HRD Programme for Exchange of ICT Researchers/Engineers

Through Collaborative Research

FINAL TECHNICAL REPORT

On

Cognitive Radio and Dynamic Spectrum Sharing in

Cognitive Radio

Submitted To

Asia-Pacific Telecommunity

Submitted By

Mr. Nipendra Kayastha

Mr. Tapio J. Erke

Dr. Teerawat Issariyakul

September 2008

i

ACKNOWLEDGEMENT

We would like to express our sincere gratitude towards Asia-Pacific Telecommunity

for conducting HRD Programme for Exchange of ICT researchers through

Collaborative Research. This research opportunity provided us to explore some new

technologies which would be helpful in completing the project objectives.

I would like to personally thank my advisor Mr. Tapio J. Erke (Associate Professor,

AIT, Thailand) for his continuous help and guidance. His untiring willingness to help

and cheerful and friendly disposition made it very easy for me during the preparation

of this report. I am also thankful to Dr. Teerawat Issariyakul (Project Coordinator,

TOT Public Company Limited, Thailand) for providing me with the opportunity to

carry out this joint research with Asia-Pacific Telecommunity.

Also finally, I would like to extend my gratitude to Dr. R.M.A.P. Rajatheva

(TC/ICT coordinator, AIT, Thailand) for his valuable suggestions.

Project Members

Mr. Nipendra Kayastha

Mr. Tapio J. Erke (Advisor)

Dr. Teerawat Issariyakul (Project Coordinator)

ii

ABSTRACT

The main objective of this report is to study overlay dynamic spectrum sharing for

cognitive radios. The report provides a comprehensive description of cognitive radio

and a detailed overview of different overlay dynamic spectrum sharing techniques

that has been proposed so far.

The report aims to cover different aspects of Dynamic Spectrum Sharing in cognitive

radio environment for providing fairness among the cognitive radio users. Cognitive

users being the secondary users of the system are force to terminate whenever the

licensed users reappear and demand for the spectrum which is being used by the

cognitive users. The force termination leads to the dropdown of the ongoing

communication whose degradation is more severe than that of when the users are not

able to connect. These issues are studied and explored in this report with relevant case

studies and results. These case studies and background overview of cognitive radio

technologies are then used to formulate a simulation test bed which will be used to

study traffic behavior in a time dependent environment taking in consideration the

performance and fairness issues when using different overlay dynamic spectrum

sharing techniques.

iii

TABLE OF CONTENTS

Chapter Title Page

Acknowledgement i

Abstract ii

Table of Contents iii

List of Abbreviations vi

List of Figures vii

List of Tables ix

List of Symbols x

1 Introduction 1

1.1 Background 1

1.2 Objective 2

1.3 Report Outline 3

2 Literature Review 4

2.1 Dynamic Spectrum Sharing 4

2.2 Types of Dynamic Spectrum Sharing 4

2.2.1 Underlay Spectrum Sharing 4

2.2.2 Overlay Spectrum Sharing 5

2.3 Cognitive Radio 5

2.4 Issues of Cognitive Radio 7

2.4.1 Identification and Protection of Primary Users 7

2.4.2 Coexistence and Fairness issues in Spectrum Sharing 10

2.5 Significant Work in Overlay Dynamic Spectrum Sharing 11

2.5.1 Spectrum Pooling 11

2.5.2 Prioritized Primary Access for Spectrum Sharing 14

2.5.3 Spectrum Handoff using Optimal Channel Reservation for

CognCognitive Radio 17

2.5.4 Spectrum Sharing Among the Cognitive Users 19

2.6 Discussion 24

2.7 Comparison 25

iv

TABLE OF CONTENTS

Chapter Title Page

3 Research and Development 28

3.1 Methodology 28

3.2 System Model 29

3.2.1 Spectrum Sharing with Uncontrolled Access Scheme 30

3.2.2 Spectrum Sharing with Controlled Access Scheme 31

3.2.3 Spectrum Sharing with Cognitive Spectrum Hopping 31

3.2.4 Spectrum Sharing with Buffering of Cognitive Users 31

3.3 System Traffic Model 32

3.4 Performance Measurements 32

3.5 Performance and Fairness Analysis Methods 33

3.5.1 Analytical Method 34

3.5.2 Simulation Method 34

4 Analytical model 35

4.1 Markov chain Model for Uncontrolled Access Scheme 35

4.2 Markov chain Model for Controlled and Cognitive Spectrum

hophopping 35

4.3 Markov chain model for Spectrum sharing with Cognitive

BufBuffering 35

5 Simulation Model 38

5.1 Overview 38

5.2 Simulation Flow chart 38

5.3 Input Parameters 40

5.4 Traffic Generation: Arrival and Termination Processes 40

5.4.1 Arrival Process 40

5.4.2 Service Time 40

5.5 Spectrum Etiquette Rules 41

5.6 Admission Control Unit 41

5.7 Performance Parameter Calculation 41

5.7.1 Holding Time Measurement 42

v

5.7.2 Service Time Measurement 42

5.7.3 Performance Parameter Calculation 43

TABLE OF CONTENTS

Chapter Title Page

5.8 Validation of Simulation Model 43

5.8.1 Simulation Parameters 44

6 Preliminary Results 46

6.1 Study of Different Access Schemes 46

6.2 Performance Analysis of Different cases 46

6.2.1 Effect of Buffering of Cognitive users in different Access

Schemes 60

6.3 Overview of Result 63

7 Conclusion 65

References 66

Apppendix A 69

Apppendix B 72

vi

LIST OF ABBREVIATIONS

A/D Analog-to-Digital

AGC Automatic gain control

CR Cognitive Radio

DFS Dynamic Frequency Selection

DSA Dynamic Spectrum Access

DSS Dynamic Spectrum Sharing

FCC Federal Communication Commission

FCC Federal Communications Commission

GoS Grade of Service

IEEE Institute of Electrical and Electronics Engineers

LAN Local Area Networks

OFDM Orthogonal Frequency Division Multiplexing

PLL Phase locked loop

PU Primary User

QoS Quality-of-Service

SARA Spectrum Agile Radios

SDR Software Defined Radio

SR Software Radio

SU Secondary User

TPC Transmit Power Control

VCO Voltage-Controlled Oscillator

WiMAX Worldwide Interoperability for Microwave Access

WLANs Wireless Local Area Networks

xG Next Generation

vii

LIST OF FIGURES

Figure Title Page

1.1 Spectrum Usage in an Urban Area 1

2.1 Spectrum Hole Concept 6

2.2 Markov model for Controlled Channel Assignment 12

2.3 Markov model for Uncontrolled Channel Assignment 13

2.4 Three Dimensional Markov Chain Model 15

2.5 Modified Markov Chain Model for Reservation Case 17

2.6 Rate diagram of state (i, j) with Spectrum Handoff and Channel Reservation

Reservati Reservation 18

2.7 Frequency Channel use by two different types of radio systems A & B 20

2.8 Markov Chain to Model the Unlicensed Spectrum Access Process with

waiting waiting 21

2.9 Packing Behavior Example 22

3.1 System Diagram of Cognitive Radio Environment 29

3.2 Frequency Band of two users 29

3.3 The System Model 30

4.1 State Diagram for Controlled Access Scheme with Buffering 36

5.1 Simulation Model 38

5.2 Simulation Flow chart 39

5.3 Statistical Data measurement in Simulation Model 42

5.4 Measurement of Holding Time 42

5.5 Measurement of Carried Traffic 43

6.1 Performance Analysis of 1 server with 0.2 Erl Primary Offered Traffic 48

6.2 Performance Analysis of 1 server with 0.4 Erl Primary Offered Traffic 49

6.3 Performance Analysis of 2 servers with 0.4 Erl Primary Offered Traffic 50

6.4 Performance Analysis of 2 servers with 0.7 Erl Primary Offered Traffic 51

6.5 Performance Analysis of 5 servers with 1 Erl Primary Offered Traffic 52

6.6 Performance Analysis of 5 servers with 2.5 Erl Primary Offered Traffic 53

6.7 Performance Analysis of 20 servers with 10 Erl Primary Offered Traffic 54

viii

6.8 Performance Analysis of 20 servers with 14 Erl Primary Offered Traffic 55

LIST OF FIGURES

Figure Title Page

6.9 Performance Analysis of 50 servers with 20 Erl Primary Offered Traffic 56

6.10 Performance Analysis of 50 servers with 30 Erl Primary Offered Traffic 57

6.11 Performance Analysis of 100 servers with 40 Erl Primary Offered Traffic 58

6.12 Performance Analysis of 100 servers with 75 Erl Primary Offered Traffic 59

6.13 Performance Analysis of 1 server with 0.4 Erl Primary Offered Traffic with

buffer buffer 61

6.14 Performance Analysis of 2 servers with 0.7 Erl Primary Offered Traffic with

buffer buffer 61

6.15 Performance of Cognitive Users for different give up time 63

ix

LIST OF TABLES

Table Title Page

2.1 Comparison of different spectrum Overlay Approaches 25

3.1 Different Parameters for Performance and Fairness Analysis 33

5.1 Input Parameters 40

5.2 Performance Parameter Calculation 43

5.3 Initial Simulation Parameters 44

5.4 Simulation Parameters to Validate Erlang Loss System 44

5.5 Simulation Parameters to Validate Different Access Schemes 44

6.1 Different Access Scheme Considered for Simulation 46

x

LIST OF SYMBOLS

Symbols Definition

Equal number of Sub bands

Total Bandwidth

Capacity of Secondary Users

Bandwidth of i type user

Weight Fairness

Number of primary sub bands

Number of Primary users

Number of Players

Number of spectrum bands

Number of Type i radio system

Steady State Probability

Maximum allowable Blocking Call Probability

Blocking Probability of the Cognitive users

with r reserve channel

Block Call Probability of Secondary Users

Maximum allowable Drop call Probability

Drop call Probability of Secondary Users

Forced Termination Probability of cognitive

users

Forced Termination Probability of the Cognitive users

with r reserve Channel

State Probability

Number of reservation channels for cognitive user

Number of reserve channel for primary user

Time Interval of Secondary users to vacate

Time limit for simulation

Time limit for simulation

Utility Factor

Players Payoff

xi

LIST OF SYMBOLS

Symbols Definition

Envy factor

Random variable which describes the occupied

bandwidth

Guilt factor

Bandwidth Utilization

Transition rate from state (i, j) to state (i−k, j +1)

Arrival rate of Cognitive users

,

Arrival rate of Primary/Owner/Licensed users

Arrival rate for radio system i

Forced termination rate.

Small Observation period

Termination rate of Cognitive users

Termination rate of Primary users

Termination rate for radio system i

1

CHAPTER 1

INTRODUCTION

1.1 Background

Today the wireless communication technology is developing at a rapid pace and the availability

of the network is being more ubiquitous. But this advancement in the technology is creating

more challenges in the field of communication. One key area that is being affected is spectrum

access policies and spectrum management for wireless network since its demand is increasing

day by day. But the radio spectrum which is a natural resource is scarce and hard to manage on

real time.

Moreover, this concept of scarcity of spectrum availability is more prominent from the intensive

usage of the spectrum below 3 GHz in an urban area as shown in Figure 1.1. It can be seen that

the usage of the frequency band below 3 GHz is drastically high whereas the usage of spectrum

at higher frequency is quite low. This shows that, not all the radio spectrums are occupied by the

users at given time and location making its usage quite random in nature. A quick look of the

Fig 1.1 shows that, spectrum usage in 3-4 GHz is quite moderate and is low at 4-5 GHz. This

availability of the unused spectrum is opening a new paradigm in the spectrum access policies.

Figure 1.1: Spectrum Usage in an Urban Area

(Adapted from [Brodersen et al., 2004])

These unoccupied spectrums can be considered as Spectrum Holes in the spectrum band which

can be defined as “a band of frequencies assigned to a primary user, but, at a particular time and

specific geographic location, the band is not being utilized by that user” [P. Kolodzy et al.,

2001, cited in Haykin, 2005].

2

This is in fact contradictory to the very fact that there is spectrum shortage because we see here

that the most of the spectrum are free and this problem of scarcity in fact is outcome of

regulatory and licensing process [Brodersen et al., 2004]. So some kind of approach or radio

system is needed which can extend the usage of spectrum holes to other unlicensed users without

interfering with the present users who have legacy right to use it.

As a consequence, a new radio system called Cognitive Radio (CR) [Mitola III, 2000] was

proposed which is able to sense the spectral environment for spectrum holes, detect the

presence/absences of legacy users over a wide available band, and use the spectrum only if

communication does not interfere with the legacy users [Brodersen et al., 2004]. Thus the

cognitive radio has been proposed by FCC in order to efficiently use the available spectrum by

learning and changing certain parameters like frequency selection, transmit power, carrier

frequency, and modulation schemes to adapt to the variation in the available spectrum in order to

maximize the utilization of radio spectrum [Haykin, 2005]. The key characteristic of CR is

dynamic spectrum allocation (DSA) whose ability is to adapt their operation by sensing the radio

environment around it in opportunistic manner [Akyildiz et al., 2006].

This usage of spectrum holes allows sharing of the licensed bands by unlicensed or cognitive

users provided that there is no interference to the licensed users. This phenomenon of sharing is

called Dynamic Spectrum Sharing (DSS) which is the key characteristic of DSA which is

responsible for providing fairness and efficiency in spectrum access among the different users of

cognitive radio and also among licensed and unlicensed users (or Primary users and Secondary

Users). Although sharing can be either in the form of underlay using Ultra Wide Band (UWB) or

in the form of opportunistic overlay usage of spectrum holes by the cognitive radio [Nekovee,

2006]. In this research work focus is given to the overlay spectrum access and the fairness issues

concerned with it.

In case of overlay spectrum access, the cognitive users have to vacate the spectrum whenever the

legacy user wants to use it for their communication. In order to do so the CR should be able to

detect the presence of the Primary users in order to ensures noninterference to licensed or legacy

users. So the spectrum sensing should involve more sophisticated techniques than simple

determination of power in a frequency band. Also the force termination of cognitive users affects

the communication link decreasing the overall throughput and performance of the cognitive radio

users. This evokes the issue of fairness among the cognitive radio as there is a chance of force

termination of the communication by the licensed or legacy users [Zhu et al., 2007].

1.2 Objective

The main objective of this report is to give an insight into the emerging technology called

Dynamic Spectrum Sharing using cognitive radios and to discuss the fairness issues related to the

cognitive radio. The main focus of this report can be summarized as:

1. To study Dynamic Spectrum Sharing and related issues.

2. To study various overlay spectrum sharing techniques which address fairness issues in

the cognitive radio environment.

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3. To provide a basis for the further research work in the area of traffic modeling and

analysis under the Cognitive radio environment.

