SPECTRUM SHARING IN DYNAMIC SPECTRUM

ACCESS NETWORK

PROJECT REPORT

Submitted by

N. DHARMARAJ

Register No: 14MCO007

in partial fulfillment for the requirement of award of the degree

of

MASTER OF ENGINEERING

in

COMMUNICATION SYSTEMS

Department of Electronics and Communication Engineering

KUMARAGURU COLLEGE OF TECHNOLOGY

(An autonomous institution affiliated to Anna University, Chennai)

COIMBATORE - 641 049

ANNA UNIVERSITY: CHENNAI 600 025

APRIL - 2016

ii

BONAFIDE CERTIFICATE

Certified that this project report titled “SPECTRUM SHARING IN DYNAMIC

SPECTRUM ACCESS NETWORK” is the bonafide work of DHARMARAJ N

[Reg. No. 14MCO007] who carried out the project under my supervision. Certified

further, that to the best of my knowledge the work reported herein does not form part of

any other project or dissertation on the basis of which a degree or award was conferred

on an earlier occasion on this or any other candidate.

SIGNATURE SIGNATURE

Ms. K. JASMINE Dr. A. VASUKI

ASSISTANT PROFESSOR HEAD OF THE DEPARTMENT

Department of ECE Department of ECE

Kumaraguru College of Technology Kumaraguru College of Technology

Coimbatore-641 049 Coimbatore-641 049

The Candidate with university Register No. 14MCO007 was examined by us in the

project viva –voice examination held on...........................

INTERNAL EXAMINER EXTERNAL EXAMINER

iii

ACKNOWLEDGEMENT

First, I would like to express my praise and gratitude to the Lord, who has showered his

grace and blessings enabling me to complete this project in an excellent manner.

I express my sincere thanks to the management of Kumaraguru College of Technology

and Joint Correspondent Shri. Shankar Vanavarayar for the kind support and for

providing necessary facilities to carry out the work.

I would like to express my sincere thanks to our beloved Principal Dr.R.S.Kumar

Ph.D., Kumaraguru College of Technology, who encouraged me in each and every steps

of the project.

I would like to thank Head of the Department, Electronics and Communication

Dr.A.Vasuki Ph.D., for her kind support and for providing necessary facilities to carry

out the project work.

In particular, I wish to thank with everlasting gratitude to the project coordinator

Dr.M.Alagumeenaakshi Ph.D., Assistant Professor (SRG), Department of Electronics

and Communication Engineering, for her expert counseling and guidance to make this

project to a great deal of success.

I am greatly privileged to express my heartfelt thanks to my project guide

Ms.K.Jasmine M.E., Assistant Professor, Department of Electronics and

Communication Engineering, throughout the course of this project work and I wish to

convey my deep sense of gratitude to all teaching and non-teaching staffs of ECE

Department for their help and cooperation.

Finally, I thank my parents and my family members for giving me the moral support and

abundant blessings in all of my activities and my dear friends who helped me to endure

my difficult times with their unfailing support and warm wishes.

.

iv

ABSTRACT

In Federal Communication Commission (FCC) that at says most of the

spectrum in current wireless networks is unused most of the time, while some spectrum is

heavily used. Recently, dynamic spectrum access (DSA) has been proposed to solve this

spectrum inefficiency problem, by allowing the users to deviously access to unused

spectrum. In DSA, by efficiently share the spectrum among users, in which the spectrum

utilization can be increased and also the wireless interference can be reduced. Spectrum

sharing can be formalized as a graph colouring problem. We focus on surveying the

spectrum sharing techniques in DSA networks by spectrum sharing using the distributed with

common control channel. We propose the Dynamic Open Spectrum Sharing (DOSS)

protocol, (ie) a distributed protocol that allows for dynamic control channels and arbitrary

data channels. The control channels are robust to jamming and are adaptive to traffic load,

while the arbitrary data channels maximize the use of the available spectrum. Finally, the

challenges in current spectrum sharing research and its performance is evaluated using NS2

simulations.

v

TABLE OF CONTENTS

CHAPTER

No.

TITLE PAGE

No.

ABSTRACT iv

LIST OF FIGURES vii

LIST OF TABLES viii

LIST OF ABBREVIATIONS ix

1 INTRODUCTION

1.1 WIRELESS NETWORK 1

1.2 ISSUES WITH WIRELESS NETWORK 2

1.3 CHALLENGES IN WIRELESS NETWORK 2

1.4 APPLICATIONS OF WIRELESS NETWORK 3

1.5 BASICS OF DYNAMIC SPECTRUM ALLOCATION 3

1.5.1 OBJECTIVES OF DYNAMIC SPECTRUM

ALLOCATION

4

2 LITERATURE REVIEW

3 3.1 INTRODUCTION 8

3.2 DYNAMIC SPECTRUM ACCESS 8

3.3 DIFFERENT APPROCHES OF DSA MODELS 11

3.3.1 DYNAMIC EXCULSIVE USE MODEL

3.3.2 OPEN SPECTRUM SHARING MODEL

11

11

3.3.3 HIERARCHICAL ACCESS MODEL 12

3.4 PROPOSED YSTEM 13

3.4.1 COMMON CONTROL CHANNEL (CCC) 13

3.4.1.1 DISTRIBUTED WITH COMMON CONTROL

CHANNEL

13

3.4.1.2 EVALUATION 13

3.5 COGNITIVE RADIO 14

3.5.1 MAJOR FUNCTIONS OF COGNITIVE RADIO 16

3.5.1.1 SPECTRUM SENSING 16

3.5.1.2 SPECTRUM MANAGEMENT 24

3.5.1.3 SPECTRUM MOBILITY

3.5.1.4 SPECTRUM SHARING

3.5.2 CHALLENGES IN COGNITIVE RADIO

3.5.3 ADVANTAGES OF COGNITIVE RADIO

3.5.4 DISADVANTAGES OF COGITIVE RADIO

24

25

25

26

26

v

4 SYSYEM SPECIFICATION

4.1 SOFTWARE SPECIFICATION 27

4.2 NETWORK SIMULATOR 27

4.3 NETWORK SIMULATOR - 2 29

5 SIMULATION SCENARIO AND RESULTS

5.1 SIMULATION SCENARIO 34

5.2 PACKET DELIVERY RATIO 35

5.3 THROUGHPUT 35

5.4 DROPPING RATIO 36

5.5 JITTER 37

5.6 CONTROL OVERHEAD 38

6 CONCLUSION 44

7 REFERENCES 45

vii

LIST OF FIGURES

FIGURE

No.

FIGURE NAME PAGE

No.

1.1 SUBSET OF CURRENT SPECTRUM ASSIGNMENT 1

1.2 SPECTRUM UTILIZATION EXAMPLE 4

3.1 CONCEPT OF SPECTRUM HOLE 9

3.2 SPECTRUM SENSING AND SPECTRUM SHARING IN

THE TCP/IP STACK MODEL

9

3.3 COEXISTENCE OF MULTIPLE PRIMARY AND

SECONDARY USER NETWORKS

10

3.4 COGNITIVE RADIO CYCLE 15

3.5 SPECTRUM SENSING TECHNIQUES 17

3.6 BLOCK DIAGRAM OF ENERGY DETECTION 18

3.7 BLOCK DIAGRAM OF MATCHED FILTER 19

3.8 CYCLOSTATIONARY FEATURE DETECTOR BLOCK

DIAGRAM

20

3.9 TRANSMITTER DETECTION PROBLEM 20

3.10 INTERFERENCE TEMPERATURE MODEL 21

4.1 NS-2 ARCHITECTURE 29

5.1 SIMULATION SETUP 34

5.2 PACKET DELIVERY RATIO 35

5.3 THROUGHPUT 36

5.4 DROPPING RATIO 37

5.5 JITTER 37

5.6 CONTROL OVERHEAD 38

viii

LIST OF TABLES

TABEL No. TABEL NAME PAGE No.

