technique outperforms the others two

techniques. At the higher SNR values of 20dB, all three techniques yield the same performance. It is

observed that, if a given probability of false alarm increases, the probability of detection also

increases. The position and number of the relays also affect the probability of detection. It is clear

from Fig. 5.7, when number of the relays used is four, shows the better performance as compared

with the Fig. 5.8, where the number of relay, used is one.

-40 -30 -20 -10 0 10 20 30 4010

-5

10-4

10-3

10-2

10-1

100

SNR(dB)

Pro

ba

bili

ty o

f D

ete

ctio

n (

Pd

)Performance comparison of Transmitter detection techniques: over 2-hop CRN


Energy Detection


-0.2

0

0.2

0.4

0.6

0.8


49

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2

So, the Cyclostationary feature detection technique is best choice above others two techniques in

the 2-hop cooperative cognitive radio network.

Figure 5.5- Comparison of ED, MFD, and CFD at fixed SNR of -15dB. The cognitive relays

are assumed to Place near the PU.

Figure 5.6- Comparison of ED, MFD, and CFD at fixed SNR of higher SNR of 20dB. The

Cognitive relays are assumed to Place near the PU.

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Probability of False Alarm (Pf)

Pro

ba

bili

ty o

f d

ete

ctio

n (

Pd

)

Performance comparison of Transmitter detection techniques

Matched Filter


Energy detection

SNR= -15dB

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10.84

0.86

0.88

0.9

0.92

0.94

0.96

0.98

1

1.02


Pro

ba

bili

ty o

f d

ete

ctio

n (

Pd

)


Matched Filter


Energy detection

SNR=20dB


50

2

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Figure 5.7- Comparison of ED, MFD, and CFD at fixed SNR of 3dB. The cognitive relays

are assumed to Place near the PU.

Figure 5.8- Comparison of ED, MFD, and CFD at fixed SNR of 3dB. The cognitive relays

are assumed to Place in middle of the PU and the SU.

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1


Pro

ba

bili

ty o

f d

ete

ctio

n (

Pd

)Performance comparison of Transmitter detection techniques

Matched Filter


Energy detection

SNR= 3dB

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1


Pro

ba

bili

ty o

f d

ete

ctio

n (

Pd

)



Matched Filter

Energy detection

SNR= 3dB


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5.1.3 Probability of Detection vs Lamda

In Fig 5.9, 5.10, 5.11 and 5.12, impact of the threshold value λ on the detection is studied. A

performance comparison of three transmitter detection techniques, ED, MFD, and the CFD is made

based on the impact of threshold value on the probability of detection. In Fig 5.9, the impact of

threshold on the probability of detection is studied at lower SNR of -20dB. It is observed that, as the

threshold increases, the Pd decreases. Performance of the CFD is much better as compared with the

MFD and the ED. At the higher SNR value of 20dB, it is observed that all three techniques show the

same performance as shown in Fig 5.10. the MFD technique shows the better performance above

the ED. It is observed that, when the cognitive relays are placed in the middle of the PU and the SU,

the detection performance is slightly increases as shown in Fig 5.11. when the CRs are placed near

the SU, the performance of all the three techniques is similar to that when the CRs are near the PU.

So, the CFD technique performance is best in this case also.

Figure 5.9- Pd vs Lamda: Performance analysis of the ED, MFD, and CFD. Cognitive relays are

assumed to be placed near the PU. The SNR is -20dB and the numbers of relays are 4.

0 2 4 6 8 10 12 14 16 18 2010

-4

10-3

10-2

10-1

100

Lambda

Pro

ba

bili

ty o

f d

ete

ctio

n (

Pd

)

Pd vs Lamda, performance comparison over 2-hop CRN

Matched Filter

Cyclostationary Feature Detection

Energy Detection

SNR= -20dB


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Figure 5.10- Pd vs Lamda: Performance analysis of the FD, MFD, and CFD. Cognitive relays

are assumed to be placed near the PU. The SNR is 20dB, and the numbers of relays are 4.