1.3 Report Outline

The main objective of this report is to study fairness issues under various Dynamic Overlay

spectrum sharing techniques in Cognitive Radio environment. The outline of this report is as

follow. The basic introduction is given in Chapter 1. Chapter 2 discuss the details of Dynamic

Spectrum Sharing Cognitive Radios and provide different traffic models for spectrum sharing

with detailed reviews of the spectrum sharing techniques for both licensed and unlicensed

spectrums in the literature. Chapter 3 presents research methodology as well as the current status

of the project. Chapter 4 gives the analytical modeling overview which is followed by simulation

model and parameters in Chapter 5. In Chapter 6 preliminary results of the proposed simulation

test bed is discussed. Finally the conclusion is given in Chapter 7.

4

3.5.1

CHAPTER 2

LITERATURE REVIEW

2.1 Dynamic Spectrum Sharing

The need for efficient usage of spectrum has always been one of the concerned topics for

regulating bodies. With the knowledge that a majority of licensed spectrum is underutilized in

time and frequency, the concept of dynamic spectrum allocation (DSA) has been proposed as a

solution to the potential spectrum scarcity problem, where unlicensed users temporarily

“borrow” frequency bands from spectrum licensees while simultaneously respecting the rights of

the incumbent license holders. The key characteristic of DSA is Dynamic Spectrum Sharing

(DSS) which allows the legacy user to share their unused spectrum with other unlicensed users

by providing fair and efficient spectrum allocation between them [Ji and Ray Liu, 2007]. DSA

has been already employed in cellular mobile communication. But we should keep in mind that

the existing DSA schemes for general cellular networks are not suitable for a CR network.

Because the number of available spectrum is not fixed owing to fact that cognitive user uses the

spectrum form licensed users and which could change from one region to another in case of CR

networks.

For CR to utilize the underutilize spectrum, DSA networks employing cognitive radios are being

considered. In order to utilize these „Spectrum holes‟, the FCC has issued a Notice of Proposed

Rule Making [FCC I, 2003] advancing CR technology as a candidate to implement negotiated or

opportunistic spectrum sharing. Today, DSA and CR are like interchangeable terms which have

revolutionize the telecommunications industry, significantly changing the way we use spectrum

resources, and design wireless systems and services. Cognitive radios have been identified as a

key enabler for DSA networks, where the operating parameters of the unlicensed device can be

rapidly reconfigured to the changing requirements and conditions of the transmission

environment.

That‟s why there are number of projects that have their focus on DSA architecture where the CR

operates autonomously. Defense Advance Research Project Agency (DARPA) xG Program in

USA [DARPA] is one of the most motivated projects for DSA based on CR technology.

2.2 Types of Dynamic Spectrum Sharing

The DSS functions in a way such that it tries to sense the spectrum looking for spectrum

opportunity. In case there is no primary user, the device identifies the spectrum opportunity and

makes the legacy spectrum available to the licensed users and at the same time avoiding

interference. There are two techniques for spectrum sharing as defined in [Berlemann et al.,

2005]:

2.2.1 Underlay Spectrum Sharing

This method of spectrum sharing provides access to the radio spectrum to those systems that

have minimal transmission powers. The signals are emitted over a large band of spectrum so as

5

to put the unwanted signal power that might be caught by serving licensed radio devices below

the designated threshold level. The transmission power being limited in order to reduce

interference, the shared spectrum can be used if the interference is contained below the threshold.

For this purpose, FCC has defined space between the original noise floor and the licensed signal

of the incumbent radios called the “new opportunities for spectrum use”. Spread spectrum,

Multi-Band Orthogonal Frequency Division Multiplex (OFDM) or Ultra-Wide Band (UWB) are

examples of this kind of technique.

2.2.2 Overlay Spectrum Sharing

In this method, the network is accessible only when the spectrum is unoccupied by licensed

users. Due to this, only a small fraction of the radio spectrum is available as open frequency band

for unlicensed users and even this availability is difficult to trace. So, for the identification of

under-utilized spectrum without affecting other legacy radios, flexible spectrum access

techniques are implemented using CR. This kind of opportunistic spectrum access to under-

utilized spectrum is referred as overlay spectrum sharing.

2.3 Cognitive Radio

Cognitive radio (CR) is a paradigm for wireless communication in which either a network or a

wireless node changes its transmission or reception parameters to communicate efficiently

without interfering with licensed users. This alteration of parameters is based on the active

monitoring of several factors in the external and internal radio environment, such as radio

frequency spectrum, user behavior and network state.

The idea of CR was first introduced officially in the article by Joseph Mitola III and Gerald Q.

Maguire, Jr. (1999). Mitola described it as a particular extension of Software Define Radio with

learning and reasoning capabilities that works in application layer and higher [Mitola III, 2000].

Since CR is a new concept and it means different thing to different audiences, the FCC view is

quite simple and global to follow. According to [FCC II, 2003] “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”.

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 user‟s

communication. If we scan the portions of radio spectrum we would find that some frequency

bands are largely unoccupied most of the time while some other frequency bands are only

partially occupied and the remaining frequency bands are heavily used [Haykin, 2005]. This

leads to a term called spectrum holes which is defined in [P. Kolodzy et al., 2001, cited in

Haykin, 2005] as “a band of frequencies assigned to a primary user, but, at a particular time and

specific geographic location, the band is not being utilized by that user”. Thus from these

theories we can formulate that in order to improve the spectrum utilization more efficiently, there

should be some access technique which will allow the secondary users to access the spectrum

holes which are unoccupied by the licensed or primary user at a particular location and time as

shown in Figure 2.1. Figure 2.1 merely shows the availability of the spectrum holes and also

shows how the cognitive users or secondary users are moved to another spectrum hole when

there is need of the spectrum band by the primary users. In doing so the cognitive radios alters

6

the power level so that to minimize the interference that may be caused due to secondary users

[Akyildiz et al., 2006]. Thus we see 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 capture 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 more efficiently.

“Spectrum Holes”

Time

Power

Frequency

Spectrum in Use

Dynamic

Spectrum

Access

Figure 2.1: Spectrum Hole Concept

( Adapted from Haykin, 2005 )

The Federal Communications Commission (FCC) has identified in [FCC III, 2005] the

following features that cognitive radios can incorporate to enable a more efficient and flexible

usage of spectrum:

Frequency Agility – The radio is able to change its operating frequency to optimize its use

in adapting to the environment.

Dynamic Frequency Selection (DFS) – The radio senses signals from nearby transmitters to

choose an optimal operation environment.

Adaptive Modulation – The transmission characteristics and waveforms can be

reconfigured to exploit all opportunities for the usage of spectrum

Transmit Power Control (TPC) – The transmission power is adapted to full power limits

when necessary on the one hand and to lower levels on the other hand to allow greater

sharing of spectrum.

Location Awareness – The radio is able to determine its location and the location of other

devices operating in the same spectrum to optimize transmission parameters for increasing

spectrum re-use.

7

Negotiated Use – The cognitive radio may have algorithms enabling the sharing of spectrum

in terms of prearranged agreements between a licensee and a third party or on an ad-hoc/real-

time basis.

CR is an intelligent radio which is capable of listening to the outer environment which uses

methodology of understanding by learning the environment in order to adapt its internal states to

the requirements by changing the statistical variables like transmit power, modulation schemes,

carrier frequency to provide more reliable and efficient spectrum access. For this CR needs to be

programmed as it is not possible to change the hardware to do so. This is where the software

defined radio (SDR) comes into play.

SDR is the basis for the cognitive radio which was also introduced by Mitola in his dissertation.

At this point it is relevant to discuss the issues related to software radio (SR) and software

defined radio (SDR). SR is a radio which directly samples the antenna output but in case of SDR

receive signals are sampled after appropriate band filtering. That is why the SDR is considered as

practical version of SR since the analog to digital converter that can be employed in SR is not

realizable till today [Jondral, 2005]. But for future developments this direct digitization should

be the major goal for the researchers as it will increase the efficiency of the sampling.

On the other hand a cognitive radio (CR) is an SDR that is capable of sensing its environment,

tracks changes, and reacts upon its findings. We can consider a CR as an automatic unit which

frequently exchanges information with the networks accessible to it and also with other CRs in

the vicinity of each other. From this we can say that a CR is a redefined SDR [Jondral, 2005].

2.4 Issues of Cognitive Radio

Although cognitive radio opened a new dimension in spectrum access and spectrum sharing,

there are some issues which should be acknowledged in order to implement the cognitive radio

practically. There are two challenges associated with cognitive radios:

Identification of and Protection of Primary Users

Coexistence and Fairness issues in Spectrum Sharing

2.4.1 Identification and Protection of Primary Users

The cognitive radios (i.e. Secondary Users or unlicensed users) are considered to have lower

priority than the primary users (i.e. licensed users) for accessing the spectrum. And they use the

spectrum whenever the primary users are not using it which is considered as spectrum

opportunity for cognitive users. Spectrum opportunities can be detected if the cognitive radio can

detect the primary users such that in doing so it can have the information of the spectrum which

are not being used by the primary users. Also they have to vacate the spectrum whenever the

primary user reappears in the scenery within some time interval. That is why one of the

fundamental requirements of the cognitive users is to avoid interference to potential primary

users in their vicinity. Therefore, cognitive radios should be able to detect Primary Users

correctly so as to know the presence of spectrum opportunity through continuously sensing.

Detecting these spectrum opportunity is not an easy task and there are numerous technique for

detecting it.

8

Since the Primary system allows the use of the unused spectrum to cognitive users, it should be

kept in mind that doing so the primary system should not be change. This is another issue

regarding the stability of the primary system. In order to retain the integrity of the primary

system, spectrum pooling can be used. Spectrum pooling is another innovative strategy which

can be used for spectral sharing, where all the spectral range from different spectral owner is

brought together into a common pool. The idea was to host this common pool by any licensed

system and let the secondary user to temporarily use the spectrum resources when the licensed or

primary user was idle. Doing so the licensed or primary system remains unchanged but other

secondary user can then access the spectrum pool whenever the primary users were idle. To put

in a simpler way, spectrum pooling is a collection of free spectral bands of licensed users that

can be accessed by the unlicensed users without destroying the transmission quality of the

licensed system. It is actually a resource sharing strategy, which allows a license owner to share

unused part of his licensed spectrum with the unlicensed user, until he needs it himself.

However, the license owner has the absolute priority to access the shared spectrum. This means,

the secondary user has to monitor the channel and extract the channel allocation information

(CAI), i.e. it has to detect, which parts of the shared spectrum the owner system accesses to, in

order to immediately vacate the frequency bands being required by the licensed owner and to

gain access to the frequency bands, which the license owner has stopped using. For this the

secondary system needs to be highly flexible which is achieved by using Orthogonal Frequency

Division Multiplexing (OFDM). A spectrum pooling architecture based on Orthogonal

Frequency Division Multiplexing (OFDM) has been presented by Weiss and Jondral [Weiss and

Jondral, 2004].

There are some techniques which have already been proposed for reliable detection of the

primary users. According to [Akyildiz et al., 2006], for detecting spectrums opportunity,

spectrum sensing can be classified as transmitter detection, cooperative detection, and

interference based detection.

2.4.1.1 Transmitter Detection

Transmitter detection is based on the detection of weak signal from a primary user transmitter

through local observation. The approach uses the concept of beaconing in which the primary user

broadcasts beacon in order to signal the availability of the licensed spectrum for cognitive user

usage. The beacon can permit or deny the usage of spectrum. So the usage of licensed spectrum

by the cognitive users is controlled and there is no need to detect the spectrum opportunity by the

cognitive radio. However, this method may not be reliable due to the fact that the beaconing

signal may not reach the cognitive user [Nekovee, 2006].

Other detection methods include match filter detection, energy detection and cyclostationary

feature detection [Cabric et al., 2004]. Match filters maximizes the received signal to noise ratio

and are thus suitable for primary user detection. However, it requires information of the

demodulation parameters like modulation type, order and pulse shaping formats beforehand in

order to be able to detect and decode the signal. Since the cognitive radio needs prior

information, dedicated receivers are required for primary users and this is the main drawback of

this approach.

9

The energy detection method averages the frequency bins of a Fast Fourier transform to detect

the presence of use. The drawback of this simple approach was that it was unable to differentiate

between modulated signals, noise, and interference [Cabric et al., 2004].

The cyclostationary feature detection exploits the fact that modulation offered to any random

stationary process results in built in periodicity [Cabric et al., 2004] as a result of which

parameters can be set in cognitive radio to detect the primary users. These modulation signals are

termed as cyclostationary since their static mean and autocorrelation exhibits periodicity. Auto

correlation function can be used to detect any background noise since noise is a wide sensed

stationary signal without correlation. Thereffore, cyclostationary feature is better than energy

detector at detection.

2.4.1.2 Cooperative Detection

The performance of Transmitter detection techniques is limited by received signal strength which

may be severely degraded due to multi-path fading and shadowing. In most of the case the

primary users are separated from the cognitive users so there is no interaction between them. So

transmitter detection methods cannot avoid the interference caused by the lack of primary

receiver information. Also the transmitter detection methods cannot prevent the hidden terminal

problem which takes place when a cognitive radio cannot detect all of the radios with which it

might interfere because some of the radios are hidden from it [Akyildiz et al., 2006].

That is why new approaches are required to exploit for higher sensing and detection capabilities.

One method can be using the information of other cognitive radios in cooperative manner to

reliably detect the primary users. Analytical and simulative results reveal that the mentioned hard

requirements on the detection probability can only be fulfilled with a cooperation diversity

approach. Such cooperative sensing by exchanging sensing information between cognitive

radios, have a better chance of detecting the Primary User compared to individual sensing [Guo

et al., 2006].

Therefore, individual 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

multiple cognitive radio users is included for primary user detection. Hence, cooperative sensing

provides more accurate result because the uncertainty associated with a single user‟s detection

can be minimized. In [Brodersen et al., 2004] a centralized cooperative network called

CORVUS is proposed where the cognitive users forms 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

which then use to alert the cognitive user about the primary user reappearance. The problem in

cooperative detection method is that it is difficult to combine the results of all the secondary

users because different secondary users have different sensitivities and sensing times. And they

also cause adverse effect in the network due to additional operations and overhead traffic.

10

2.4.1.3 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 takes place at

the receivers 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 for measuring

interference, known as interference temperature was proposed by the FCC in 2003 [FCC IV,

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.

The measurement of an interference temperature limit provides a worst case depiction of the

radio environment in a particular frequency band and at a particular geographic location such

that the receiver could operate satisfactorily. In other words, the cognitive radio must be able to

estimate the interference temperature that the primary user can tolerate. 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 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 [Brodersen et al., 2004]. After 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 considered to be harmful if it

increases the noise floor above the interference temperature limit and if the interference

temperature is not exceeded in a frequency band then that frequency band is made available for

the use of secondary users.