3.1 PERFORMANCE EVALUATION FOR DIFFERENT

SPECTRUM SHARING TECHNIQUES

25

ix

LIST OF ABBREVIATIONS

FCC Federal Communication Commission

DSA Dynamic Spectrum Allocation

PCS Personal Communication service

ISM Industrial, Scientific and Medical

CRN Cognitive Radio Network

SDR Software Defined Radio

RF Radio Frequency

QoS Quality-of-Service

DOSS Dynamic Open Spectrum Sharing

SS Spectrum Sensing

SS Spectrum Sharing

TCP/IP Transmission Control Protocol/Internet Protocol

UWB Ultra Wide Band

CCC Common Control Channel

DARPA Defense Advanced Research Projects Agency

SM Spectrum Management

SM Spectrum Mobility

MF Matched Filter

PSD Power Spectral Density

ED Energy Detection

CFD Cyclostationary Feature Detection

FFT Fast Fourier Transform

ITM Interference Temperature Management

OTcL Object Oriented Tool Command Language

UDP User Datagram Protocol

ATM Asynchronous Transfer Mode

OPN Optimized Network Engineering Tool

PDR Packet Delivery Ratio

CBR Constant Bit Rate

VoIP Voice over Internet Protocol

TDMA Time Division Multiple Access

CDMA Code Division Multiple Access

1

CHAPTER 1

INTRODUCTION

1.1 WIRELESS NETWORKS

In current wireless networks, the spectrum is regulated by governmental

agencies, such as Federal Communication Commission (FCC) in United States, and is

statically assigned to licensed users on a long term basis. For example, 824-849 MHz, 1.85-

1.91 GHz, 1.930-1.99 GHz frequency bands are reserved for licensed cellular and personal

communication services (PCS) and require a valid FCC license, whereas the most popular

unlicensed bands are the Industrial, Scientific, and Medical (ISM) bands at 900 MHz, 2.4

GHz, and 5.8 GHz. Figure 1.1 shows a subset of current static spectrum assignment, ranging

from sonic to ultraviolet. For more detailed current radio spectrum (3KHz - 300GHz)

allocation in United States.

Fig.1.1 Subset of current spectrum assignment

1.2 ISSUES WITH WIRELESS NETWORK

1. Low power consumption in sensor networks is needed to enable long operating

lifetime by facilitating low duty cycle operation, local signal processing.

2

2. Distributed Sensing effectively acts against various environmental obstacles and care

should be taken that the signal strength, consequently the effective radio range is not

reduced by various factors like reflection, scattering and dispersions.

3. Multihop networking may be adapted among sensor nodes to reduce the

communication link range and also density of sensor nodes should be high.

4. Long range communication is typically point to point and requires high transmission

power, with the danger of being eavesdropped. So we should consider short range

transmission to minimize the possibility of being eavesdropped.

5. Communication systems should include error control subsystems to detect errors and

to correct them.

1.3 CHALLENGES IN WIRELESS NETWORK

Hardware Cost: The current cost of each individual sensor unit is still very high.

Commercially available platforms cost in the order of Rs. 5000 per unit with temperature,

humidity and light sensors when bought in large quantities. Capable sensors able to track

human mobility inside buildings are costing around Rs.15000 per unit.

System Architecture: There is no unified system and networking architecture that is stable

and mature enough to build different applications on top. Most of the applications and

research prototypes are vertically integrated in order to maximize performance.

Wireless Connectivity: Wireless communication in indoor environments is still quite

unpredictable using low-power consumption RF transceivers, in particular in clutter

environments common inside buildings, with many interfering electromagnetic fields, such

as the one produced by elevators, machinery and computers, among others.

Programmability: Some form of network re-programmability is desirable; doing so in

energy and communication conservative form remains a challenge.

Security: The security challenges are at many levels.

From the system point of view, it is critical that the information provided by the

nodes to be authenticated and the integrity verified, since this information provides

the feedback loop to expensive equipment controlling power consumption in the

building.

3

From the users’ point of view, it is also critical that this information cannot be

easily spoofed and remains protected in the back end processor, since it may affect

the privacy of users.

1.4 APPLICATIONS OF WIRELESS NETWORK

Environmental monitoring (e.g., traffic, habitat, security).

Industrial sensing and diagnostics (e.g., appliances, factory, supply chains).

Infrastructure protection (e.g., power grids, water distribution).

Battlefield awareness (e.g., multitarget tracking).

Context-aware computing (e.g., intelligent home, responsive environment) .

1.2 BASICS OF DYNAMIC SPECTRUM ALLOCATION

However, a recent study by FCC shows that most of the spectrum is, in practice,

unused most of the time, while some spectrum is heavily used, as shown in Figure 1.2. For

example, within ISM bands, anyone can transmit at any time, as long as their power does

not exceed the band's regulatory maximum. This results that the ISM bands are crowded and

may sometimes experience significant interference. Current limited availability and

inefficient usage of spectrum necessitate a new communication paradigm. Recently

software defined radio (SDR) has been developed to enable on the fly changes to the

characteristics of radio such as power, modulation, and allows the same hardware to be

reconfigured for use in different parts of the radio spectrum. Based on the development of

SDR, dynamic spectrum access (DSA) is proposed by researchers to solve spectrum

inefficiency problems by allowing opportunistic spectrum access.

In DSA networks, there are two classes of spectrum users, which are primary and

secondary users. Primary users already possess a license to use a particular frequency and

always have full access to the spectrum when they need it. Secondary users could use the

licensed/unlicensed spectrum opportunistically when it would not interfere with the primary

user. DSA mainly consists of two components, which are spectrum sensing and spectrum

sharing. Secondary users observe by sensing wide spectrum to find out which spectra are

currently unused by primary users. After spectrum sensing, spectrum sharing assigns and

schedules spectrum among secondary users. Compared to traditional radio, DSA can

4

increase spectrum utilization and reduce wireless interference, hence improving network

throughput, quality of service (QoS), etc.

Fig.1.2 Spectrum utilization example

Basically spectrum sharing can be formalized as a graph coloring problem.

Recently, intense research efforts have been made towards spectrum sharing in DSA

networks. Classified in different aspects, there are centralized versus distributed spectrum

sharing by the architecture, cooperative versus non-cooperative spectrum sharing by

cooperation behaviour, with versus without common control channel, and single versus

multiple radio interfaces, etc. DARPA started next generation (XG) program, which aims to

build a DSA network for military usage. XG radios demonstrate for the first time that DSA

networks are capable to utilize wide-range spectrum in realistic environments.

Spectrum sharing plays a key role in DSA, since its design significantly affects

the performance of DSA networks, such as interference level, network throughput. Efficient

spectrum sharing is integral to the success of open spectrum systems, and there are still

many challenges in spectrum sharing research.

1.5.1 OBJECTIVES OF DYNAMIC SPECTRUM ALLOCATION

Manage spectrum in a converged radio system and share it among all

participating radio networks over space and time, to increase overall the spectrum

efficiency.

5

CHAPTER 2

LITERATURE REVIEW

“DYNAMIC OPEN SPECTRUM SHARING MAC PROTOCOL FOR

WIRELESS AD HOC NETWORKS” By Liangping Ma, Xiaofeng Han, Chien-Chung Shen.