Figure 5.11- Pd vs Lamda: Performance analysis of the FD, MFD, and CFD. Cognitive relays are

assumed to be in the middle of the PU and the SU. The SNR is -20dB, and the numbers of relays

are 4.

0 2 4 6 8 10 12 14 16 18 2010

-1

100

101

Lambda

Pro

ba

bili

ty o

f d

ete

ctio

n (

Pd

)


Matched Filter


Energy Detection

SNR= 20dB

0 2 4 6 8 10 12 14 16 18 2010

-4

10-3

10-2

10-1

100

Lambda

Pro

ba

bili

ty o

f d

ete

ctio

n (

Pd

)


Matched Filter


Energy Detection


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Figure 5.12- Pd vs Lamda: Performance analysis of the FD, MFD, and CFD. Cognitive relays are

assumed to be near the SU. The SNR is -20dB, and the numbers of relays are 4.

0 2 4 6 8 10 12 14 16 18 2010

-4

10-3

10-2

10-1

100

Lambda

Pro

ba

bili

ty o

f d

ete

ctio

n (

Pd

)


Matched Filter


Energy Detection


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Chapter 6

Performance Evaluation of Fuzzy Combined Logic Spectrum Sensing

approach

6.1 Introduction

The transmitter detection techniques have been analyzed in the chapter 5. The simulation results

show that, the cyclostationary feature detection technique is most optimum choice for spectrum

sensing. Problem with the cyclostationary feature technique is the complexity and it’s taking more

time for sensing the PU signal. Spectrum sensing performance of the energy detection technique at

the higher SNR value is outstanding and it is also simplest technique but at the lower SNR value its

performance is not good. To achieve the more reliable and efficient spectrum sensing, the Fuzzy

Logic approach is implemented in [10], where all the three mentioned spectrum sensing techniques

are implemented in parallel and then the outputs from all three techniques are combined by using

Fuzzy Logic to get more reliable results. This approach is implemented to improve the local spectrum

in [10], in this thesis, “Fuzzy Logic based spectrum sensing” is implemented in 2-hop cooperative

cognitive radio.

6.2 Fuzzy combined Logic Spectrum Sensing

The framework for implementation of the Fuzzy Combined Logic approach is illustrated in Figure 6.1.

The Fuzzy algorithm in [10], takes the outputs of the each mentioned transmitter detection

techniques and treat them as input for the Fuzzy logic. In this algorithm, the output of each sensing

technique is based on Fuzzy Logic as in [10]. In the proposed algorithm, energy detection,

cyclostationary, and matched filter technique is implemented in parallel at the CC, then the fuzzy

combined logic algorithm is implemented on the outputs of the each individual technique. There are

three input members function denoted with 0, 0.5, and 1. These numeric values indicates the

absence, not sure and present. There are seven members functions named worst, very bad, bad,

moderate, good, very good, and the best [10], the relation between the input and output functions

is illustrated in table in Figure 6.1. The Fuzzy logic table is based on the fuzzy logic used in [10]. The

function worst indicates that, the PU is absent and the best function indicates the 100% presence of

the PU. The output of this algorithm is normalized between 0 and 1 and this output is compare with

the threshold value to make final decision about the presence or absence of the PU signal.


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This approach is compared with the transmitter detection techniques on the performance matrices

“probability of detection”.

Figure 6.1- A Process Flow Diagram of Fuzzy Combined Logic at the CC.


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2

6.2.1 Probability of Primary Detection

Figure 6.2- probability of primary detection: comparative analysis of Fuzzy logic with transmitter

detection techniques, cognitive relays are assumed to be kept near the PU.