2.4.2 Coexistence and Fairness issues in Spectrum Sharing

The fairness here means that the cognitive radio users have to terminate their communication

when the primary user reappears. This evokes the issue of fairness among the cognitive radio as

that can degrade the overall performance of the cognitive users [Zhu et al., 2007]. Even though

there are many techniques to sense the primary or legacy users in order to minimize the forced

termination of the cognitive radio, there are still some issues as how to manage the sharing

efficiently and fairly. Another challenge in DSA or CR systems is the existence of the different

cognitive radio systems in the network which constantly try to access the spectrum leading to

unfair spectrum sharing them also. As different cognitive users will be competing for the same

spectrum holes, there can be fairness and optimization issues regarding resource utilization

among the cognitive users due to heterogeneity among themselves [Wang et al., 2007]. There is

no guarantee of fair resource allocation among different cognitive users. So there should be a

level of coordination and cooperation between both primary users and secondary users. There

should be cooperation between the secondary users and also between the primary and secondary

users so as to access the spectrum opportunities effectively. For this a set of etiquette rules

should be defined so that the cognitive and primary radio system can operate in cooperative

manner. These etiquette rules should be made fair in order to promote effective sharing of

spectrum among devices. Spectrum etiquette is a set of rules for radio resource management to

be followed by radio systems that operate in an unlicensed band. It defines the rules for the

behavior of radio system in order to achieve fairness in access to shared radio resources and for

11

efficient usage of the spectrum. Although spectrum etiquette does not define a protocol or a

algorithm for doing so. As a matter of fact each radio system can apply its own algorithm within

the constraints of the spectrum etiquette. So basically spectrum etiquette provides a framework

for behavior for radio system which may restrict the degree of freedom for management of radio

resources by each radio system [Mangold and Challapali, 2003]. So the spectrum etiquette rule

is generally defined before any communication link is established.

2.5 Significant Work in Overlay Dynamic Spectrum Sharing

This report is more concentrated toward overlay spectrum sharing techniques where the

unlicensed users share the spectrum with the legacy users without interfering with the legacy

users maintaining fairness among the cognitive users.

Some of the approaches are discuss here in order to formulate the base of this report. They are:

2.5.1 Spectrum Pooling

Spectrum pooling is another innovative strategy which can be used for spectral sharing, where all

the spectral range from different spectral owner is brought together into a common pool. The

idea of spectrum pool was first introduced in [Mitola III, 1999], where depending on physical

characteristic of the spectrum, Mitola III defines four spectrum pools that allows to realize this

very concept of spectrum pooling. The four spectrum pools were [Mitola III, 1999], very low

band (26.9 - 399.9 MHz), low band (404 - 960 MHz), mid band (1390 - 2483 MHz) and high

band (2483 - 5900MHz). Depending on bandwidth requirement, propagation distance and other

traffic characteristics one of these pools can be used.

The idea was to host this common pool by any licensed system and let the secondary user to

temporarily use the spectrum resources when the licensed or primary user was idle. Doing so the

licensed or primary system remains unchanged but other secondary user can then access the

spectrum pool whenever the primary users were idle without destroying the transmission quality

of the licensed system. It is actually a resource sharing strategy, which allows a license owner to

share unused part of his licensed spectrum with the unlicensed user, until he needs it himself.

However, the license owner has the absolute priority to access the shared spectrum.

[Capar et al., 2002] defines spectrum pools as “contiguous spectrum, which can be used by

renter process (=cognitive users). According to [Capar et al., 2002], these pools can be grouped

into pool group which supports the same kind of application. The pool can be characterized by

its bandwidth Bi which can be divided into equal bandwidth sub bands bi. Then the maximum

number of communication links n = . The renters can use these subbands as long as the

licnesed user does not need it. If the licensed user needs the subbands the renter user or cognitive

user are using then the renters has to leave the subbands within the time interval Tp. Depending

upon the re appearance of the licensed users, the renter process (=cognitive users) have to cease

their transmission which mean deteorating their mode of communication. So reappearance of

licensed users can be model in two ways [Capar et al., 2002].

1. Controlled access scheme: where the licensed users searches for the free spectrum within its

frequency range. Being the legacy user, it has the right to reclaim the spectrum used by the

12

unlicensed or cognitive users who are operating within the same band. In doing so licensed

users must be able to detect the presence of the cognitive users and possibly even to

communicate with them. The communication of the renters (=cognitive users) can persist as

long as the there are channel for the licensed users to process for. That means if all the sub

bands are full then the licensed user can occupy the sub bands used by the renters.

2. Uncontrolled access scheme: Where the licensed users have no knowledge of whether a sub

band is occupied by a renter (cognitive users) or not, so it treats all the sub bands as free sub

bands. In this case the renter‟s process can be forced to be terminated or interrupt even if

there are other free bands available.

For analyzing both the cases, these processes were modeled as Markov model with Poisson

arrival rates of λP and λC for the owner process (=licensed users) and renters (=cognitive users)

process with service rate of µO and µR respectively. The number of sub bands was assumed to be

m which was supposed to be share by both the users. The Markov model for both case are shown

in Figure 2.2 and Figure 2.3 respectively.

.

0,0 0,1

λC

µC

λC

2µC

0,n-10,j 0,n

λC

jµC

……... ……...

(j+1)µC

λC λCλC

(n-1)µC nµC

1,0 1,1 2µC 1,n-11,jjµC…….. ……...

(j+1)µC(n-

1)µC

λP

λP

λpλPλpµP

2µP

µPµP

λpλp

2µP 2µP

i,0 i,1µC 2µC

i,jjµC

……...

(i+1)µP

λp

λp

iµP

iµP iµP

(i+1)µP

……

……

……

λP

n-1,0 n-1,1

λC

µC

(n-1)µP(n-1)µP

n,0

nµP

……

……

Cognitive Process, j

Pri

ma

ry P

roce

ss,

i

µP

λPλC

µC

λP

λP

λP

λP

λC

……

……

λC

λC λC

λC λC λC

λP

λP

λP

λP

λP

λP

i is for Active Primary

Users

j is for Active Cognitive

Users Arrival rate of Primary λP

µP Termination rate of Primary

λC Arrival rate of Cognitive

µC Termination rate of Cognitive

Figure 2.2: Markov model for Controlled Channel Assignment

( Adopted and Modified from Capar et al., 2002 )

Since licensed user does not know whether the renter processes are occupying the pool or not so

it treats all the pool as free in case of uncontrolled access scheme. Due to this the renter process

13

may have to terminate forcefully with the rate even if other spectrums are free.

This means that the forced termination of the renter process is possible even if the system is not

fully loaded which tends to increase with the increase of the licensed users in the system. Unlike

controlled channel scheme, the forced termination can occur in every state except the state (i,0),

i = 0,1….n. Clearly we can say that the forced termination is much higher in this case than the

controlled channel assignment.

Also it should be kept in mind that the forced termination is independent of the arrival rate λC in

uncontrolled access scheme case as the termination of the renter process depends on the size of

the licensed process which is independent of λC.

0,0 0,1λC

µC

λC

2µC

0,n-10,j 0,nλC

jµC

… ……...

(j+1)µC

λC λCλC

(n-1)µC nµC

1,0 1,1 2µC 1,n-11,jjµC…….. ……...

(j+1)µC(n-

1)µC

λPµP

2µP

µPµP

2µP 2µP

i,0 i,1µC 2µC

i,jjµC

……...

(i+1)µP

iµPiµP

iµP

(i+1)µP

….

……

……

n-1,0 n-1,1

λC

µC

(n-1)µP (n-1)µP

n,0

nµP

……

……

Cognitive Process, j

Pri

ma

ry P

roce

ss, i

µP

λPλC

µC

λP

λP

λP

λP

λC

……

……

λC

λC λC

λC λC λC

i is for Active Primary

Users

j is for Active Cognitive

Users

Arrival rate of Primary λP

µP Termination rate of Primary

λC Arrival rate of Cognitive

µC Termination rate of Cognitive

λFi,j

Force Termination Rate of

Cognitive Users

λFi,j

=

λPi,j

= λP

in

j

in

jin

λP

λF0,j

λF

0,1

λP0,1

λF0,2

λP

0,j

λF0,j+1

λF

0,n-1 λ P

0,n

-1

λF0,n

λF1,1

λP

1,1

λF1,j

λFi-1,j+1

λP1,j

λPi-1 , j

λF1,n-1

λFi-1, 1

λPi-1, 1

λFi-1, j

λFi, 1

λFn-2,1

λPi, 1

λPn-2,1

λFn-2, j+1

λFn-1, 1

λFi, j

Figure 2.3: Markov model for Uncontrolled Channel Assignment

( Adopted and Modified from Capar et al., 2002 )

14

For analysis the bandwidth utilization (BU) is calculated which is define as the ratio of the

expected value of the occupied bandwidth to the total available bandwidth [Capar et al., 2002].

( 2.1 )

Where β1 is the random variable which describes the occupied bandwidth. With BU the

utilization of the spectrum can be measured if the spectrum pooling is used. And also there can

be probability that the renter process which wants to use the pool may be denied as all the

spectrum is being used by the licensed user. This probability is measured as probability of

blocking for renter process which is given as [Capar et al., 2002]

( 2.2 )

Where (i, j) represents the number of active renter and owner processes, respectively, p(i,j) is the

state probability for the corresponding state and m is the number of available channels. The

blocking probability for both the case was found to be almost same. But it was shown that the

forced termination probability is more in case of uncontrolled access scheme than in controlled

access scheme.

2.5.2 Prioritized Primary Access for Spectrum Sharing

An overlay spectrum sharing scheme considering the access priority of Primary or licensed user

is shown in [Tang et al., 2006]. That means the licensed user have the priority and also it uses

uncontrolled access scheme to access the spectrum. This means that when primary users wished

to use the spectrum, which is used by the secondary users for their transmission, then the

secondary users have to cease their transmission in short span of time which results to secondary

drop call. Also there can be secondary block calls where the secondary users needs the spectrum

but cannot access as it is being used by others. This is modification of the work done by [Capar

et al., 2002] which provides a detail study about the call blocking probability and call dropping

probability of cognitive or secondary users in free access scheme for various cases and also

propose a simple reservation scheme to reduce the call dropping probability.

A three dimensional Markov chain is used to dimension the spectrum sharing of primary and

secondary users with Poisson arrival process with mean rates λP and λS respectively with

negative exponential service time distribution of 1/µP and 1/µS respectively. The state in the

model is denoted as (i, j, k) as shown in Figure 2.4.

15

Figure 2.4: Three Dimensional Markov Chain Model

( Adopted and Modified from Tang et al., 2006 )

In the given Figure 2.4, i and j represents the number of primary and secondary users

respectively and k indicates the events, where

„0‟ denotes no collision of primary users and secondary users,

„1‟ denotes occurrence of drop call,

„2‟ denotes a block call state.

The steady state probability is denoted as P (i, j, k) and is calculated using the cut equation and

using

( 2.3 )

The secondary drop call and block call probability are calculated as [Tang et al., 2006]

16

( 2.4 )

( 2.5 )

Different analysis were made by varying λP, λS, µP, and µS to calculate call blocking and drop

call probability of the secondary users to find the best capacity performance of the secondary

users. It was shown that by controlling the number of secondary users admitted in the system by

using the notification messages, and reducing the average holding time of the secondary system,

both the blocking and dropping probability can be reduce almost to zero. As less holding time

means that most likely the secondary users can complete their transmission in time before

licensed users reappears, call blocking probability and call drop probability is more at initial

decreases with further decrease in the average holding time of the secondary users.

The capacity of the secondary system which is define as the maximum secondary users arrival

rate which satisfy the GoS requirement is given as [Tang et al., 2006]

( 2.6 )

Where pb is the maximum allowable blocking probability and pd is the maximum allowable call

dropping probability.

The notation is to denote the arrival rate of the secondary users with

condition on the blocking probability satisfied.

The notation is to denote the arrival rate of the secondary users with

condition on the dropping probability satisfied.

In order to further reduce the call dropping probability, a simple channel reservation technique is

presented in [Tang et al., 2006] where the primary users are allowed to reserve some channel in

advance only so that it can be used for future purpose. This indeed reduces the dropping

probability of the secondary users as they are not interrupted by the primary users but the

blocking probability may increase as the consequence. According to [Tang et al., 2006], an

optimal reservation channel can be found to increase the overall capacity performance of the

secondary users system.

For this primary system are allowed to reserve R (out of N) channels for their future access. So

that means this scheme limits the number of secondary users to N-R. Until the number of primary

users in the system is below the value R, the newly arrive primary users continues to use the

reserve channels. But as soon as the number of primary users are more than the value of R then

the primary system controller randomly allocates the channel to the newly arrive primary users.

This can be implemented by using primary base station to send notification messages to block

secondary users from using the reserve channels if there are any. The value of R must be chosen

such that it should increases the overall capacity performance of the secondary users

17

The modified Markov chain model is shown in Figure 2.5.

0,0,0 λS

µS

Pri

mar

y U

sers

(i)

Secondary Users (j)

1,(N-R),21,(N-R),01,1,0

0,(N-R),20,(N-R),00,1,0

N,0,0

R+1,0,0

1,0,0

(R+1),0,1

(R+1),1,0

λS

2µS

λS

(N-R)µS

λSµS

λS

2µS

λPµP

λP

(R+

1)µ

P

µP µP

1

λS

1

……

……

µP

Pλ

i-N

j-iN

Pλ

i-N

j

1

λSµS

N,0,1

N,0,2

λS

1

1

i denotes no. of primary users

j denotes no. of secondary users

K denotes the event

N no. of Bands

R no. of Reserve bands

Drop call State

Block call State

λP

R,(N-1),2R,(N-1),0R,1,0R,0,0 λSµS

λS

2µS

λS

1……

λS

λP

……

……

……

(R+1),

(N-R-1),

2

(R+1),

(N-R-1),

0

λS

1

(R+1),

(N-R-1),

1

Pλ

i-N

j

1

……

……

……

Figure 2.5: Modified Markov Chain Model for Reservation Case

( Adopted and Modified from Tang et al., 2006 )

The capacity of the secondary system under a given primary system traffic is now given as

[Tang et al., 2006]

( 2.7 )

Where the notation is to denote that the secondary arrival rate

such that the blocking probability is satisfied for primary user having R reserve channel. They

have shown that using reservation technique the dropping probability can be reduce only thing

needed is to find the optimal value of R which will give a larger secondary system capacity.

2.5.3 Spectrum Handoff using Optimal Channel Reservation for Cognitive Radio

Another Markov chain model for cognitive radio access in licensed bands using spectrum

handoff is presented in [Zhu et al., 2007] where forced termination probability, the blocking

18

probability and system throughput is calculated in order to achieve higher throughput. In a radio

network where both the primary users (licensed users) and cognitive users (unlicensed users) are

coexisting, the cognitive users need to let go those bands of primary spectrum whenever the

primary users needs to use them. And when this occurs the cognitive users may lose their

communication link and sometimes have to forced terminate the communication link.