The static spectrum allocation scheme used by the legacy wireless

communication systems results in spectrum under-utilization. To make the best of the

precious spectrum resource, any chunk of idle spectrum should be allowed to be used as a

communication channel, subject to certain physical constraints, and this is the essence of the

open spectrum paradigm. Allowing an arbitrary communication channel requires

coordination between the sender and the receiver(s) such that the receiver(s) can readily

receive the transmitted signal. This project proposes the Dynamic Open Spectrum Sharing

(DOSS) MAC protocol, a distributed protocol that allows for dynamic control channels and

arbitrary data channels. The control channels are robust to jamming and are adaptive to

traffic load, while the arbitrary data channels maximize the benefit of the available

spectrum. In addition, this protocol supports efficient multicast, needs no synchronization,

and provides an option that eliminates the hidden and exposed terminal problems. We

conduct theoretical analysis of the protocol, study its performance via simulations, and

discuss related implementation issues.

“DYNAMIC CHANNEL SHARING IN OPEN-SPECTRUM WIRELESS

NETWORKS”

By Wei Wang and Xin Liu.

The current fixed spectrum allocation scheme leads to significant spectrum white

spaces. It requires a more effective spectrum allocation and utilization policy, which allows

unused parts of spectrum to become available temporarily for commercial purposes, so that

the scarcity of the spectrum can be largely mitigated. This project is an early attempt to

study such wireless networks with opportunistic spectrum availability and access. We

studied the dynamics in the available channels caused by the location and traffic load of the

6

primary users and proposed several distributed algorithms to exploit the available channels

for secondary users. The performance of different algorithms is evaluated in networks with

static and time-varying channel availability.

“A GAME-THEORETIC APPROACH TO COMPETITIVE SPECTRUM

SHARING IN COGNITIVE RADIO NETWORKS”

By Dusit Niyato, Ekram Hossain.

"Cognitive radio" is an emerging technique to improve the utilization of radio

frequency spectrum in wireless networks. In this project, we consider the problem of

spectrum sharing among a primary user and multiple secondary users. We formulate this

problem as an oligopoly market competition and use a Cournot game to obtain the spectrum

allocation for secondary users. The Nash equilibrium is considered as the solution of this

game. We first present the formulation of a static Cournot game for the case when all

secondary users can observe the adopted strategies and the payoff of each other. However,

this assumption may not be realistic in some cognitive radio systems. Therefore, we

formulate a dynamic Cournot game in which the strategy of one secondary user is selected

solely based on the pricing information obtained from the primary user. The stability

condition of the dynamic behaviour for this spectrum sharing scheme is investigated.

“HYBRID SPECTRUM SHARING IN DYNAMIC SPECTRUM ACCESS

NETWORKS”

By S. S. Nair, S. Schellenberg, J. Seitz, M. Chatterjee

An inefficient spectrum usage problem which will lead to spectrum scarcity in

future communications has been addressed. According to Federal Communications

Commission, spectrum sharing is a technique to efficiently utilize the spectrum. We analyse

dynamic spectrum access concepts such as overlay spectrum sharing and underlay spectrum

sharing. Our overlay and underlay analysis have been made with the use of five and eight

state continuous time Markov chains respectively. We derive the steady state probability for

these states and also calculate the throughput of the aforementioned spectrum sharing

schemes with the use of Shannons channel capacity. In addition to that, specifically, we

7

propose a hybrid spectrum access scheme which combines overlay and underlay spectrum

sharing schemes and employ them to improve the system throughput by efficiently using the

spectrum. Our approach, by using continuous time Markov chain, reduces the complexity in

modelling the dynamics of primary and secondary user. We have also shown numerical

results to validate the performance of the proposed scheme.

“COOPERATIVE AND DISTRIBUTED SPECTRUM SHARING IN

DYNAMIC SPECTRUM POOLING NETWORKS”

By Pengbo Si, Enchang Sun, Ruizhe Yang, Yanhua Zhang

In dynamic spectrum access systems, such as cognitive radio networks,

spectrum pooling is one of the approaches to manage the available spectrum bands from

different licensed networks. Based on the concept of spectrum pooling, most previous work

focuses on the system architecture and the design of flexible access algorithms and

schemes. In this paper, a cooperative and distributed scheme for dynamic internetwork

spectrum sharing among multiple networks is proposed, taking into account the spectrum

access price and the spectrum efficiency. Specifically, the spectrum sharing problem is

formulated as a restless bandit’s model-based optimization system, which dramatically

reduces the complexity of the scheme by allowing the spectrum allocation scheme to be

simply select the network with the lowest index. Extensive simulation results illustrate that

the proposed scheme improves the performance significantly compared to the existing

scheme that ignores the distributed and cooperative spectrum sharing.

8

CHAPTER 3

3.1 INTRODUCTION

An overview of dynamic spectrum access and its two main components:

(i) Spectrum sensing and

(ii) Spectrum sharing

Spectrum sharing focus the outline its basic problem statement and its motivations

for DSA. Based on the taxonomy of spectrum sharing, we distribute the dynamic spectrum

sharing with common control channel.

3.2 DYNAMIC SPECTRUM ACCESS

In the early 1990s, Joseph Mitola first introduced the idea of software defined radios

(SDRs). Different with traditional radio, SDR enables on the fly changes to the characteristics

of radio such as power, modulation, and waveform, and allows the same hardware to be

reconfigured for use in different parts of the radio spectrum. SDR is an integral technique for

DSA since it enables the usage of temporarily unused spectrum referred to as spectrum hole

or white space, as shown in Figure 3.1. Compared to traditional radio, DSA can significantly

increase spectrum utilization by coordinating the spectrum usage among secondary users, thus

reducing potential interference, and improving network throughput and quality of service etc,.

The applications of DSA networks include cognitive ad hoc network (e.g. WNaN),

emergency network, military network, IEEE 802.22 etc. DSA shares some similarity with

multi-channel 802.11 MAC, in that they both allow users to opportunistically access different

parts of the spectrum. However, there are significant differences between them. DSA has the

advantages that it can utilize the whole spectrum and while incurring no interference to

primary users.

Wireless networks have both primary and secondary users. The goal of DSA is the

coexistence of primary and secondary users and the most important challenge is to share the

licensed spectrum without interfering with primary users. Typically DSA has two

components, which are spectrum sensing and spectrum sharing.

9

Fig.3.1 Concept of spectrum hole

Figure 3.2 shows the position of spectrum sensing and spectrum sharing in TCP/IP

stack model. Spectrum sensing and spectrum sharing are mainly located at the physical and

the link layer, respectively. Spectrum sensing keeps scanning a wide range of spectrum and

periodically reports spectrum information to spectrum sharing. We note that spectrum sharing

involves the part of the network layer. This is because network layer issues (such as routing)

can be taken into consideration in spectrum sharing.

Fig.3.2 Spectrum sensing and spectrum sharing in the TCP/IP stack model

10

The concept of dynamic spectrum access is the identification of spectrum holes (a

frequency band which is free enough to be used) or white spaces and uses them to

communicate.

Dynamic spectrum access is the most vital application of cognitive radios. The

primary user bands are opportunistically accessed by the secondary user networks such that

the interference caused to the primary users is negligible. Figure.3.3 shows the scenario for

dynamic spectrum access (DSA) where multiple primary users and secondary users are

coexisting.

Fig.3.3 Coexistence of multiple primary and secondary user networks (homogeneous or heterogeneous)

This is a technique by which a radio system adapts to available spectrum holes with

limited spectrum use rights dynamically, in response to changing circumstances and

objectives: the created interference changes the radio’s state in environmental constraints. The

main task of the DSA is to overcome two types of interference:

i) Harmful interference caused by device malfunctioning and

ii) Harmful interference caused by malicious users

There are three main functions in Dynamic Spectrum Access:

i) Spectrum awareness

ii) Cognitive processing and

iii) Spectrum access

11

Spectrum awareness – It creates awareness about the Radio Frequency environment

when spectrum access provides the ways to use the available spectrum opportunities for reuse

efficiently.