Fig.6.2 shows the comparison among the transmitter detection techniques and the Fuzzy combined

sensing approach on the basis of probability of detection. The fuzzy combined logic approach yield

better performance over the range of -30dB to 40dB. This technique detects the primary user even

at lower SNR values of -30dB and it shows almost 100% accuracy at -10dB. To achieve the same kind

of performance, the energy detection , matched filter, and cyclostationary feature detection

technique requires higher SNR of 5dB or above. Moreover, cyclostationary feature detection

technique shows the almost same result over the entire range of -40dB to 40dB. But its performance

starts to decrease at SNR of 5dB while the fuzzy combined logic scheme performance decreases

after -10dB or so. This sensing approach helps to overcome the short comings of all three

transmitter detection techniques. So, Fuzzy combined Logic sensing approach is more reliable and

accurate sensing technique for the 2-hop cooperative cognitive radio networks. A fuzzy Logic

technique is also implemented in the C++.


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Figure 6.3- Fuzzy Combined Logic technique implementation in C++.

Figure 6.3 shows the executable result of the C++. As it is clear from the figure, the best result is

Fuzzy Logic technique when all the three techniques give higher result that is 1.


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Chapter 7 Conclusions and Future Work

7.1 Conclusions

A comparison of different spectrum sensing techniques in the 2-hop cooperative cognitive radio

network has been made in this thesis. OSTBC scheme is used for the data transmission between the

transmitter and the receiver. DF protocol is implemented at the CRs. Transmitter detection

techniques i.e. energy detection, cyclostationary feature detection, and matched filter detection

techniques are implemented in the parallel for the PU signal detection. Each technique has its own

pros and cons. To achieve the maximum reliability, all three techniques are combined by using the

Fuzzy Logic. Detection of the PU signal is very important in the CRN. Decision about the presence or

absence of the PU in their spectrum depends on the received signal detection. This work is helpful

for detection of the PU at the low SNR and in the noise uncertainty.

All three detection techniques are implemented at the CC. Simulations results shows that, energy

detection technique gives excellent detection of the PU at higher SNR. Moreover, it is a simplest

detecting algorithm that does not depend on the noise uncertainty. Similarly, cyclostationary

feature detection and matched filter detection also shows the best detection results at higher SNR

values of 20dB or above.

It is seen that, performance of all three techniques varies as the SNR values decreases. Energy

detection technique performance degrades at lower SNR. Although it does not depends on the noise

uncertainty but it also can not differentiate between the original signal and noise. Cyclostationary

feature detection shows the better detection performance as compared with the matched filter and

energy detection techniques. As it yield good performance even at lower SNR of -40dB. It does not

depend on the noise uncertainty. Matched filter technique shows better performance as compare

the energy detection technique.


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It is observed that, each transmitter detection technique has an SNR threshold below which their

performance degrades. To get more reliable and accurate results, Fuzzy combined logic sensing

approach is best option. In this technique all three detection techniques are combined by using the

Fuzzy Logic. Fuzzy Logic approach increases the chances of accuracy and reliability. It gives more

reliable and accurate results over all the range of SNR levels as compared with ED and the MFD.

Maximum spatial diversity is achieved in the cooperative cognitive network by implementing the

OSTBC scheme, which helps to improve the detection performance. For the 2-hop CRN,

cyclstationary feature detection technique is the best transmitter sensing technique at the lower

SNR. Maximum reliability and accuracy can be achieved by the Fuzzy logic approach at the cost of

time and price.

The proposed detection algorithm is helpful in spectrum sensing for making the more reliable and

accurate decisions about the spectrum presence or absence. Through this algorithm spatial diversity

is achieved and code diversity is improved by transmitting the spectrum for sharing and accurate

detection.

7.2 Future Work

These spectrum sensing algorithms can be implemented by using the GNU software and the

USPR platform.

Testing of the proposed Fuzzy Logic approach in a real life scenario by using a spectrum

sensing hardware platform.

To improve the local spectrum sensing, this approach can be implemented via the hard

cooperation.

Hybrid sensing approach can be implemented in which first Cyclostationary feature

detection technique extract the features of the PU and then energy detection technique is

used for spectrum sensing.


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In Cognitive Radio Systems”, Australian journal of basic and applied sciences, 2010.

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