Alternatively, the cognitive users can sense idle sub-bands and reconstruct the communication

links to them without terminating the current communication. This is called a spectrum handoff

[Zhu et al., 2007]. This reallocation of band can either be performed by the base station centrally

or by the cognitive radios through suitable distributed protocols.

The spectrum consists of M primary bands and each primary band is divided into N sub-bands.

According to [Zhu et al., 2007], the cognitive user uses channel A1 to ANM while the primary

users uses channels B1 to BM. Both the bands overlap each other. Also the primary users have the

priority to use the spectrum whenever they want even the sub-bands used by the cognitive users.

So that means the primary users are in control of the usage of the spectrum. The process of the

spectrum occupation is modeled as a continuous Markov chain with Poisson arrival processes for

both the cognitive and primary users with arrival rates λa and λb respectively. The service time are

assumed to be exponentially distributed with rates µa and µb respectively. The states are describe

as an integer pair (i,j), where i is the total number of sub-bands used by cognitive users and j is

the total no. of primary bands used by the primary users. The Markov chain model for the

spectrum handoff using r reservation channel is shown in Figure 2.6.

Figure 2.6: Rate diagram of state (i, j) with Spectrum Handoff and Channel Reservation

(Adapted from [Zhu et al., 2007])

So this means that as long as there are idle sub-bands, forced termination will not take place.

Thus for state (i,j), if i+jM ≤ (N-1)M, forced termination will not occur with the arrival of a

primary user; otherwise forced termination will move states (i,j) to state ((M-j-1)N, j+1) with

transition rate of λb.

19

When r channel are reserved to assist the spectrum handoff, it was shown that probability of

forced termination can be further reduced. This case is model as in Fig 2.6 (b). Here the

transition rate diagram of state (i,j) with r reserved sub-bands is shown. Moreover the cognitive

users are blocked only when the current bandwidth occupancy (i+Nj) plus r equals the total

bandwidth NM.

The blocking probability and the forced termination probability with r reserved channels are

[Zhu et al., 2007]

( 2.8 )

( 2.9 )

Using Equation ( 2.8 ) and Equation ( 2.9) PB ( r ) and PF ( r ) can be trade off by adjusting the

value of r.

The optimal r was selected where the throughput ρ(r) was considered to be maximum for the

cognitive users. Throughput is defined as the average number of service completion per second,

and is given as [Zhu et al., 2007]

( 2.10 )

Using these equations the optimal r can be calculated.

2.5.4 Spectrum Sharing Among the Cognitive Users

As cognitive user‟s uses the spectrum of licensed used in an opportunistic manner, there can also

be fairness issues among the cognitive radio due to dissimilarities between them. [Xing et al.,

2006] addresses the problem of unfairness among the cognitive radio in an open spectrum

wireless network in terms of airtime share. Open spectrum means the band of frequency for

which no license is needed for its usages making it more efficient and mobile. Spectrum sharing

is also supported by open spectrum as different radio system can coexist. Also the radio systems

can dynamically use and release spectrum as per the requirement inside the given radio

environment. There is a special kind of radio which can achieve this usage called Spectrum

Agile Radios (SARA). SARA can help to minimize unused spectral bands in the open spectrum

networks.

In order to achieve fairness among these radios, an etiquette rule can be defined which defines

the rules for the behavior of radio system in order to achieve fairness in access to shared radio

resources and for efficient usage of the spectrum. So basically spectrum etiquette provides a

framework for behavior for radio system which may restrict the degree of freedom for

management of radio resources by each radio system [Mangold and Challapali, 2003].

20

In [Xing et al., 2006], fairness issue is considered taking into account two types of radio systems

A and B where type A operate on three frequency channels (center frequencies), and the radio

systems of type B operate on nine frequency channels (center frequencies) as shown in Figure

2.7. Type A radio system can be considered as broad band radio systems and B as narrow band

radio system. By considering a simple Markov chain model they have shown that the broad band

radio system i.e. type A radio system is always dominated by narrow band type B radio system.

The arrival rate is model as Poisson random process with rate λi for radio system i, and the inter

arrival time is negative-exponentially distributed with mean time 1/λi ms. The radio system

access duration is also negative-exponentially distributed with mean time 1/µi ms, so the

departure of the radio system is another Poisson random process with rate µi.

Figure 2.7: Frequency Channel use by two different types of radio systems A & B

(Adopted from [Xing et al., 2006])

For analysis of the system following assumptions were made [Xing et al., 2006]

The radio systems only scan their own frequencies, for example, a radio system A, with

center frequency f2 looks only in its frequency and not any other frequency.

Every radio system requires its respective frequency to be idle before allocating the radio

resources, otherwise it will be dropped, i.e., no queuing allowed.

If there is collision between the radio systems due to the detection of same idle channel by

more than one radio system, one of the radio systems is randomly selected to allocate the

radio resource, the other radio systems are dropped.

Two of the most representative etiquette rules defined in [Mangold and Challapali, 2003] are as

follows.

Rule 4: A radio system of type A or type B should apply LBT when operating.

Rule 6: In order to protect other radio systems most efficiently, a radio system B that follows

Rule 4 should synchronize its LBT process in time across neighboring frequency channels

that overlap with the same A channels.

Equal traffic load is considered for each type of radio system with same occupation time. So for

simplicity one of the type A radio systems and the three type B radio systems are assumed whose

21

required spectrum is within the type A radio system‟s spectrum range. This spectrum range is

referred to as reference range. Also since collisions rarely happen especially with low traffic

load, in this Markov model, collision state is omitted. From this Markov model the probability of

being in state i, Pi are calculated and average Airtime per radio is also calculated. Airtime refers

to the ratio of allocation time per radio system type to the reference time ( say 1 hrs) [Mangold

and Challapali, 2003].

( 2.11 )

It was seen that the airtime for type B radio system were high then the airtime for type A system

which means that if the traffic load of the radio system B increase then radio system type A goes

to 0 which is not fair as for the part of type A being the broader band radio. According to [Xing

et al., 2006] it was seen that for the radio system following the given LBT rule, the narrow

bandwidth radio system B always dominates the airtime share over the broad band radio system

A.

Also they have shown that if queuing is introduces then the airtime share of the broad band

system increase while the airtime share of narrow band radio system is not affected so much.

This model was again characterized by Markov chain model as shown in Figure 2.8 where each

state is represented as (k1, k2) and ki equals to number of radio system i on the spectrum block.

The kiw means the number of waiting radio system of type i. With queuing the airtime share for

type A is fairly increased.

Figure 2.8: Markov Chain to Model the Unlicensed Spectrum Access Process with waiting

( Adopted and Corrected from Xing et al., 2006 )

22

Further they show that if the radio system were allowed to follow the Rule 6 i.e. a narrow radio

system (here B) that follows Rule 4 should synchronize its LBT process in time across

neighboring frequency channels that overlap with the same reference channels, then it is seen

that the broad band radio system are protected and thus the airtime share also increase. But the

payoff is that the airtime share of the narrow band (Here B) reduces significantly.

Another way to increase the efficiency in spectrum access is to pack all the radio systems tightly

together in spectral domain so that there are no spectral holes or white spaces in between them.

According to [Xing et al., 2006], “packing” behavior is describe as process where if one radio

spectrum releases the spectrum, the other radio spectrum will switch the operating frequency

band so that the vacant band is occupied as per the policies defined in that particular SARA.

Then a new accessing radio system scans to find spectrum opportunities from the beginning of

the spectral band and occupies the first idle spectrum opportunity it finds. In this way the entire

spectrum holes are packed by the neighboring radios and all the spectrum fragments can be

saved for the future accessing radio systems. For example, when radio system B2 releases the

spectrum, another radio system B4 or B3 then switch to the vacant frequency f5 as shown in

Figure 2.9. Doing so the spectrum are packed and there is sequential idle spectrum for other

radio system to access for. When there is a new accessing radio say A2 (which needs 3

frequencies), then it can occupy frequency f7, f8 and f9. Without SARA, it would have to queue

or be dropped. Although, all this switching may introduce signaling overheads and delays in the

system. So to overcome, these spectrum bands can be divided into blocks, and then the packing

can be then implemented in each block reducing the switching and scanning procedure.

f1 f2 f4 f7f5 f6f3 f8 f9

B1 A1 B4B3B2

f1 f2 f4 f7f5 f6f3 f8 f9

B1 A1 B3B4

Release

Switch

f1 f2 f4 f7f5 f6f3 f8 f9

B1 A1 A2B3B4

Occupy

Frequency Frequency

Frequency Figure 2.9: Packing Behavior Example

( Adopted from Xing et al., 2006 )

A Homo Egualis (HE) model is proposed in order to model the access scheme of both the radio

system which takes into account the fact that individual shows inequity aversion i.e. individual

are ready to give up something in order to have more equitable outcomes. They have shown that

in HE model the subject suffers more inequity when they are doing worse than other. This HE

23

was modeled using the utility function of player i, ui in an n0 player game as shown in eq 4.3.1

[Gintis, 2000 cited from Xing et al., 2006]

( 2.12 )

α = envy factor, β = guilt factor

Where player‟s pay off and the second term are measures the utility loss while

doing worse than other and the third term measures the loss while doing better than others [Fehr

and Schmidt, 1999]. And 0≤ β <1 and α ≥ β which exhibits that in HE society model the subject

suffer more inequity when they are doing worse than other. In order to use this concept, it was

assume that each radio system competes for the spectrum with probability pi which they learn by

themselves.

Also for the player pay off is defined where onlinetime is average cumulative

“ON” spectrum time per radio system of type i. That is this is the time that particular radio

system is online or uses the spectrum where

Li = weight fairness = θi λi

θi = priority parameter

λi = traffic load for type i radio system

The probability pi is updated as

( 2.13 )

Where n is the number of different radio system types, 0 < βi < αi reflects the fact that radio

system exhibits strong urge to reduce inequality when doing worse than other. This forces each

radio system to access spectrum in fair manner. The only thing needed now is the radio system

own history of the onlinetime and also the onlinetime of other radio systems in order to keep

record of the busy time of the required spectrum. These records are used in order to access the

spectrum opportunistically. Record of busy time of required spectrum is kept so that when there

are more than two radio system trying to access the same spectrum the priority can be given to

those owing to the use of that spectrum previously. Each radio system can access only its own

historical records and their respective λi. Then only the priority parameter is needed to be

broadcast by the radio system to access the spectrum.

It was shown that the fairness achieved by assuming this model for both the radio system was

almost equal and was much high than any other scheme proposed [Xing et al., 2006].

24

2.6 Discussion

This section covers a brief discussion, comparison and analysis of the traffic models mention in

chapter 4. There are various overlay approaches for spectrum sharing and some of them are

discussed in this report. For this radios like CR and SARA are used which uses flexible spectrum

access techniques for identifying under-utilized spectrum and to avoid harmful interference to

other radios using the same spectrum. Such an opportunistic spectrum access to under-utilized

spectrum, whether or not the frequency is assigned to licensed primary services, is referred as

overlay spectrum sharing.

It was seen that the secondary users (or unlicensed or cognitive users) must be able to find the

spectrum opportunities in order to make use of the licensed spectrum. The secondary users can

use the spectrum as long as it is not required by the licensed users. But they have to cease the

usage of the spectrum when the primary user tries to access the very spectrum they are using it.

This reappearance of the primary users makes the secondary users to be terminated (or drop)

forcefully after some negligible delay. The force termination (or drop call probability) has

always been center of focus in any communication system because it is the probability with

which the ongoing communication links are forced to terminate.

[Caper et al., 2002] shows the channel access scheme for the primary users based on certain

level of priority. According to them there are two possibilities: controlled and uncontrolled

access scheme and they have shown that the for reliable communication and lower force

termination probability, controlled access scheme is best where the secondary users can persist as

long as there are other free channels for the primary users and later is that primary users have no

knowledge of the presence of the secondary users and treats the channel used by secondary users

like a free channel.

Also [Tang et al., 2006] is modification of the work done by [Caper et al., 2002] which

provides a detail study about the blocking probability and dropping probability of secondary

users in free access scheme for various cases and also propose a simple reservation scheme to

reduce the call dropping probability. It was shown that by controlling the number of secondary

users admitted in the system by using the notification messages, and reducing the average

holding time of the secondary system, it was seen that more secondary users were able to

complete their transmission and the both the blocking and dropping probability was reduce

almost to zero

In [Zhu et al., 2007] a simple channel reservation technique together with spectrum handoff is

shown to reduce the force termination probability with slight increase in blocking probability. It

was shown that even though there is slight increase in blocking probability, the overall

throughput of the traffic increases.

Also according to [Nekovee, 2006], today most of the research on cognitive radio is being

carried out in operation of these radios in open spectrum. Open spectrum is defined as the band

of frequency for which there is no need of license to use it and no restriction in accessing it. But

25

the utilization is restricted as unlimited number of users are sharing the same Open (or

unlicensed) spectrum. Spectrum usage is allowed to all devices that satisfy certain technical rules

or standards (like limitation of transmission power or advanced coexistence capabilities) in order

to mitigate potential interference.

In [Xing et al., 2006], overlay spectrum access scheme of a narrow band radio (eg Bluetooth)

and broad band radio (eg WiFi) has been studied. They have shown that the operation of these

radios under simple Listen-before-talk rule results in unfair allocation of airtime where narrow

band radio gets the largest airtime share. Also a modified access protocol is also presented based

on Homo Egualis (HE) model which uses utility function in decision making step together with

inequality aversion to achieve fairness in air time share. They have also shown using this

protocol with spectrum agile radio provides superior airtime performance. Due to this today

spectrum agile radio are considered to be one of the best options for Dynamic Spectrum Access

in open wireless networks.

2.7 Comparison

The various access schemes which are mentioned above are classified and compared in the table.

Table 2.1 can give in brief the approach and analysis of different access scheme which was cover

in this report in Chapter 4.

Table 2.1: Comparison of different spectrum Overlay Approaches

Method and Approach Measurement

Parameters

Results Conclusion and

Comments

1. Controlled and Uncontrolled

spectrum access using spectrum

pooling: [Caper et al., 2002]

Case I: Controlled where SU can

persist as long as there are other

idle channel for PU

Case II: Uncontrolled where the

PU has no knowledge of presence

of the SU in the system and treats

all channels as free.

Blocking

probability

(Pblock),

Force

termination

probability

(Pforce) and BU

Case I has low blocking

Probability and Force

termination probability

(PForce ≤ 1% or 0.01)

Case II has also low

blocking probability but

high force termination

rate since PU does not

know SU exists and can

take the channel used by

SU (PForce ≥1% or 0.01)

In this approach it was

found that blocking

probability and BU are

almost same for both

case but the force

termination is much

higher in case II.

Reliability of the

uncontrolled uses can

be achieved if

intelligible

rescheduling is used

for secondary process.