Cognitive processing - Is the intelligence and decision making function that performs

several subtasks like learning of the radio environment, designing sensing efficient, and

Spectrum access - Policies which manage interference for coexistence of the

secondary user networks with the primary user networks.

3.3. DIFFERENT APPORACHES OF DSA MODELS

Dynamic spectrum access strategies can be classified as dynamic exclusive use, open

sharing model, and hierarchical access model.

3.3.1 Dynamic Exclusive Use model

The basic structures of the current spectrum regulation policy are maintained in this

model: Spectrum bands are licensed to services for exclusive use. The main concept is to

improve spectrum efficiency by introducing flexibility. Two approaches have been

considered under this model:

i) Spectrum property rights and ii) dynamic spectrum allocation.

Spectrum property rights – It allows license to sell and trade spectrum and to choose

technology freely. Therefore, the economy and market will play a major important role with

the most profitable use of this limited resource.

Dynamic spectrum allocation – It aims to improve the efficiency of spectrum through

dynamic spectrum assignment by using the spatial and temporal traffic statistics of different

services, i.e., spectrum is allocated to services for exclusive use in a given region at a given

time.

3.3.2 Open Spectrum sharing model

Open sharing model is also called spectrum commons model. In the spectrum

commons model, every user has equal rights to use the spectrum. This is also known as an

open spectrum model and it is also applied to wireless services which operates in the

12

unlicensed Industrial, Scientific and Medical (ISM) radio band (e.g., WLAN). Open sharing

among users as the foundation for managing a spectral region used by this model. There are

three types of spectrum commons model: i) Uncontrolled- commons, ii) Managed-commons

and iii) Private-commons.

i) Uncontrolled-commons: Uncontrolled commons means when a spectrum band is managed

and using uncontrolled commons model, where no entity has exclusive license to the

spectrum band.

ii) Managed-commons: Managed-commons represent an effort to avoid the tragedy of

commons by imposing a limited form of structure of spectrum access. This is a resource

which is owned or controlled by a group of individuals or entities and it is characterized by

restrictions on when and how the resource is used.

iii) Private-commons: The concept of Private Commons was introduced by FCC in its second

report on the elimination of barriers to the development of secondary markets for spectrum.

This concept grew on allowing use of advanced technologies which enable multiple users to

access the spectrum.

3.3.3 Hierarchical Access Model

This model adopts a Hierarchical Access Structure with primary and secondary

users. This model uses licensed spectrum to Secondary Users (SUs) while limiting

interference perceived by primary users. The other two models are the spectrum underlay and

the spectrum overlay. The underlay approach executes severe limitations on the transmission

power of secondary users so that they operate below the noise floor of primary users by

spreading transmitted signals over Ultra Wide Band (UWB). Secondary Users (SUs) can

potentially achieve a short-range high rate with extremely low transmission power. Based on

a worst-case assumption primary users transmit all the time, this approach does not rely on

detection and exploitation of spectrum white space. This model restricts on where and when

spectrum can transmit.

13

3.4 PROPOSED SYSTEM

3.4.1 Common Control Channel (CCC)

Common Control Channel (CCC) is a specific control channel predefined for all

secondary users to communicate control information with each other. The control information

includes spectrum assignment, spectrum negotiation, spectrum time scheduling, etc. The use

of Common Control Channel can simplify the design of DSA networks, however, the

indefinability of spectrum in DSA networks may result a very low probability that an

Common Control Channel can actually exist. Moreover, Common Control Channel has

saturation problem and is vulnerable to security attack such as jamming.

3.4.1.1 Distributed with Common Control Channel

Distributed spectrum sharing does not require any centralized entity or

infrastructure, instead users self-organize and decide (cooperatively or non-cooperatively) the

spectrum assignment due to changing environment. Hence distributed spectrum sharing is

more scalable, which is suitable for military network (e.g. Next generation), emergency

network, etc. Research activities in distribute spectrum sharing techniques include (co-

operative) and (non-cooperative). We focus on Dynamic Open Spectrum Sharing (DOSS)

protocol, which is a representative one for distributed spectrum sharing and uses common

control channel.

3.4.1.2 Evaluation

Dynamic Open Spectrum Sharing is distributed, cooperative spectrum sharing

with CCC and multiple radio interfaces. We summarize the strengths and limitations of DOSS

as follows.

Strengths:

Resulted from its distributed nature, Dynamic Open Spectrum Sharing (DOSS) does

not require any central entity or infrastructure and is more scalable compared to

centralized spectrum sharing. Moreover, the design of DOSS is simplified by the use

of multiple radio interfaces and Common Control Channel.

14

By employing a busy tone on a dedicated transceiver, the constraints (no interference)

of the graph coloring problem are satisfied naturally. The hidden and exposed terminal

problems are eliminated in DOSS. However, DOSS only consider single-hop based

spectrum negotiation and does not apply any optimization goals for spectrum sharing.

Limitations:

DOSS requires at least two transceivers: one for data and control channel, the other

dedicated for busy tone. More radio interfaces will increase device cost. What is more,

besides normal spectrum sensing, DOSS sender needs to listen to to busy tones of

other receivers to prevent possible interference, thus imposing additional overhead.

Although DOSS proposes several techniques to mitigate the CCC saturation problem,

such as limiting the traffic going through CCC and allowing slow migration of CCC

traffic to current data channel, CCC is still vulnerable to security attack and has the

potential to become a single point of failure.

3.5 COGNITIVE RADIO

A cognitive radio (CR) is an intelligent radio that can be programmed and

configured dynamically.

A radio automatically detects available channels in wireless spectrum, then

accordingly changes its transmission or reception parameters to allow more concurrent

wireless communications in a given spectrum band at one location. Cognitive radio (CR)

technology is a paradigm for wireless communication in which transmission or reception

parameters of network or wireless node are changed to communicate avoiding interference

with licensed or unlicensed users.

There are two types of cognitive radio: i) full cognitive radio and ii) spectrum-sensing

cognitive radio.

Full Cognitive radio - considers all parameters, a wireless node or network can be aware of

every possible parameter observable by a wireless node or network is considered.

Spectrum-sensing cognitive radio - detects the channels in the radio frequency spectrum and

considers radio frequency spectrum. The requirements of the performances for cognitive radio

15

system are: i) Authentic spectrum hole and detection of the primary user, ii) Precise link

estimation between nodes, iii) Fast and accurate frequency control and iv) Method of power

control that assures reliable communication between cognitive radio terminals and non-

interference to the primary users.

There are two main characteristics of the cognitive radio and can be defined:

Cognitive capability: The ability of the radio technology is to capture or sense the

information from its radio environment.

Reconfigurability: Spectrum awareness is provided by the cognitive capability whereas

the radio to be dynamically programmed according to the radio environment are enabled by

the reconfigurability.

Cognitive cycle requires adaptive operation in open spectrum access. Three major parts

of the cognitive cycle are: spectrum sensing, spectrum analysis, spectrum decision as shown

in Figure 3.4.

