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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.
.
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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
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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
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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
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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.
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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.
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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
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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
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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.
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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.