26

Method and Approach Measurement

Parameters

Results Conclusion and

Comments

2. Spectrum Sharing with

Prioritized Primary Access:

[Tang et al., 2006]

Overlay approach for spectrum

sharing is used to find the overall

capacity performance of SU in

free access scheme and simple

reservation scheme to reduce call

blocking and dropping

Probability.

Blocking

probability

(Pblock), and

Dropping

probability

(Pdrop) for

determining

GoS

Various cases for the free

access scheme is consider

and was found that that

the call blocking and

dropping probability is

lowest in the case when

the holding time of the

SU were reduce.

Also by using reservation

scheme the overall drop

probability is reduce but

blocking probability is

increased slightly

By the use of a simple

reservation technique

the drop call

probability can be

reduce but due to

reservation there is

slight increase in the

blocking probability.

Optimal value of R

can be found such that

there is balance in

block call and drop

call probability

3. Optimal channel reservation

[Zhu et al., 2007]

In this approach a channel

reservation scheme for CR

spectrum handoff is proposed to

achieve higher throughput and

also to trade off the forced

termination and blocking

probability

Blocking

probability

(PB( r )),

Force

Termination

probability

(PF( r ))and

Traffic

throughput

It was shown that with

the use of spectrum hand

off the force termination

probability can be reduce.

Similarly using optimal

reservation the force

termination can be

reduced significantly.

There is significant

reduce in force

termination probability

with the introduction

of reservation with

very small increase in

Blocking probability.

This is one advantage

of this approach. The

overall traffic

throughput is high for

the spectrum handoff

and reservation case.

4. Dynamic Spectrum access in

Open Spectrum Networks

[Xing et al., 2006]

In this various overlay models

like simple Markov model

without queuing and queuing,

Homo Egualis (HE) model and

spectrum access using SARA

(Packing Behavior) for providing

fairness in a narrow band and

broad band radio systems are

discussed. They have shown that

the fairness can be improved for

the given radio system

Airtime share

which is ratio

of allocation

time per radio

system type to

reference

time.

It was shown that using

etiquette rule 6 with the

simple LBT (rule 4) the

broad band system can be

protected at the price of

reduction in airtime share

of narrow band systems.

Further they show that

the approach based on

HE society can provide

good fairness than using

the etiquette rule 6 alone.

Finally they have shown

Over all it was shown

greater fairness in

airtime can be

achieved if the radio

systems were allowed

to switch their

frequency which

enables them to pack

themselves in the

spectral domain.

27

that using SARA together

with HE model greater

airtime fairness and lower

blocking probability can

be achieved

From the above comparison we can say that for increasing the capacity performance of the

secondary users and also to decrease the force termination probability, the prioritize spectrum

sharing discuss in section 2.5.2 is better since it uses priority based access scheme and also uses

the channel reservation technique to limit the number of secondary users in the system. But as I

have mentioned before, the force termination of the secondary users can be minimized

significantly if intelligent rescheduling of the secondary user could be made possible which

allows them to reschedule their transmission when they are forced to terminate by the primary

users. For this the secondary users must be able to sense and hop between the different

frequencies available in the spectrum group. This hopping can be defined as spectrum hopping.

Also in case of the open spectrum access scenario where the radio systems are free to do

anything, spectrum agile radio together with HE protocol can have significant increase in airtime

share for the broad band radio system without degrading the other narrow band radio systems.

It was seen from this that if the CR or any radio system sharing the frequency spectrum either in

licensed or unlicensed bands, somehow switch their frequency in such a way that they are able to

use other frequency slots while they are communicating then greater fairness and capacity

performance can be achieved.

28

CHAPTER 3

RESEARCH AND DEVELOPMENT

Traffic modeling and analysis of any network help dimension the network more properly. Also it

is interesting to study different behavior of access schemes in a time dependent environment by

analyzing the performance and utilization of the radio resources. This research study has been

my base and motivation for my work entitled “Performance and Fairness study of Overlay

Spectrum Sharing in Cognitive Radio Environment”. The study of the traffic transitions and its

adaption in different overlay schemes helps understand more about its behaviors which can be

used to achieve the desired performance and fairness among the cognitive users.

The main objectives of this research work can be summarized as:

1. To study the fairness issues in cognitive radio environments for different user groups --

licensed and cognitive users.

2. To develop a generic simulation platform for studying various overlay spectrum sharing

techniques for measuring the performance of different cognitive users in terms of

utilization of the radio resource, loss and airtime share.

3. To observe and compare the performance of different cognitive radio system in a time

dependent environment by analyzing the arrival rates, holding time and process

occupation model using the proposed simulation model.

4. To study the temporal traffic behavior of the cognitive users in a time dependent

environment.

3.1 Methodology

The main concern of this research work is to study performance and fairness issues regarding

different overlay spectrum sharing in a cognitive environment by constructing a simulation

environment for it. In a dynamic spectrum access network, there are multiple unlicensed users

which are allowed to access the unused spectrum bands of the primary users or licensed users

without disturbing the usage of the primary users as shown in Figure 3.1. The primary users are

denoted by P1 and P2 and the unlicensed or cognitive users are denoted as A and B. These

cognitive users can be narrow band users or broad band users depending upon their bandwidth

requirements.

29

Sen

sing

SensingBase Station

Primary User

P1

Cognitive User A

(Broad Band)

Cognitive user B

(Narrow Band)

Primay Users

Cognitive Users

Primary User

P2

Frequency Grid

Used by Primary user

Used by Narrow band cognitive radio

Used by broad band cognitive radio

Figure 3.1: System Diagram of Cognitive Radio Environment

3.2 System Model

For considering the appropriate system model, first we consider the spectrum bands of the users.

Since the unlicensed users utilizes the unused spectrum band of the licensed users so for system

model two types of users are considered in general, the Licensed or Primary user and cognitive

users both operating in same spectrum.

We consider the concept of spectrum pooling where there are n bands (or n server) and both the

primary and cognitive users share the spectrum as shown in Figure 3.2. For this research work, it

is considered that one bandwidth unit (BU) is equivalent to one band or server.

1 2 nn-1n-2

Figure 3.2: Frequency Band of two users

The system model is shown in Figure 3.3. The primary user is denoted by P and since they are

the legacy users of the spectrum, its access should not be disturbed by the operation of any

cognitive users. For this the priority to access the spectrum is given to licensed or primary users.

The cognitive users are denoted by C and have less priority than the licensed users.

30

System with “n”

bands for Licensed

or Primary users

and Cognitive users

“n” bands

Cognitive

Users, C

Pλ

Primary

User, P

Cλ

1

NP

1

NC

Service Type

Identifier

Primary

Controller

Unit

Cognitive

Controller

Unit

Admission

Control

Unit

Primary

Cognitive

Spectrum

Etiquette

Rules

Figure 3.3: The System Model

There can be NP primary users and NC cognitive users. In this research work the simulation

platform can handle two primary users and two cognitive users simultaneously with different

bandwidths. The arrival process is considered as independent Poisson arrival process with rate

for primary users and for cognitive users. The service time is considered as negative –

exponentially distributed with mean time of and for primary and cognitive users respectively.

The service type identifier identifies which type of service is trying to access the system and the

Admission Control Unit determines whether to accept the process or not depending on the

algorithm or rules defined in each of the control units. The Admission Control Unit consist of

two control units for respective processes which is responsible for controlling the sharing of the

spectrum in opportunistic manner. These rules are termed as spectrum etiquette rules which

restricts the degree of freedom of radio systems for management of radio resources by each

system [Mangold and Challapali, 2003]. In a way these rules governs the radio systems to

efficiently share the radio resources among themselves.

It is considered that the system consist of n number of bands or servers which is shared among

the primary and cognitive users using spectrum pooling concept.

This research work is concentrated on making a generic simulation platform which can

accommodate different overlay models in cognitive traffic environment which can be used to

study fairness and performance of the spectrum sharing. In this research work, four type of

overlay spectrum sharing is considered. Depending upon the access scheme of primary and

cognitive users they are classified as follow:

3.2.1 Spectrum Sharing with Uncontrolled Access Scheme

In this spectrum sharing technique, the primary users are allowed to access the spectrum pool in

uncontrolled fashion such that it doesn‟t know that the cognitive users coexist in the system.

31

Cognitive users on the other hand use the spectrum bandwidth opportunistically whenever there

is free frequency band in the system. That means the primary users have no control over the

existence of the cognitive user in the system. But primary user being the licensed user of the

system, it has the priority over the cognitive users and thus they can force terminate the cognitive

users if the cognitive users are using the frequency band needed by the primary users. More to

that due to the uncontrolled access scheme of the primary users, the cognitive user have high

probability of getting forced terminated even if there are other idle spectrum for the primary

users to use. This case is considered as the base case for this research work. An attempt is made

to reduce the force termination probability and to increase the bandwidth utilization of the

cognitive users by considering different spectrum sharing techniques.

3.2.2 Spectrum Sharing with Controlled Access Scheme

In this spectrum sharing technique, the primary users are in control of the existence of the

cognitive users in the system. The primary users force the cognitive users to terminate only when

the system is full and there are no idle spectrums available for them to use. In a way the primary

users acknowledge the presence of the cognitive user in the system. The force termination of the

cognitive user is reduced using this access scheme and hence increases the bandwidth utilization

of the cognitive users.

3.2.3 Spectrum Sharing with Cognitive Spectrum Hopping

The cognitive users are allowed to hop to another available idle frequency band whenever the

primary users need the spectrum band used by the cognitive users. In this way the cognitive

user‟s communication is not interrupted when the force termination occurs. Since the cognitive

users uses cognitive radio for communication purpose, cognitive users can sense the arrival of

the primary users and hop to new available frequency in order to finish the communication.

In a way this spectrum sharing is same as of controlled access scheme but the difference is that in

this the cognitive user alters the sharing strategy in order to complete its communication. By

doing so, the integrity of the primary users is preserved as they are the licensed user of the

system and it is not necessary for them to acknowledge the presence of the cognitive users in the

system. Hence the forced termination probability and the bandwidth utilization is same as of

controlled access scheme.

3.2.4 Spectrum Sharing with Buffering of Cognitive Users

In this scheme the cognitive users are allowed to wait in the buffer whenever they are interrupted

by the primary users or whenever they are not allowed to enter the system. So that mean

whenever there is force termination of the cognitive user then the interrupted traffic of the

cognitive user is stored in buffer and cognitive users scans the spectrum band continuously for

the idle spectrum. The retransmission starts as soon as the spectrum band becomes idle again.

Depending upon this there can be two type of buffering systems. They are:

1. Infinite Buffering System:

32

The buffer is considered to be of infinite length and with First In – First Out (FIFO) principle. If

infinite buffering is allowed then both the forced termination and blocking becomes zero

increasing the bandwidth utilization of the cognitive users. The price to pay is that they spend

infinite time in the buffer hence decreasing the efficiency of usage.

2. Finite Buffering with Impatient Cognitive Users

Impatient users are those which waits exactly time unit in the buffer before it totally gives up

for entering into the system. As cognitive users are the unlicensed users of the system, this trend

is more imminent in their behavior. This case is same as that of the case with cognitive buffering,

but the cognitive user waits for some time unit and after that they again retry to acquire the

idle spectrums. This time unit that the cognitive user waits in the buffer has a directly effect on

the force termination probability and as well as the bandwidth utilization of the cognitive users.

3.3 System Traffic Model

The traffic patterns for both primary and cognitive user are considered to be random in nature to

simulate the real life environment. Since the primary users are the legacy users of the system and

it can be of any type meaning that the source type can be finite and infinite depending upon the

nature of the primary users. Moreover the cognitive users are unlicensed users of the system

which access the unused spectrum of the primary users, there can be infinite number of cognitive

users that can access the system. So depending upon this there can be many type of system traffic

model. In this research work, the system traffic is modeled as Markov chain model with state ( i,

j) where i represents the primary users and j represents the cognitive users.

3.4 Performance Measurements

The performance of the both the primary and cognitive users are measured based on the total

number of the process served, total number of process blocked or forced terminated, their

holding time, service time and bandwidth utilization. The performance parameters considered are

briefly defined here as follows:

Blocking : Blocking for primary user refers to the case where the primary user cannot

access the spectrum band as all the available spectrum band is occupied by other primary

users. Whereas for the cognitive user, blocking is defined as the case where it cannot

access the spectrum band as the spectrum band is occupied by either primary users or

cognitive users.

Forced Termination : Forced termination occurs for cognitive users only and is

define as the case where the primary users tries to use the frequency band already in used

by the cognitive users. The forced termination is the count of number of incomplete

process of cognitive user due to interruption by the primary users. Forced termination

probability is measured with reference to:

o Offered calls

The forced termination is measured out of the total offered which includes the

blocked calls also.

33

o Served calls

The forced termination is measured out of the connected or served calls.

Bandwidth Utilization : Bandwidth utilization means the overall utilization of the

available bandwidth or frequency band by the primary or cognitive users.

All these parameters help to measure the Grade of Service (GoS) of both the primary and

cognitive user transmission. So in order to measure the performance of the system, different

parameters like blocking probability, forced termination probability, carried traffic, bandwidth

utilization are calculated. These raw results are then used to analyze the performance of the

system.

3.5 Performance and Fairness Analysis Methods

In order to analyze the performance and fairness of the overlay spectrum sharing in cognitive

environment both analytical and simulation methods are used. The analytical method is done in

order to verify the simulation method. The parameters considered for both the case are

summarize in Table 3.1.

Table 3.1: Different Parameters for Performance and Fairness Analysis

Parameters

Analysis Method

Analytical Simulation

Number of Servers (n) Finite Finite

Arrival Process (λ) Poisson Any

Service Time Exponential Any

Traffic Model Markov Chain Markov Chain

Bandwidth Unit (BU) 1 BU = 1 server 1 BU = 1 server

Number of Primary Source Finite Finite and Infinite

Primary Users Type 1 or 2 1 or 2

Bandwidth of Primary Users (dP) 1 or 2 1 or 2

Number of Cognitive Source Infinite Infinite

Cognitive Users Type 1 or 2 1 or 2

Bandwidth of cognitive Users (dC) 1 or 2 1 or 2

34

3.5.1 Analytical Method

For the validation purpose, analytical methods are done for small cases to calculate the state

probability and performance parameters like blocking probability, forced termination probability,

carried traffic and bandwidth utilization of each service type. The complexity of the analytical

method grows as we increase the number of servers. In this research work, generalize traffic

model was developed for the mentioned access schemes. For the sake of verification, those

traffic model are solved for n = 1 and n = 2 servers. Detail description is given in Appendix B.

3.5.2 Simulation Method

For simulation of this traffic model, event based simulation model is developed using C and C

++ language. The simulation model is capable of performing analysis for very large number of

servers with finite and infinite number of primary and cognitive user.