Transmitted signal RF Stimuli

Spectrum decision Cognitive radio, receiver

Result of

Cognitive radio, detection

transmitter

Cooperative

sensing

Spectrum analysis

Fig.3.4 Cognitive radio cycle

Radio

environment

Cognitive

radio

network

Reconfiguration:

transmitted power

carrier frequency

Spectrum

sensing

Spectrum

allocation

16

A. Spectrum Sensing

It which the presence of licensed users and spectrum hole. The spectrum sensing

techniques are:

1. Primary transmitter detection

2. Primary receiver detection

3. Interference temperature management

B. Spectrum Analysis

If performs the estimation of spectrum hole through a spectrum sensing.

C. Spectrum Decision

A Cognitive radio determines the channel capacity, spectrum whole information

along with data rate and bandwidth of the transmission. The appropriate spectrum band is

chosen for transmission of the signal. Parameters to define the presentation of a particular

spectrum bands are:

1. Interference – estimate permissible power of the CR.

2. Path loss - closely related to distance and frequency.

3. Wireless link errors – depending on the modulation scheme and the interference level.

4. Link layer delay – different types required at different bands.

3.5.1 Major Functions of Cognitive Radio

Cognitive radio has four major functions. They are Spectrum Sensing, Spectrum

management, Spectrum Mobility and Spectrum Sharing.

3.5.1.1 Spectrum Sensing

Spectrum sensing determines if a primary user is present on a band. After sensing the

spectrum, the cognitive radio can share the result of its detection with other cognitive radios.

Spectrum sensing technique can be categorized into two types. They are: Direct and

Indirect Techniques. Direct Technique is also called as frequency domain in which estimation

is carried out directly from signal approach. Where as in Indirect Technique (also called as

time domain approach), estimation is performed using autocorrelation of the signal. Another

way of classification depends on the need of spectrum sensing as stated below.

17

A. Spectrum Sensing for Spectrum opportunities

1) Primary transmitter detection : Based on the received signal at CR users the detection

of primary users is performed. This approach includes matched filter (MF) based

detection, energy based detection, covariance based detection, waveform based

detection, cyclostationary based detection, Primary Transmitter Detection etc.

2) Cooperative and collaborative detection: The primary signals for spectrum

opportunities are detected reliably by interacting or cooperating with other users, and

the method can be implemented as either centralized access to spectrum coordinated

by a spectrum server or distributed approach implied by the spectrum load smoothing

algorithm or external detection.

B. Spectrum Sensing for Interference Detection

1) Interference temperature detection: In this approach, Cognitive radio system works in

the ultra wide band (UWB) technology where the secondary users coexist with the

primary users are allowed to transmit with low power and are restricted by the

interference temperature level so as not to cause harmful interference to primary users.

2) Primary receiver detection: In this method, the interference and the spectrum

opportunities are detected based on primary receiver's local oscillator leakage power.

I) Classification of Spectrum Sensing Techniques

Fig. 3.5 Spectrum Sensing Techniques

Spectrum

sensing

Cooperative

system

Non

Cooperative

system

Interferene

based

sensing

Energy

detection

Matched

Filter

detection

Cyclostationary

Feature detection

18

A. Primary Transmitter Detection: The few primary transmitter detection techniques:

1) Energy Detection: In this technique there is no need of prior knowledge of Primary signal

energy.

The block diagram for the energy detection technique is shown in the Figure 3.6.

Fig.3.6 Block Diagram of Energy Detection

Where H0 = Absence of User.

H1 = Presence of User.

In this method, signal is passed through band pass filter of the bandwidth W and is

integrated over time interval. The output from the integrator block is then compared to a

predefined threshold. This comparison is used to discover the existence of absence of the

primary user. The threshold value can be fixed or variable based on the channel conditions.

y(k) = n(k)…………… H0

y(k) = h * s(k) + n(k)…… H1

Where y (k) is the sample to be analyzed at each instant k and n (k) is the noise of

variance σ2. Let y(k) be a sequence of received samples kϵ{1, 2….N} at the signal detector,

then a decision rule can be stated as,

H0…… if ɛ > v

H1…… if ɛ < v

Where ɛ = E |y(k)|2 the estimated energy of the received signal and v is chosen to

be the noise variance σ2. However ED is always accompanied by a number of disadvantages:

i) Sensing time taken to achieve a given probability of detection may be high.

ii) Detection performance is subject to the uncertainty of noise power.

iii) ED cannot be used to detect spread spectrum signals.

PSD BPF Integrator

H0

H1

H1

19

2) Matched Filter:

Fig.3.7 Block Diagram of Matched Filter

Where H0 = Absence of User.

H1 = Presence of User.

A matched filter (MF) is a linear filter designed to maximize the output signal to

noise ratio for a given input signal. When a secondary user has a prior knowledge of primary

user signal, matched filter detection is applied. Matched filter operation is equivalent to

correlation in which the unknown signal is convolved with the filter whose impulse response

is the mirror and time shifted version of a reference signal. The operation of matched filter

detection is expressed as:

Y[n] = Σ h[n-k] x[k]

Where ‘x’ is the unknown signal (vector) and is convolved with the ‘h’, the impulse

response of matched filter that is matched to the reference signal for maximizing the SNR.

Detection by using matched filter is useful only in cases where the information from the

primary users is known to the cognitive users.

Advantages:

Matched filter detection needs less detection time because it requires only O

(1/SNR) samples to meet a given probability of detection constraint. When the information of

the primary user signal is known to the cognitive radio user, matched filter detection is the

optimal detection in stationary Gaussian noise.

Disadvantages:

Matched filter detection requires a prior knowledge of every primary signal. If the

information is not accurate, MF performs poor result. Also the most significant disadvantage

BPF Matched

Filter

H0

H1

20

of Matched filter is that a Cognitive radio would need a dedicated receiver for every type of

the primary user.

3)Cyclostationary Feature Detection:

To identify the received primary signal in the presence of primary users it exploits

periodicity of modulated signals couple with sine wave carriers, hopping sequences, cyclic

prefixes etc. Block diagram of Cyclostationary feature is shown in Figure 3.8.

This technique is robust in discriminating in noise so it performs better than energy

detector.It has demerit that it need more computational complexity and longer observation

time.

Fig.3.8 Cyclostationary feature detector block diagram

B. Cooperative Detection

In this technique for detection of primary user multiple CR users are incorporated.

In primary transmitter detection technique, there was a hidden terminal problem exist while

having a good line-of-sight to recover CR transmitter that may not be able to detect the

transmitter due to shadowing as shown in Figure 3.9.

Fig.3.9 Transmitter detection problem: (a) Receiver uncertainty and (b) Shadowing uncertainty

BPF Correlate Average over T Feature

detection N point FFT

21

Cooperative sensing techniques are classified as (1) Centralised Coordinated

(2) Decentralised Coordinated (3) Decentralised Uncoordinated.

1) Decentralized Uncoordinated Techniques:

In uncoordinated techniques Cognitive Radio will independently detect the channel

and will vacate the channel when it finds a primary user without informing the other users. So

Cognitive Radio users will experience bad channel realizations detect the channel incorrectly,

thereby causing interference at the primary receiver. So these are not advantageous when

compared to coordinated techniques.

2) Centralized Coordinated Techniques:

In this technique we have Cognitive radio controller. When one Cognitive Radio

detects the presence of primary user then it intimates the Cognitive Radio controller. Then

that controller informs to all the Cognitive radio users by the broadcast method. This is

furthermore classified into two types as partially cooperative in which network nodes

cooperate only in sensing the channel. The other technique is totally cooperative in which

nodes cooperate in relaying each other’s information in addition to cooperatively sensing the

channel.

3) Decentralized Coordinated Techniques:

This type of coordination implies building up a network of cognitive radios without

having the need of a controller. Various algorithms have been proposed for the decentralized

techniques among which are the gossiping algorithms or clustering schemes, where cognitive

users gather in clusters, auto coordinating themselves. The cooperative spectrum sensing

raises the need for a control channel, which can be implemented as a dedicated frequency

channel or as an underlay UWB channel.

Advantages:

Cognitive users selflessly cooperating to sense the channel have lot of benefits

among which the plummeting sensitivity requirements: channel impairments like multipath

fading, shadowing and building penetration losses, impose high sensitivity requirements

inherently limited by cost and power requirements.

22

Disadvantages:

Cooperative technique even has disadvantage like the CR users need to perform

sensing at periodic time intervals as sensed information become fast due to factors like

mobility, channel impairments etc.

C) Interference -Based Detection:

We present interference based detection so that the CR users would operate in

spectrum underlay (UWB like) approach.

1) Primary Receiver Detection:

The Primary receiver emits the local oscillator (LO) leakage power from its RF

front end while receiving the data from primary transmitter. It has been suggested as a method

to detect primary user by mounting a low cost sensor node close to a primary user's receiver

in order to detect the local oscillator (LO) leakage power emitted by the RF front end of the

primary user's receiver which are within the communication range of CR system users. The

local sensor then reports the sensed information to the CR users so that they can identify the

spectrum occupancy status. We note that this method can also be used to identify the

spectrum opportunities to operate CR users in spectrum overlay.

2) Interference Temperature Management:

Unlike the primary receiver detection, the basic idea behind the interference

temperature management is to set up an upper interference limit for a given frequency band in

specific geographic location such that the CR users are not allowed to cause harmful

interference while using the specific band in specific area. Typically, CR user transmitters

control their interference by regulating their transmission power (their out of band emissions)

based on their locations with respect to primary users. This method basically concentrates on

measuring interference at the receiver. The operating principle of this method is like an UWB

technology where the CR users are allowed to coexist and transmit simultaneously with

primary users using low transmit power that is restricted by the interference temperature level

so as not to cause harmful interference to primary users.

23

Fig.3.10 Interference temperature model

II) Issues in Spectrum Sensing

A. Channel Uncertainty:

Because of fading or shading of the channel there will be uncertainties in the

received signal strength which will lead to wrong interpretation. To avoid this Cognitive

Radios must have high sensitivity so that it can differentiate between faded primary signal

and a white space. If the fading is severe, a single cognitive radio cannot give high sensitivity

so handle this we go for a set of cognitive radios which share their local measurements and

collectively decide on the occupancy state of a licensed band.

B. Noise Uncertainty:

The detection sensitivity can be defined as the minimum SNR at which the

primary signal can be accurately detected by the cognitive radio and is given by

( )

Where N= Noise power.

Pp= Power Transmitted by Primary User.

D= Interference Range of Secondary User.

R= Maximum distance between Primary Transmitter and corresponding Receiver

The noise power estimation is limited by calibration errors as well as changes in

thermal noise caused by temperature variations. Since a cognitive radio may not satisfy the

24

sensitivity requirement due to underestimate of N, ɤmin should be calculated with the worst

case noise assumption, thereby necessitating more sensitive detector.

C. Aggregate Interference Uncertainty:

If multiple Cognitive Radios are operating in the same licensed band which will

lead to spectrum sensing will be affected by uncertainty in aggregate interference. Even

though the primary user is out of interference range this uncertainty may lead to wrong

detection so this uncertainty will create a need of more sensitive detector.

D. Sensing Interference Limit:

There are two factors in this issue that is when an unlicensed user may not know

exactly the location of the licensed receiver which is required to compute interference caused

due to its transmission. The second reason is that if a licensed receiver is a passive device, the

transmitter may not be aware of the receiver. So these factors need attention while calculating

the sensing interference limit.

3.5.1.2 Spectrum Management

Based on the availability of the spectrum and other policies, CR user allocates the

best available spectrum band to achieve high quality of service requirement. There are two

techniques for spectrum management:

• Spectrum analysis: In this technique each spectrum hole should be characterized considering

not only the time-varying radio invironment but also the primary user activity.

• Spectrum Decision: When all the analysis of spectrum band is done, the appropriate

spectrum band is being selected for the current transmission considering the QoS

requirements and the spectrum characteristics. According to user requirement the data rate,

bandwidth is determined and according to decision rule the appropriate spectrum band is

choosen.

3.5.1.3 Spectrum Mobility

Spectrum mobility is a function related to the variation of operating frequency

band of Cognitive radio users. When a licensed user begins to access a radio channel which is

25

currently being used by an unlicensed user, the unlicensed user can change the idle spectrum

to an active spectrum band. This change in the operating frequency band is known as

spectrum handoff. The protocol parameters at the different layers in the protocol stacks have

to be adjusted to match the new operating frequency band during spectrum handoff. Spectrum

handoff must try to ensure that the unlicensed user can continue the data transmission in the

new spectrum band.

3.5.1.4 Spectrum Sharing

Since there is a number of secondary users available in the spectrum holes,

cognitive radio has to maintain balance between its self-goal of information transferring

efficiently and selfless goal to share the available spectrum with other cognitive and non-

cognitive users. This is done by determining the behaviour of cognitive radio in the radio

environment. The fair spectrum scheduling method, uses open spectrum in the spectrum

sharing is one of the major challenges. In existing systems, it is similar to generic media

access control MAC problems.

A summary of the performance evaluation of different spectrum sharing techniques:

Performance DSAP DOSS MAC XG

Design Complexity Low Medium High High

Range of Optimization High Low Medium Medium

Scalability Low Medium High High

Security Medium Low High Hign

Device cost Expensive Expensive Cheap Expensive

Flexibility Low Low Low High

Table.3.1 performance evaluation for different spectrum sharing techniques

3.5.2 Challenges in Cognitive radio

1) Challenges in Spectrum Sensing:

i. Interference temperature measurement.

ii. Spectrum sensing in multi-user network.

iii. Detection capability.

26

2) Challenges in Spectrum Management:

i. Decision model.

ii. Multiple spectrum band decision.

iii. Cooperation with reconfiguration.

iv. Spectrum decision over heterogeneous spectrum bands.

3) Challenges of Spectrum Mobility:

i. Spectrum handoff.

ii. Spectrum mobility in multiple users.

4) Challenges in Spectrum Sharing:

i. Common Control Channel (CCC).

ii. Dynamic radio range.

iii. Spectrum unit.

3.5.3 Advantages of Cognitive radio

Unused spectrum is determined and use them automatically.

Several network standards are interoperated and recognized.

It improves and executes its progress and minimize interference.

3.5.4 Disadvantages of Cognitive radio

Cognitive radio has no sense of sight which severely limits the ability to detect the

environment.

This can lead to the hidden terminal problem where the sensing secondary user is

unaware of the presence of a primary user because it cannot detect its presence.

27

CHAPTER 4

SYSTEM SPECIFICATION

4.1 SOFTWARE SPECIFICATION

Operating system : Fedora - Linux

Scripting language : Network Simulator 2.34

Protocol developed : C++

4.2 NETWORK SIMULATOR

Introduction

A network simulator is a software program that imitates the working of a computer

network. In simulators, the computer network is typically modelled with devices, traffic,

etc., and the performance is analysed. Typically, users can customize the simulator to fulfill

their specific analysis needs. Simulators typically come with support for the most popular

protocols in the use today, such as Wireless LAN, Wi-Max, UDP, and TCP. A network

simulator is a piece of software or hardware that predicts the behaviour of a network,

without an actual network being present. NS is an object oriented simulator, written in C++,

with an OTcl interpreter as a frontend.