35

CHAPTER 4

ANALYTICAL MODEL

The different overlay spectrum sharing are model as Markov Chain model in order to verify the

simulation model. It is assumed that there is a spectrum pool of number of frequency band

which is shared by both the primary and cognitive users. The arrival rates for the primary users

and cognitive users are considered to be Poisson process with arrival rate

respectively. The service time distribution is exponentially distributed with mean rate of

respectively. An attempt is made to calculate the general flow equations for each of the different

Markov chain models. The analytical models of the four cases are solved to some extend in order

to validate the simulation model.

4.1 Markov chain Model for Uncontrolled Access Scheme

For numbers of frequency bands available for the primary and cognitive users, the

uncontrolled access scheme is modeled as Markov chain model as discuss in [Capar et al.,

2002] section Error! Reference source not found.. The state is represented as where

epresents the number of active primary users and represents the number of active cognitive

users in the system. But in this case the blocking for primary is also considered so as to make the

case more realistic. The state diagram is same as shown in section Error! Reference source not

ound. Error! Reference source not found..

The state probabilities are calculated by numerical solution using Matlab by generating the

infinitesimal generation matrix. Once the state probabilities are calculate the blocking for

primary and cognitive users, forced termination probability and bandwidth utilization can be

calculated.

4.2 Markov chain Model for Controlled and Cognitive Spectrum hopping

As mentioned in section Error! Reference source not found., in this access scheme the primary

sers are in control of letting the cognitive user to coexist in the system. Also model wise the both

control and cognitive spectrum hopping access scheme are same. Both the access scheme is

modeled using same Markov chain model with infinite source as discuss in section Error!

eference source not found.. The primary losses are considered in this case also and similarly as

in case of the uncontrolled access scheme we can calculate the state probability using numerical

solutions using Matlab. Once the state probabilities are calculate the blocking for primary and

cognitive users, forced termination probability and bandwidth utilization can be calculated.

4.3 Markov chain model for Spectrum sharing with Cognitive Buffering

In this case the cognitive users which are interrupted are made to wait in some buffer and the

service is resumed as soon as there are idle servers available. Also the cognitive users which are

not allowed to enter the system are made to wait in the buffer until any idle servers are available.

The buffer is infinite in structure and follows FIFO principle. The state diagram is represented by

36

three integer pair where is for the active primary users, is for active cognitive users

and represents the number of cognitive waiting in the system. Figure 4.1 shows the state

diagram for controlled Access scheme with infinite buffer.

0,0,0 0,1,0

λC

µC

λC

2µC

0,n-1,00,j,0 0,n,0

λC

jµC

……... ……...

(j+1)µC

λC λCλC

(n-1)µC nµC

1,0,0 1,1,0

2µC

1,n-1,01,j,0jµC

…….. ……...

(j+1)µC (n-1)µC

λP

λP

λpλP

λpµP

2µP

µPµP

λpλp 2µP2µP

i,0,0 i,1,0

µC 2µC

i,j,0

jµC

……...

(i+1)µPλp

λp

iµP

iµP iµP

(i+1)µP

……

……

……

λP

n-1,0,0 n-1,1,0

λC

µC

(n-1)µP (n-1)µP

n,0,0

nµP

……

……

Cognitive Process, j

Pri

ma

ry P

roce

ss, i

µP

λP

λC

µC

λP

λP

λP

λP

λC

……

……

λC

λCλC

λC λCλC

λC

λP

λC

λC

λC

λP

0,n,1

1,n-1,1

µP

1,n-1,2

µP

nµC

λC

2µP

i,j,1

nµCjµC

λC

……

λP 2µP

……

λP 2µP

λp iµP

nµC nµC

λC

1,n-1,2 1,n-1,3

λC

nµC

λpiµP λp

iµP

λp λp

nµC

……...

λp (i+1)µPλp (i+1)µP

λp (i+1)µP λp (i+1)µP

……...

……...

……...

……

……

……

nµC

λC

nµC

λC

nµC

λC

nµC

n-1,1,1

nµC

λpiµP

λC λC

i,j,1

nµCnµC

λC

λp iµP

λC

1,n-1,2 1,n-1,3

λC

nµC

λpiµP λp

iµP

nµC

……...

λp nµP λp nµPλp

nµP λpnµP

……...

λC

nµC

n,0,1

nµC

λC λC

n,0,2

nµCnµC

λCλC

n,0,n-1 n,0,n

λC

nµCnµC

……...

λC

nµC

λC

……... n,0,n+1

nµC

λP λP λP λP λP

……...

……...

λC

nµC

i is for Active Primary

Users

j is for Active Cognitive

Users

k is for the waiting of

cognitive users

Arrival rate of Primary λP

µP Termination rate of Primary

λC Arrival rate of Cognitive

µC Termination rate of Cognitive

i,j,k Represents waiting state

λp nµP

Figure 4.1: State Diagram for Controlled Access Scheme with Buffering

37

CHAPTER 5

SIMULATION MODEL

5.1 Overview

The simulation platform models the spectrum sharing schemes for various types of traffic

generated by primary and cognitive users. The flow diagram of the simulation is shown in Figure

5.1.

Traffic

Generation

Performance

Parameters

Calculation

Spectrum

Etiquette

Rules

Until

t ≤ T

Admission

Control

Unit

Primary

Control

Unit

Cognitive

Control

Unit

Server

Allocation

Unit

Input

Parameters

Figure 5.1: Simulation Model

The simulation is done on event basis meaning that at particular given time there can be only one

event in the system. The event can be arrival or Termination of the primary user and cognitive

users or the forced termination of the cognitive user. Depending upon the event, different

algorithms or rules are implemented in order to access the available spectrum or bands.

5.2 Simulation Flow chart

The overall flowchart of the simulation program is shown in Figure 5.2. The detail blocks are not

shown in this flow chart which can be seen in the Appendix A.

38

Figure 5.2: Simulation Flow chart

39

5.3 Input Parameters

In order for the simulation to start certain input parameters should be set for generating the traffic

and also for considering the spectrum etiquette rule to be applied. The list of input is summarized

in the Table 5.1. Depending upon the system, these parameters can be set where „0‟ means false

and „1‟ means true for 0/1 option.

Table 5.1: Input Parameters

Parameters value

Any

Number of Repetition Any

Simulation time Any

Observation start time Any

Number of Servers (n) Finite number

Number of Source (N) Finite/Infinite

Primary Arrival Process Any

Primary Service Time Any

Cognitive Arrival Process Any

Cognitive Service Time Any

Bandwidth of Primary Users (dP) 1 or 2

Bandwidth of cognitive Users (dC) 1 0r 2

Queuing Enable/Disable

5.4 Traffic Generation: Arrival and Termination Processes

Traffic generation block generates processes based on arrival process and service time which

will be used as input to the system. This block is responsible for generating initial process and as

well as random arrivals or terminations of both the radio systems or forced terminations of the

cognitive users.

5.4.1 Arrival Process

In this report, the simulation model can handle Poisson arrival process with rate for primary

users and for cognitive users. The inter arrival time, holding time and service time are

generated randomly to make the simulation more realistic. The inter arrival time is calculated as

( 5.1 )

5.4.2 Service Time

For the verification purpose, the spectrum access duration is assumed to be negative –

exponentially distributed with mean time of and for primary and cognitive users respectively.

That means the departure rate of primary and cognitive users are and respectively.

Although in simulation, uniform and constant service time distribution can also be considered.

40

5.5 Spectrum Etiquette Rules

In order to achieve fair share of the spectrum, rules should be define for their access scheme.

These rules must be defined in order to coordinate the sharing of the system resource for

achieving fairness and efficiency in spectrum usage. These rules can be defines as spectrum

etiquette rules which basically provides a framework for behavior for radio system which may

restrict the degree of freedom of radio systems for management of radio resources by each radio

system [Mangold and Challapali, 2003]. The rules considered in this work are:

1. The access scheme of Primary users can be controlled so as to minimize the forced

termination of the cognitive users.

2. All the cognitive users must use listen before talk (LBT) rule. This rule helps to distribute

the control of radio access among cognitive users.

3. Cognitive users can perform spectrum handoff. That means cognitive users are allowed to

move to another available idle bands when the primary user tries to use the spectrum

band used by the cognitive users.

4. Cognitive users are allowed to wait until the desired spectrum is free for their access.

5.6 Admission Control Unit

For analyzing the different access schemes depending upon the spectrum etiquette rule defined,

the admission control unit manages the arrival and termination of the process for both the uses

type. In order to do so the admission control unit generates traffic models depending upon the

spectrum etiquette rules defined. For simulation purpose the traffic model is modeled as Markov

chain model for analysis of different overlay spectrum schemes. There are independent control

units for the primary and cognitive users which controls the behavior of the radio system.

Depending upon the decision made by these control units the available server is assign to

respective processes.

5.7 Performance Parameter Calculation

In order to measure the performance of the system, different performance parameters like

blocking probability, forced termination probability, carried traffic, bandwidth utilization are

calculated. The simulation measurements are performed as shown in Figure 5.3.

t1 = 0

Simulation

Start

∆t ∆t∆t∆t∆t

1 2 3 Nt2 = ∆t

Observation

Start

Total Simulation Time, T

Total Observation Time, Tobs

tstop = t1 + N∙∆t

Simulation

Stops

41

Figure 5.3: Statistical Data measurement in Simulation Model

The total simulation time is defined as T which is divided into N numbers of small ∆t time units.

The simulation starts at t1 = 0 whereas the measurements are taken after time t2 = ∆t. For each ∆t

time period, statistical data such as number of offered process, block process, served process, and

forced terminate process are measured and then the performance parameters like blocking

probability, forced termination probability, carried traffic, bandwidth utilization are calculated.

After N repetition, the average of the measured data is calculated.

5.7.1 Holding Time Measurement

The holding time of each radio system is measured for each ∆t as well as for overall simulation

period T. The holding time is calculated only for those processes which are able to complete their

process within time ∆t as shown in Figure 5.4.

t = t1 t = t1 + ∆t

∆t

tmp1 tmc1 tmc2 tmp2

Primary Arrival

Cognitive Arrival

tmc3 tmp3

Figure 5.4: Measurement of Holding Time

The holding time for the primary users is defined by as

( 5.2 )

Similarly the holding time for the cognitive users is defined by as

( 5.3 )

5.7.2 Service Time Measurement

The service time for each ∆t is measured as shown in Figure 5.5. The service time for the

primary and cognitive users are denoted as and respectively.

42

∆t

t = t1 t = t1 + ∆t

tsp1 tsp2tsc1 tsp2 tsp4

t1 = 0

Simulation

Start

tsc2

Primary Arrival

Cognitive Arrival

Figure 5.5: Measurement of Carried Traffic

5.7.3 Performance Parameter Calculation

The performance calculation for the given system is calculated as shown in the Table 5.2. Here

represents the type of radio systems.

Table 5.2: Performance Parameter Calculation

Parameters Calculation

Blocking Probability

Forced Termination Probability

Carried Traffic

Bandwidth Utilization

servers

5.8 Validation of Simulation Model

The simulation model for each access scheme is validated by comparing the result with the

purposed analytical solutions. However for the access scheme with cognitive buffering is

validate by validating the program flow consistency with an Erlang delay system (M/M/n). In all

the cases the result obtain was almost same as that of the analytical solutions. The detail

calculation is given in Appendix B.

43

5.8.1 Simulation Parameters

For running the simulation efficiently, certain simulation parameters are set in the beginning of

the simulation like the observation period, total simulation time, small observation time and

number of repetition. For validating the simulation model following parameters are considered.

These are summarized as shown in Table 5.3

Table 5.3: Initial Simulation Parameters

Parameters value

1000 min

Number of Repetition 10

Simulation time 10000 min

Observation start time 1000 min

The simulation parameters as shown in Table 5.4 are used to validate the Erlang loss system for

varying offered traffic.

Table 5.4: Simulation Parameters to Validate Erlang Loss System

Parameters value

Number of Servers (n) Finite

Arrival Process (λ) Poisson

Service Time Exponential

Bandwidth Unit (BU) 1 BU = 1 server

Primary Users Type 1

Bandwidth of Primary Users (dP) 1 or 2 BU

Cognitive Users Type 1

Bandwidth of cognitive Users (dC) 1 or 2 BU

Similarly for validating the different access scheme, the following parameters as shown in Table

5.5 are used.

Table 5.5: Simulation Parameters to Validate Different Access Schemes

Parameters value

44

Number of Servers (n) Finite

Number of Source (N) Infinite

Primary Arrival Process Poisson

Primary Service Time Exponential

Cognitive Arrival Process Poisson

Cognitive Service Time Exponential

Bandwidth Unit (BU) 1 BU = 1 server

Primary Users Type 1

Bandwidth of Primary Users (dP) 1 BU

Cognitive Users Type 1

Bandwidth of cognitive Users (dC) 1 BU

Queuing Infinite

45

CHAPTER 6

PRELIMINARY RESULTS

6.1 Study of Different Access Schemes

In this research work, the simulation model is capable of handling the spectrum etiquette rules

shown in Table 6.1.

The buffering or waiting of the cognitive users in the buffer is optional that is it can be enabled

or disable depending upon the input parameters. So with buffering of the cognitive users all

together the simulation model is capable of handling four types of traffic model. These access

schemes are then analyzed in order to study the performance and fairness issues of the cognitive

users in a cognitive environment. Each system is simulated for different number of servers

(Spectrum Bands) with constant primary offered traffic with varying cognitive offered traffic in

order to compare the performance of these entire schemes on a flat line.

Table 6.1: Different Access Scheme Considered for Simulation

Case Type of Access Scheme Buffering

1 Uncontrolled Access Scheme for Primary users Optional

2 Controlled Access Scheme for Primary users Optional

3 With Spectrum Hopping for Cognitive users Optional

The performance of these systems in terms of blocking , forced termination probability

and bandwidth utilization are compared to observe the influence of different

access schemes

6.2 Performance Analysis of Different cases

The performance analysis of the different cases was performed taking into account small cases

with 1 server up to big system of 100 servers. Depending upon the number of servers (or

frequency band) available, the analysis is divided into three categories. They are

1. Small Systems

With servers or frequency slots (n) ranging from

2. Medium Systems

With servers or frequency slots (n) ranging from

3. Big systems

46

With servers or frequency slots (n) ranging from

For each system, the behaviors of the cognitive radio are studied by varying its offered traffic

over a constant primary offered traffic. For this the holding time of both the users are made

constant and only the cognitive arrival rates are varied for a particular case. Simulation was

performed for different case of primary offered traffic from low to high traffic for the systems

mentioned above. These runs were made in order to see the behavior of the cognitive radio

system in different primary offered traffic. The simulation results for 1 server with primary offer

traffic of 0.2 Erlang are shown in Figure 6.1.

(a)

(b)

47

(c) (d)

Figure 6.1: Performance Analysis of 1 server with 0.2 Erl Primary Offered Traffic

It is seen from the Figure 6.1(a) that for low primary offered traffic of 0.2 Erl, the blocking for

primary radio system is not so high whereas the blocking for the cognitive radio systems

increases with the increase in its offered traffic. The force termination probability out of offered

decreases as the offered traffic increase as shown in Figure 6.1(b). The reason for this is simply

as the cognitive offered traffic increases, more of them are blocked and consequently the ratio of

the incomplete ones is less than the total offered ones. Whereas the force termination probability

out of served ones is constant as the number of cognitive users which will be served will always

be constant as shown in Figure 6.1(c). The overall and individual bandwidth utilization is shown

in Figure 6.1(d) and we can see that bandwidth utilization of the cognitive user‟s increases with

the increase in the cognitive offered traffic.

(a)

(b)

48

(c) (d)

Figure 6.2: Performance Analysis of 1 server with 0.4 Erl Primary Offered Traffic

Figure 6.2 shows the analysis for 1 server with primary offered traffic of 0.4 Erl. It can be seen

that with increase in the primary offered traffic there is increase in the force termination

probability of the cognitive users which results in the low bandwidth utilization also.

Similarly the simulation is perform for 2 server system with primary offered traffic of 0.4 Erl and

the results are shown in Figure 6.3. We can see that there is significant growth in the bandwidth

utilization of the cognitive users as its offer traffic increases with not much increase in the force

termination probability. The control and the one with hopping have less force termination

probability in that case.

(a)

(b)

49

(c)

(d)

Figure 6.3: Performance Analysis of 2 servers with 0.4 Erl Primary Offered Traffic

With increase in the primary offered traffic to 0.7 Erl, there is increase in the force termination

probability of the cognitive user as the primary user tries to get more and hence there is decrease

in the bandwidth utilization of the cognitive users as shown in Figure 6.4 (d).

(a)

(b)

50

(c)

(d)

Figure 6.4: Performance Analysis of 2 servers with 0.7 Erl Primary Offered Traffic

For medium systems, if the primary offered traffic is low then the force termination of the

cognitive user is also low and hence this increases the bandwidth utilization of the cognitive

users. As seen in Figure 6.5 (a), the blocking probability of the cognitive users increases as the

offer traffic increases. The force termination probability for the uncontrolled case shows unusual

pattern such that as the offered traffic increase the force termination for that case decreases as

shown in Figure 6.5 (b) and (c) . This is due to the fact that in uncontrolled access scheme, the

primary users hits the spectrum randomly and if the number of server or frequency slots

increases then for the primary users with constant arrival rate and holding time, the probability of

hitting the cognitive decreases. That is there is high probability of hitting the empty spectrum.

But in case of the controlled and hopping case the force termination probability increase as the

offered traffic increases due to the fact that in those case the primary users first tries to hit the

empty spectrum and if there are no empty spectrum then only it tries to force terminate the

cognitive users. There is not so high drop rate in this case and the bandwidth utilization is quite

high for the cognitive users.

51

(a)

(b)

(c)

(d)

Figure 6.5: Performance Analysis of 5 servers with 1 Erl Primary Offered Traffic

Similarly for the case with primary offered traffic of 2.5 Erl, the force termination increases and

the bandwidth utilization also decreases as more primary radio systems tries to occupy the

available radio resources. But we can see that even though there is increase in the primary

offered traffic, there is not so much decrease in the bandwidth utilization of the cognitive users.

This shows that in medium or bigger systems the coexistence of the cognitive users is more

likely to occur then in small systems. Although there is increase in the cognitive blocking but it

can be minimize by letting the cognitive to buffer in the system until it is served.

52

(a)

(b)

(c)

(d)

Figure 6.6: Performance Analysis of 5 servers with 2.5 Erl Primary Offered Traffic

Similarly, for a system of 20 servers or frequency slots, the performance parameters are shown in

Figure 6.7. The blocking for both the primary and cognitive is not so high which implies much

more cognitive traffic can be served to the system with not much increase in the force

termination probability.

53

(a)

(b)

(c)

(d)

Figure 6.7: Performance Analysis of 20 servers with 10 Erl Primary Offered Traffic

Figure 6.8 shows the simulation results for system with 20 servers with 14 Erl of primary offered

traffic. There is not much blocking for both the users and the force termination for the control

case is almost low as 20 %. This again shows that for bigger systems the primary users doesn‟t

have much effect to the cognitive users in terms of force termination or bandwidth utilization.

Although the uncontrolled access scheme has quite high force termination probability but this

54

can be overcome by simple change in strategy as controlled access scheme or if the cognitive

users are allowed to hop whenever they are forced to terminate.

(a)

(b)

(c)

(d)

Figure 6.8: Performance Analysis of 20 servers with 14 Erl Primary Offered Traffic

For really big systems, when the offer primary traffic is moderate, the blocking for both the users

are almost zero or less than 1%. Since there is such a low blocking, the force termination of

offered and of served are almost the same. Also it is quite low for the control access scheme. The

55

bandwidth utilization of cognitive users increase with increase in the offer traffic and it really

can hold high cognitive traffic as shown in Figure 6.9.

(a)

(b)

(c)

(d)

Figure 6.9: Performance Analysis of 50 servers with 20 Erl Primary Offered Traffic

The increase in the primary traffic has direct influence in the bandwidth utilization of the

cognitive users. But since there is not much blocking together with not so high force termination

56

in case of controlled or hopping case, the cognitive traffic can really be increase and the

coexistence is beneficial to both the users. The overall bandwidth utilization can be up to 80 to

90 % depending upon the traffic offered for both the users. Figure 6.10 to Figure 6.12 shows this

kind of trend for the cognitive users.

(a)

(b)

(c)

(d)

Figure 6.10: Performance Analysis of 50 servers with 30 Erl Primary Offered Traffic

57

(a)

(b)

(c)

(d)

Figure 6.11: Performance Analysis of 100 servers with 40 Erl Primary Offered Traffic

Thus we can see that as the number of the available spectrum slots or servers increases, there is

more chance for the cognitive system to coexist with the primary radio systems. The

performance of the cognitive radio system gets better when the system is big. More servers mean

more opportunity for the cognitive users to use the available spectrum. Whereas if the system is

small, then there is less opportunity for the cognitive users to coexist with the primary users.

58

(a)

(b)

(c)

(d)

Figure 6.12: Performance Analysis of 100 servers with 75 Erl Primary Offered Traffic

These initial runs shows that coexistence of the cognitive users is catastrophic when the system

is really small and in the other hand for the medium and big system there is really high

probability for coexistence of both the users.

59

6.2.1 Effect of Buffering of Cognitive users in different Access Schemes

Buffering of the cognitive users means that they are allowed to wait in the buffer whenever they

are interrupted by the primary users and also when they cannot be served instantly as all the

available spectrum band are occupied by either primary or cognitive users. The priority is given

to those which are interrupted in the buffer. Basically two types of buffering are considered.

They are:

1. Infinite Buffering System:

If the buffer or queue is of infinite size i.e. it can hold as many as it can then all of the cognitive

users can be served. But the mean waiting time in the buffer increase as the offered traffic

increases. For all the cases with buffering the performance parameters are almost same as the

buffer size is infinite. Figure 6.13 shows the simulation results for 1 server with cognitive

buffering. Since the buffer is infinite, there is no blocking and no force terminations in this case

as all are served eventually.

(a)

(b)

(c)

(d)

60

Figure 6.13: Performance Analysis of 1 server with 0.4 Erl Primary Offered Traffic with

buffer

Buffering of the cognitive user allows reducing the blocking and forcing termination probability

of the cognitive users. Random simulation results for small and medium system are shown in

Figure 6.13 to Error! Reference source not found.. We can see that with the introduction of the

cognitive buffering the bandwidth utilization almost increases by double. The only price the

cognitive users have to pay is infinite time in the queue.

(a)

(b)

(c)

(d)

Figure 6.14: Performance Analysis of 2 servers with 0.7 Erl Primary Offered Traffic with

buffer

61

The buffering case is studied in small systems as it has profound effect in these systems. For big

system also the buffering can be allowed to really control the blocking and force termination

probabilities of the cognitive users.

2. Finite Buffering with Impatient Cognitive Users

In this case the cognitive user waits in the buffer for exactly τ time units before they give up.

That means the cognitive users scans for the idle spectrum for τ time unit and gives up after that

time unit. These types of customer are called impatient customers and since cognitive users are

the unlicensed users, this nature is observable in them. In this research work, this very nature is

studied for different give up rate of these impatient cognitive users for a moderate system of 5

servers with primary offered traffic of 2.5 Erl. The simulation result are shown in

(a)

(b)

62

(c)

Figure 6.15: Performance of Cognitive Users for different give up time

Figure 6.15 (a) shows the bandwidth utilization of the cognitive users with increase in the

waiting time unit, . As we can see, the bandwidth utilization increase as increases and as

all the cognitive process in the buffer are served. The price is the waiting time is infinite.

Figure 6.15 (b) and (c) shows the relation of the forced termination probability with the give up

time of the cognitive users in the buffer. It is seen that as the waiting time in buffer increases the

forced termination probability decreases and becomes zero when the waiting time is infinite.

6.3 Overview of Result

In this research work, different overlay access scheme are considered for the primary and

cognitive users to access the spectrum so as to increase the overall performance of the cognitive

users. The most primitive form of access strategy is the uncontrolled scheme in which the force

termination probability of cognitive use is quite high and bandwidth utilization is quite low

compare to the primary users. It was also shown that the forced termination of the cognitive

users can be decrease simply by using the controlled access scheme by the primary users. Even

though the force termination probability is reduced, there is no significant increase in the

bandwidth utilization of the cognitive users. These are studied by studying the various systems

with different traffic inputs.

Another scheme is to allow the cognitive users to change their frequency band to the available

idle bands whenever the spectrum band used by them is needed for the primary users to transmit.

This is a simple strategy which can be applied for the uncontrolled access scheme where the

cognitive user performs spectrum hopping by sensing the primary users. This scheme is same as

of the controlled scheme where the difference is on the implementation of the technology. The

latter case is more feasible as it preserves the integrity of the primary users and the cognitive user

performs spectrum hopping using cognitive radio.

Further the forced termination probability can be reduces if the cognitive users are allowed to

wait in the buffer whenever they are interrupted by the primary users such that they continue

retransmission as soon as there is any idle spectrum band available. The buffer can hold those

cognitive users also which are not able to enter the system as there were no idle spectrum band

63

available for them to transmit. The case study was performed using different waiting time of

cognitive users in the buffer as shown in Figure 6.15. It was seen that the force termination

probability decreases as the waiting time increases and it is almost zero when the waiting time is

infinite. Similarly the bandwidth utilization increases with increase in the waiting time .

64

CHAPTER 7

CONCLUSION

The usage of the available spectrum in fair and efficient manner is one of the most discussed

issues in wireless communication. Various researches are going on to ensure effective use of this

available spectrum holes. One of the solutions is the use of Dynamic Spectrum Sharing in

Cognitive Radio Networks.

This research study report has tried to explain various overlay spectrum sharing techniques

which can be used to share the available radio spectrum in opportunistic manner without

interfering the transmission of the ongoing systems. Emphasis is given to analysis of these

overlay spectrum sharing techniques from traffic modeling aspects. An effort is made to analyze

and understand the Markov Chain modeling for the dynamic spectrum sharing among different

cognitive users using the simulation test bed.

65

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68

APPPENDIX A

SIMULATION MODEL

A.1 Arrival Process

The arrival process is assumed to be Poisson process with mean arrival rate for

primary and cognitive users. For the calculation purpose is considered where represents

either primary or cognitive user.Therefore the probability density function of exponential

distribution is given as

( A.1 )

The cumulative density function is then calculated as

( A.2 )

From this we can calculate

( A.3 )

Thus the inter arrival time is calculated as

( A.4 )

A.2 Service Time

The service time is considered to be exponentially distributed in most of the case and is used

mainly for the verification of the simulation process. But the simulation program can generate

different service time distributions depending upon the user input. Some of the detail calculation

of different service time are shown below.

A.2.1 Exponential

The probability density function is given as

( A.5 )

Where is the holding time of particular service i.e. primary or cognitive users.

The cumulative density function is then calculated as

69

( A.6 )

From this we can calculate

( A.7 )

Thus the inter arrival time is calculated as

( A.8 )

A.2.2 Uniform

For the uniform distribution the probability density function is given as

( A.9 )

Then the cumulative density function is calculated as

( A.10 )

With mean holding time

For

( A.11 )

The service time is then calculated as

( A.12)

A.2.3 Constant

For the constant distribution the probability density function is given as

( A.13 )

Then the cumulative density function is calculated as

70

( A.14 )

The service time is then calculated as

( A.14)

A.3 Flow chart of Simulation

The main core of the program flow is shown in the figure below.

Check Event_list

If

Evnt_time < ∆t

Update serve

count of P

Check

Event

ArrivalTermination

Primary(P) or

Cognitive(C) ?

Primary Cognitiv

e

If

BW_cnt<=n-

dP

Update serve

count of C

Increase state XP

XP=XP+1

Increase State XC

XC=XC+1

Increase BW cnt

BW_cnt=BW_cnt+dP

Increase BW cnt

BW_cnt=BW_cnt+dC

Primary(P) or

Cognitive(C) ?

Update

Blk

count of P

Update Blk

count of C

Decrse BW cnt

BW_cnt=

BW_cnt - dP

Decrse BW cnt

BW_cnt=

BW_cnt - dC

Primary Cognitive

Decrease

State

XP=XP - 1

Decrease

State

XC=XC - 1

Move to Next

observation ∆tYe

s

No

Scan and allocate

server

Yes

Yes

No

NoCheck for

idle servers

Update serve

count of P

Increase state XP

XP=XP+1

Yes

No

Decrease

State

XC=XC - 1

Increase Force

termination

Cnt

P = Primary Users

C = Cognitive Users

Update

Server_list

Update

Server_list

Update

Server_list

Generate New

Primary arrival,

Add to Event_list

Update

Server_list

Generate New

Primary arrival,

Add to Event_list

Update

Server_list

Generate New

Cognitive arrival,

Add to Event_list

Delete Current

Event

Delete Current

Event

Delete Current

Event

Delete Current

Event

Delete Current

Event

Check for

idle servers

Check

occupancy of

cognitive

Generate Service

Time

Generate Service

Time

Is

Buffer

Enable

Add

Conitive

into buffer

Is

Buffer

Enable

Generate Service

Time

Add

Conitive

into buffer

Yes

Is

Buffer

Enable

Is

Buffer

Enable

Add the

buffer event

to Even_list

YesYes

No No

Figure A.1: Flow Chart for core of the Simulation

71

APPPENDIX B

VALIDATION OF SIMULATION MODEL

This chapter contains the validation of the simulation model. The consistency of the program

flow is first verified using Erlang loss model and as well as using Erlang delay model for the

case of buffering of the cognitive users. Also the validation of the mentioned access scheme is

verified by comparing the results of analytical solutions.