The simulator supports a class hierarchy in C++ and a similar class hierarchy

within the OTcl interpreter. The two hierarchies are closely related to each other; from the

user’s perspective, there is a one-to-one correspondence between a class in the interpreted

28

hierarchy and one in the compiled hierarchy. The root of this hierarchy is the class Tcl

object. Users create a new simulator object through the interpreter; these objects are

instantiated within the hierarchy. The interpreted class hierarchy is automatically

established through methods defined in the class Tcl object. There are other hierarchies in

the C++ code and OTcl scripts; these other hierarchies are not mirrored in the manner of Tcl

object.

Uses of Network simulators

Network simulators serve a variety of needs. Compared to the cost and time

involved in setting up an entire test bed containing multiple networked computers, routers

and data links, network simulators are relatively fast and inexpensive. They allow engineers

to test scenarios that might be particularly difficult or expensive to emulate using real

hardware- for instance, simulating the effects of sudden bursts in the traffic or a Dos attack

on a network service. Networking simulators are particularly useful in allowing designers to

test new networking protocols or changed to existing protocols in a controlled and

reproducible environment. Network simulators simulate and then analyze the effect of

various parameters on the network performance. Typical network simulators encompasses a

wide range of networking technologies and help the users to build complex networks from

basic building blocks like variety of nodes and links. With the help of simulators one can

design hierarchical networks using various types of nodes like computers, hubs, bridges,

routers, optical crossconnects, multicast routers, mobile units, etc. various types of Wide

Area Network (WAN) like TCP, ATM, IP etc and Local Area Network (LAN) technologies

like Ethernet, token rings etc, can all be simulated with the typical simulator and the user

can test, analyze various routing etc. There are a wide variety of network simulators,

ranging from the very simple to very complex. Minimally a network simulator must a user

to represent a network topology, specifying the nodes of the network, the links between the

nodes and the traffic between the nodes. More complicated systems may allow the user to

specify everything about the protocols used to handle network traffic. Graphical

applications allow users to easily visualize the working of their simulated environment. Text

based applications may provide a less intuitive interface, but may permit more advanced

forms of customization. Others, such as GTNets, are programming- oriented, providing a

29

programming framework that the user then customizes to create an application that

simulates the networking environment to be tested.

4.3 NETWORK SIMULATOR – 2 (NS 2)

What is NS 2

NS2 is an open- source simulation tool that runs on Linux. It is a discrete event

simulator targeted at networking research. NS provides substantial support for simulation of

TCP, routing, and multicast protocols over wired and wireless (local and satellite) networks.

NS-2

Is a discrete event simulator for networking research

Simulates at packet level

Substantial support to simulate many protocols

Simulate wired and wireless network

Is primarily Unix based

o A package of tools that simulate the behaviour of networks

Create network topologies.

Log events that happen under any load.

Analyse events to understand the network behaviour.

Fig.4.1 NS-2 Architecture

30

Supporting Protocols

Wired Networking

Routing: Unicast, Multicast, and Hierarchical Routing, etc.

Transportation: TCP, UDP, others;

Traffic sources: web, ftp, telnet, cbr, etc.

Queuing disciplines: drop-tail, RED, FQ, DRR, etc.

QoS: IntServ and Diffserv Wireless Networking

Wireless

Ad hoc routing and mobile IP

Routing Protocol: AODV, DSDV, DSR, etc.

MAC layer Protocol: TDMA, CDMA, IEEE Mac 802.x

Physical layers: different channels, directional antenna

Sensor networks: diffusion

Satellite networks

Installation of NS2

The primary platform of NS-2: Linux

It supports other platforms: Windows

In this course

Linux: Fedora Core 13

It was already installed in VM and is ready to use

NS-2: 2.34

The “all-in-one’’ package is used

ns-allinone-2.34.tar.gz

Fedora Core 13

All development packages were installed

Tested in VMware 6.5.2

root password: **********

Networked

Linux (Guest) : 192.168.224.1/24

31

Windows (Host) : 192.168.224.2/24

Samba server is enabled

Firewall SELinux are disabled

The “all-in-one” package contains all components

In a .tar.gz file

Extract the file

tar -xzf ns-allinone-2.34.tar.gz

All components are installed by a single command

./install

Creating a Tcl scenario

To define trace files with the data that needs to be collected from the simulation,

we have to create these files using the command open:

#open the trace file

set traceFile [open out.tr w]

$ns trace-all $traceFile

#open the Nam trace file

set namFile [openout.nam w]

$ns namtrace-all $namFile

#define the TCP agent

Set tcp [new Agent/TCP]

$ns attach-agent $n(0) $tcp

Set sink [new Agent/TCPSink]

$ns attach-agent $n(1) $sink

$ns connect $tcp $sink

Node Configuration

Node configuration essentially consists of defining the different node characteristics

before creating them. They may consists of the type of addressing structure used in the

simulation, defining the network components for mobile nodes, turning on or off the trace

32

options at Agent/Router/MAC levels, selecting the type of adhoc routing protocol for

wireless nodes or defining their energy model. The node- configure command would look

like the following:

$ns_ node-configure –addressType hierarchical\

-adhocRouting AODV\

-11TypeLL\

-macTypeMac/802_11\

-ifqType Queue/DropTail/PriQueue\

-ifqLen50\

-antType Antenna/OmniAntenna\

-propType propagation/TwoRayGround\

-phyType Phy/WirelessPhy\

-topologyInstance $topo\

-channel Channel/wirelessChannel\

-agentTrace ON\

-routerTrace ON\

-macTrace OFF\

-movement Trace OFF

Simulation Procedure

Run the script by typing at the Console as

ns filename.tcl

On completion of the run, Trace output file “filename-out.tr” and nam output file

“filename-out.nam” are created. Running filename-out.nam, the mobile nodes moving in the

nam window can be seen. The active senders start informing the network about its presence

and begin sending data according to the random progress method

The finish procedure is given as

proc finish{} {

$ns flush-trace

close $r

33

close $nf

exec nam –r filename. nam &

exit 0

}

In the finish procedure, the trace file buffer is cleared and the graphs are generated in

the terminal in a pipelined manner. $ns is used to close the trace field. Now the animator

field is generated using command

exec nam filename.nam

To run the file $ns run command is used and the tcl script is executed.

To execute the graph exec ns graph.tcl command is used.

Advantages of NS2

Open source

Free (no money)

Supported protocols

Supported platforms

Modularity

Popular

Documentation

Disadvantages of NS2

Complicated structure

Bugs

Unreliable

Simulation validation

Patching & Extending

Unrealistic abstraction

Speed & Memory

34

CHAPTER 5

SIMULATION SCENARIO AND RESULTS

5.1 SIMULATION SCENARIO

Simulation has been carried out using Network Simulator. Totally 50 nodes are

deployed for simulation scenario. Some of the nodes are fixed and some are movable. The

nodes act as gateways for sensor network in every cell. Each cell is provided with a Base

Station Controller to control and resolve dynamic routing strategies for the gateways and

sensor nodes. There is a network monitor deployed per every three cell to monitor the

communication.

Fig.5.1. Simulation Setup

35

5.2 PACKET DELIVERY RATIO

The ratio of the number of delivered data packets to the destination. This

illustrates the level of delivered data to the destination.

PDR = ∑ Number of packets receive x 100 ∑ Number of packets send

The greatest value of the packet delivery ratio means better performance of the

protocol. Where, the sum of data packets received by the each destination and the sum of

data packets generated by the each source. The time versus the number of nodes graphs show

the fraction of data packets that are successfully delivered during simulation. It is shown that

in the existing system, the average packet delivery ratio is much less and it is enhanced by

the implementation of flat converged proposal. In enhanced proposal, the interference in the

channel is avoided and this improves the delivery ratio to an optimized value.