B.1 Validation of Program Consistency

B.1.1 Using Erlang Loss System

This is validated by simulating the Erlang loss system with only one type of service in the system

i.e. either primary or cognitive user. The arrival process is considered to be Poisson process with

exponentially distributed service time. The simulation was done for different size of the system

and was compared with the theoretical values from Erlang loss table.

Table B.1: Validation for Erlang Loss System

n = 10 n = 100

Offered

Traffic

(Erl)

Theoretical Simulated

Offered

Traffic

(Erl)

Theoretical Simulated

2 0.00004 0.00000 50 0.00000 0.00000

4 0.00531 0.00638 70 0.00014 0.00040

6 0.04314 0.04473 90 0.02696 0.03501

8 0.12166 0.12260 110 0.13612 0.14775

10 0.21458 0.21816 130 0.25163 0.25849

12 0.30193 0.30112 150 0.34537 0.35131

14 0.37728 0.37488 170 0.41966 0.41613

16 0.44056 0.44100 190 0.47928 0.47860

18 0.49348 0.49281 210 0.52800 0.52619

20 0.53796 0.53562 230 0.56848 0.56758

72

(a) For number of server n = 10

(b) For number of server n = 100

Figure B.1: Validation for Program consistency with Erlang Loss System

B.1.2 Using Erlang Delay Sytem

For verifying the queuing system in the simulation model, the simulation model is run for 1

server Erlang Delay system with infinite queue. M/M/1 is a delay system where the offered

traffic is Poisson and the interval arrival times and service time are exponentially distributed.

Since it is a delay system, the queue size is infinite and the customer in the queue doesn‟t give

up. The general state diagram is shown in Figure B.2.

73

0

λ

µ

1+q1

λ

µ

q = 0

…………………….

λ

µ

2

λ

µ

x ≥ 1

Arrival rate λ

µ Termination rate

Figure B.2: General Erlang Delay system for n servers

The queue follows the FIFO principle. For the verification of the queuing algorithm in the

simulation, M/M/1 is calculated analytically and compared it with the simulation results in Table

B.2.

Table B.2: Validation of M/M/1 System

A Dn(A) Analytical Simulated Analytical Simulated Analytical Simulated Analytical Simulated

0.2 0.2 1.2500 1.2063 0.0500 0.0545 0.2 0.2042 0.2 0.2042

0.3 0.3 1.4286 1.3530 0.1286 0.1252 0.3 0.3034 0.3 0.3034

0.4 0.4 1.6667 1.7750 0.2667 0.2910 0.4 0.4059 0.4 0.4059

0.5 0.5 2.0000 2.2828 0.5000 0.5886 0.5 0.5049 0.5 0.5049

0.6 0.6 2.5000 2.5183 0.9000 0.8934 0.6 0.5856 0.6 0.5856

0.7 0.7 3.3333 3.0842 1.6333 1.5163 0.7 0.6964 0.7 0.6964

0.8 0.8 5.0000 4.8826 3.2000 3.1410 0.8 0.7951 0.8 0.7951

0.9 0.9 10.0000 9.3613 8.1000 7.8933 0.9 0.9095 0.9 0.9095

Mean Waiting Time

tw

Mean Queue Length

E (q )

Carried Traffic BW Utilization

74

(a)

(b)

(c)

Figure B.3: Validation of M/M/1 System

75

B.2 Validation for Different Access Scheme

For the validation different cases, analytical solution is done taking different number of spectrum

bands. The simulation model can consider any number of spectrum bands but for the validation

purpose two cases are solved analytically with one server and 2 servers. For either case the

arrival rate is Poisson process with exponentially distributed service time. The calculations are

made by varying the termination rate of the cognitive users.

B.2.1 With One Server

For one server (Spectrum Band) case, the Markov Chain model is almost same for all the cases

i.e. uncontrolled, controlled and with cognitive spectrum hopping. Figure B.4 shows the state

diagram for all the cases.

0,0

λC

µC

Pri

ma

ry

Use

rs (

i)

Secondary Users (j)

0,1

1,0

λP

µP

λP

λC

λC

λP

i denotes no. of primary users

j denotes no. of secondary users

Arrival rate of Primary λP

µP Termination rate of Primary

λC Arrival rate of Secondary

µC Termination rate of Secondary

Figure B.4: Markov Chain model for 1 server

Then solving analytically using the general equation as derived in section 4.1, we have

For Node (0,0) (B.1)

For Node (0,1)

(B.2)

For Node (1,0)

(B.3)

Then normalizing with

76

( B.4 )

Once the state probabilities are calculated then we can calculate the parameters like

Blocking

( B.5 )

( B.6 )

Force Termination Probability for Cognitive users

( B.7 )

Bandwidth Utilization

( B.8 )

( B.9 )

The values are calculated and shown in Table B.3 for primary users and Table B.4 for cognitive

users.

Table B.3: Validation for Different Access Scheme with 1 server for Primary Users

Blocking Carried Traffic Bandwidth Utilization

AP AC Analytical Simulation Analytical Simulation Analytical Simulation

0.4 0.8 0.2857 0.2808 0.2857 0.2840 0.2857 0.2771

0.4 0.4 0.2857 0.2856 0.2857 0.2834 0.2857 0.2896

0.4 0.26667 0.2857 0.3122 0.2857 0.2817 0.2857 0.3006

0.4 0.2 0.2857 0.2883 0.2857 0.2782 0.2857 0.2921

0.4 0.16 0.2857 0.2877 0.2857 0.2771 0.2857 0.2856

0.4 0.13333 0.2857 0.2818 0.2857 0.2790 0.2857 0.2921

0.4 0.11429 0.2857 0.2917 0.2857 0.2796 0.2857 0.2880

0.4 0.1 0.2857 0.2877 0.2857 0.2801 0.2857 0.2878

0.4 0.08889 0.2857 0.2978 0.2857 0.2839 0.2857 0.2839

Table B.4: Validation for Different Access Scheme with 1 server for Cognitive Users

77

AP AC Analytical Simulation Analytical Simulation Analytical Simulation AnalyticalSimulation

0.4 0.8 0.5455 0.5478 0.2597 0.2601 0.1299 0.1315 0.2597 0.2638

0.4 0.4 0.4643 0.4702 0.1786 0.1789 0.0893 0.0900 0.1786 0.1729

0.4 0.2667 0.4283 0.4283 0.1361 0.1379 0.0680 0.0678 0.1361 0.1337

0.4 0.2 0.3956 0.3958 0.1099 0.1126 0.0549 0.0547 0.1099 0.1059

0.4 0.16 0.3779 0.3761 0.0922 0.0950 0.0461 0.0451 0.0922 0.0922

0.4 0.1333 0.3651 0.3629 0.0794 0.0824 0.0397 0.0391 0.0794 0.0790

0.4 0.1143 0.3554 0.3541 0.0697 0.0711 0.0348 0.0350 0.0697 0.0714

0.4 0.1 0.3478 0.3444 0.0621 0.0634 0.0311 0.0314 0.0621 0.0617

0.4 0.0889 0.3417 0.3511 0.0560 0.0573 0.0280 0.0268 0.0560 0.0573

Blocking Carried Traffic Bandwidth UtilizationForce Termination

Graphs are plotted for comparison between analytical and simulated values as shown in Figures

below

Figure B.4: Validation for Different Access Scheme for 1 server

Primary users

Cognitive users

78

Figure B.5: Validation Different Access Scheme for 1 server

B.2.2 With Two Server

B.2.2.1 Uncontrolled Access Scheme

Figure B.6 shows the Markov chain model for uncontrolled access scheme for two server‟s case.

Primary users

Cognitive users

79

0,0 λC

µC

Pri

ma

ry

Use

rs (

i)

Cognitive Users (j)

1,1

0,20,1

2,0

1,0

λC

2µC

λC

µC

λP

µP

λP 2µP

µP

λC

λ P0

,1=1

/2λ

P

λF0,2

= λP

λC

λC

λF1,1

= λP

λF 0,1

=1/2λ

P

i denotes no. of Primary users

j denotes no. of Cognitive users

Arrival rate of Primary λP

µP Termination rate of Primary

λC Arrival rate of Cognitive

µC Termination rate of Cognitive

λFi,j

= Pin

j

in

ijn

λP

i,j = λP

λP

Figure B.6: Markov Chain Model For 2 Servers

From the general equation as describe in section 4.1, the node equations can be generated as

For Node 0,0

(B.10)

For Node 01

(B.11)

For Node 02

(B.12)

For Node 10

(B.13)

For Node 11

(B.14)

80

For Node 20

(B.15)

Then normalizing with

( B.16 )

Once the state probabilities are calculated then we can calculate the parameters like

Blocking

( B.17 )

( B.18 )

Force Termination Probability for Cognitive users

( B.19 )

Bandwidth Utilization

( B.20 )

( B.21 )

The values are calculated and shown in Table B.5 for primary users and Table B.6 for cognitive

users.

81

Table B.5: Validation for Uncontrolled Scheme with 2 server for Primary Users

Blocking Carried Traffic

Bandwidth

Utilization

AP AC Analytical Simulation Analytical Simulation Analytical Simulation

1 2 0.2000 0.2031 0.8000 0.8066 0.4000 0.4033

1 1.2 0.2000 0.1974 0.8000 0.8035 0.4000 0.4017

1 0.6 0.2000 0.1875 0.8000 0.7991 0.4000 0.3996

1 0.4 0.2000 0.2125 0.8000 0.8023 0.4000 0.4012

1 0.3 0.2000 0.2020 0.8000 0.8031 0.4000 0.4015

1 0.24 0.2000 0.1917 0.8000 0.8089 0.4000 0.4044

1 0.2 0.2000 0.1944 0.8000 0.8138 0.4000 0.4062

1 0.17143 0.2000 0.1967 0.8000 0.8064 0.4000 0.4032

1 0.15 0.2000 0.1784 0.8000 0.7997 0.4000 0.3999

Table B.6: Validation for Uncontrolled Scheme with 2 server for CognitiveUsers

AP AC Analytical Simulation Analytical Simulation Analytical Simulation Analytical Simulation

1 2.0000 0.4829 0.4883 0.5470 0.5506 0.2436 0.2450 0.2735 0.2753

1 1.2000 0.4243 0.4262 0.4486 0.4561 0.2019 0.2014 0.2243 0.2281

1 0.6000 0.3477 0.3493 0.3068 0.3138 0.1409 0.1321 0.1534 0.1572

1 0.4000 0.3107 0.3059 0.2324 0.2335 0.1083 0.1041 0.1162 0.1167

1 0.3000 0.2888 0.2846 0.1870 0.1839 0.0880 0.0883 0.0935 0.0920

1 0.2400 0.2744 0.2746 0.1564 0.1532 0.0742 0.0717 0.0782 0.0766

1 0.2000 0.2640 0.2693 0.1344 0.1311 0.0641 0.0640 0.0672 0.0656

1 0.1714 0.2563 0.2598 0.1178 0.1158 0.0565 0.0552 0.0589 0.0579

1 0.1500 0.2502 0.2549 0.1049 0.1030 0.0505 0.0505 0.0524 0.0515

Blocking Carried Traffic Force Termination Bandwidth Utilization

82

83

Figure B.8: Validation for Uncontrolled scheme

B.2.2.2 Controlled and Cognitive Hopping

The Markov chain model for both the controlled and with cognitive hopping is same and is

shown in Figure

0,0 λC

µC

Pri

ma

ry

Use

rs (

i)

Secondary Users (j)

1,1

0,20,1

2,0

1,0

λC

2µC

λC

µC

λP

µP

λP 2µP

µP

λC

λP

λP

λC

λC

λP

λP

i denotes no. of primary users

j denotes no. of secondary users

Arrival rate of Primary λP

µP Termination rate of Primary

λC Arrival rate of Secondary

µC Termination rate of Secondary

Figure B.9: Markov Chain Model Controlled Scheme for 2 server

From the general equation as describe in section 4.1, the node equations can be generated as

For Node 0,0

(B.22)

For Node 0,1

(B.23)

For Node 0,2

(B.24)

For Node 1,0

84

(B.25)

For Node 1,1

(B.26)

For Node 2,0

(B.27)

Then normalizing with

( B.28 )

Once the state probabilities are calculated then we can calculate the parameters like

Blocking

( B.29 )

( B.30 )

Force Termination Probability for Cognitive users

( B.31 )

Bandwidth Utilization

( B.32 )

( B.33 )

The values are calculated and shown in Table B.7 for primary users and Table B.8 for cognitive

users.

85

Table B.8: Validation for Controlled Scheme with 2 servers for Primary Users

AP AC Analytical Simulation Analytical Simulation Analytical Simulation

1 2 0.2000 0.1952 0.8000 0.8118 0.4000 0.4059

1 1.2 0.2000 0.2015 0.8000 0.7965 0.4000 0.3982

1 0.6 0.2000 0.2004 0.8000 0.7776 0.4000 0.3888

1 0.4 0.2000 0.2041 0.8000 0.8101 0.4000 0.4051

1 0.3 0.2000 0.2006 0.8000 0.7919 0.4000 0.3960

1 0.24 0.2000 0.2024 0.8000 0.8116 0.4000 0.4058

1 0.2 0.2000 0.1976 0.8000 0.8193 0.4000 0.4097

1 0.1714 0.2000 0.1949 0.8000 0.7851 0.4000 0.3926

1 0.15 0.2000 0.1945 0.8000 0.7854 0.4000 0.3927

Blocking Carried Traffic Bandwidth Utilization

Table B.9: Validation for Controlled Scheme with 2 servers for Cognitive Users

AP AC Analytical Simulation Analytical Simulation Analytical Simulation Analytical Simulation

1 2 0.5067 0.5051 0.5778 0.5618 0.2044 0.2074 0.2889 0.2809

1 1.2 0.4435 0.4397 0.4730 0.4659 0.1623 0.1589 0.2365 0.2329

1 0.6 0.3592 0.3539 0.3208 0.3330 0.1062 0.1057 0.1604 0.1665

1 0.4 0.3182 0.3339 0.2412 0.2371 0.0788 0.0815 0.1206 0.1185

1 0.3 0.2941 0.2839 0.1930 0.1958 0.0627 0.0605 0.0965 0.0979

1 0.24 0.2782 0.2810 0.1607 0.1646 0.0522 0.0542 0.0804 0.0823

1 0.2 0.2670 0.2664 0.1377 0.1352 0.0447 0.0500 0.0688 0.0676

1 0.1714286 0.2586 0.2527 0.1204 0.1241 0.0391 0.0376 0.0602 0.0621

1 0.15 0.2522 0.2404 0.1070 0.1074 0.0348 0.0385 0.0535 0.0537

Force Termination Bandwidth Blocking Carried Traffic

86

Figure B.11: Validation for Controlled scheme