Fig.5.2 Packet delivery ratio

5.3 THROUGHPUT

It is defined as the total number of packets, delivered over the total simulation

time. Throughput or network throughput is the rate of successful message delivery over a

communication channel. The data that belong to a particular simulation time may be

delivered over a physical or logical link, or it can pass through a certain network node.

95

96

97

98

99

100

101

4 5 6 7 8

PD

R

Number of channels

Packet delivery ratio

pdr_cbr_crdsa

pdr_voip_crdsa

pdr_video_crdsa

36

Throughput is usually measured in bits per second (bit/s or bps), and sometimes in data

packets per second (p/s or pps) or data packets per time slot.

Mathematically, throughput can be defined as:

Throughput= N/1000

Where N is the number of bits received successfully by all destinations.

Fig.5.3 Throughput

5.4 DROPPING RATIO

Dropping ratio is defined as the total number of packets dropped during the

simulation. Packet loss occurs when one or more packets of data travelling across a network

fail to reach their destination. Packet loss is typically caused by network congestion.

Packet lost = Number of packets send – Number of packets received

Packet loss may be measured as the frame loss rate is defined as the percentage

of frames that should have been forwarded by a network. The lower value of the packet lost

means better performance of the protocol. The amount of packet loss that is acceptable

depends on the type of data being sent.Losses between 5% and 10% of the total packet

238000

240000

242000

244000

246000

248000

250000

252000

254000

4 5 6 7 8

Th

rou

gh

pu

t (b

ps)

Number of channels

Throughput

throughput_cbr_crdsa

throughput_voip_crdsa

throughput_video_crdsa

37

stream will affect the quality significantly. Another described less than 1% packet loss as

"good" for streaming audio or video, and 1-2.5% as "acceptable".

Fig.5.4 Dropping ratio

5.5 JITTER

Jitter is defined as a variation in the delay of received packets. Jitter is the variation in

latency as measured in the variability over time of the packet latency across a network. A

network with constant latency has no variation (or jitter). Packet jitter is expressed as an

average of the deviation from the network mean latency.

Fig.5.5 Jitter

0

0.5

1

1.5

2

2.5

3

4 5 6 7 8

Dro

pp

ing r

ati

o

Number of channels

Dropping ratio

dropping ratio_cbr_crdsa

dropping ratio_voip_crdsa

dropping ratio_video_crdsa

0.0610.0615

0.0620.0625

0.0630.0635

0.0640.0645

0.0650.0655

4 5 6 7 8

jitt

er (

ms)

Number of channels

Jitter

jitter_cbr_crdsa

jitter_voip_crdsa

jitter_video_crdsa

38

Total jitter (T) is the combination of random jitter (R) and deterministic jitter (D):

T = Dpeak-to-peak + 2× n×Rrms

In which the value of n is based on the bit error rate (BER) required of the link.

5.7 CONTROL OVERHEAD

Overhead is any combination of excess or indirect computation time, memory,

bandwidth, or other resources that are required to attain a particular goal. It can be

expressed as a percentage of non-application bytes (protocol and frame

synchronization) divided by the total number of bytes in the message.

Number/size of routing control packets sent by the protocol.

Calculated using counters while simulating with test flows.

Sometimes expressed as a ratio of control to data.

Indication of how efficiently a routing protocol operates

o High control overhead may adversely affect delivery ratio and latency under

higher loads.

Fig.5.7 Control overhead

0

1

2

3

4

5

6

4 5 6 7 8

over

hea

d

Number of channels

Overhead

overhead_cbr_crdsa

overhead_voip_crdsa

overhead_video_crdsa

39

COMPARISON BETWEEN DSA & CRDSA

(i) Number of channels vs Packet delivery ratio

80

85

90

95

100

105

4 5 6 7 8

pd

r

Number of channels

Packet delivery ratio

pdr_cbr_dsa

pdr_cbr_crdsa

80

85

90

95

100

4 5 6 7 8

pd

r

Number of channels

Packet delivery ratio

pdr_voip_dsa

pdr_voip_crdsa

0

50

100

150

4 5 6 7 8

pd

r

Number of channels

Packet delivery ratio

pdr_video_dsa

pdr_video_crdsa

40

(ii) Number of channels vs Throughput

0

50000

100000

150000

200000

250000

300000

4 5 6 7 8

thro

ug

hp

ut

Number of channels

Throughput

throughput_cbr_dsa

throughput_cbr_crdsa

0

50000

100000

150000

200000

250000

300000

4 5 6 7 8

thro

ugh

pu

t

Number of channels

Throughput

throughput_voip_dsa

throughput_voip_crdsa

0

50000

100000

150000

200000

250000

300000

4 5 6 7 8

thro

gh

pu

t

Number of channels

Throughput

throughput_video_dsa

throughput_video_crdsa

41

(iii) Number of channels vs Dropping ratio

0

5

10

15

4 5 6 7 8

dro

pp

ing r

ati

o

Number of channels

Dropping ratio

dropping ratio_cbr_dsa

dropping ratio_cbr_crdsa

0

2

4

6

8

10

12

14

4 5 6 7 8

dro

pp

ing r

ati

o

Number of channels

Dropping ratio

dropping ratio_voip_dsa

dropping ratio_voip_crdsa

0

5

10

15

20

25

30

4 5 6 7 8

dro

pp

ing r

ati

o

Number of channels

Dropping ratio

dropping ratio_video_dsa

dropping ratio_video_crdsa

42

(iv) Number of channels vs Jitter

0

0.1

0.2

0.3

0.4

4 5 6 7 8ji

tter

Number of channels

Jitter

jitter_cbr_dsa

jitter_cbr_crdsa

0

0.05

0.1

0.15

0.2

0.25

4 5 6 7 8

jitt

er

Number of channels

Jitter

jitter_voip_dsa

jitter_voip_crdsa

0

0.1

0.2

0.3

0.4

0.5

4 5 6 7 8

jitt

er

Number of channels

Jitter

jitter_video_dsa

jitter_video_crdsa

43

(v) Number of channels vs Control overhead

0

10

20

30

40

50

4 5 6 7 8

over

hea

d

Number of channels

Overhead

overhead_cbr_dsa

overhead_cbr_crdsa

0

10

20

30

40

4 5 6 7 8

over

hea

d

Number of channels

Overhead

overhead_voip_dsa

overhead_voip_crdsa

0

20

40

60

80

100

4 5 6 7 8

over

hea

d

Number of channels

Overhead

overhead_video_dsa

overhead_video_crdsa

44

CHAPTER 6

CONCLUSION

We focus on efficient spectrum sharing through distributed coordination. In

this system I proposed a Dynamic open spectrum sharing protocol, for a wireless network

that operates over the open spectrum. As a distributed protocol, DOSS allows for dynamic

control channels and arbitrary data channels. The control channels are robust to jamming and

are adaptive to traffic load, while the arbitrary data channels maximize the value of the

available spectrum. In DSA networks by using spectrum sharing the interference is reduced

and throughput is increased by utilizing in the available spectrum. This protocol supports

efficient multicast and with no synchronization and provides a selection that eliminates the

hidden and exposed terminal problems using well designed spectrum sharing. An analysis of

the DOSS protocol is done and it is validated through simulations and its performance is

evaluated. All these factors impact the performance of the protocol and thus it is taken into

consideration.

45

CHARTER 7

REFERENCES

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47

CONFERENCES & PUBLICATIONS

Presented a paper on 4th

National Conference on Advanced Computing and Communication

Systems (NCACCS’16) on April 4th

, 2016 held at Goverment College of Technology,

Coimbatore – 13.