HRD Programme for Exchange of ICT Researchers/Engineers · Mr. Nipendra Kayastha Mr. Tapio J. Erke...
Transcript of HRD Programme for Exchange of ICT Researchers/Engineers · Mr. Nipendra Kayastha Mr. Tapio J. Erke...
HRD Programme for Exchange of ICT Researchers/Engineers
Through Collaborative Research
FINAL TECHNICAL REPORT
On
Cognitive Radio and Dynamic Spectrum Sharing in
Cognitive Radio
Submitted To
Asia-Pacific Telecommunity
Submitted By
Mr. Nipendra Kayastha
Mr. Tapio J. Erke
Dr. Teerawat Issariyakul
September 2008
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ACKNOWLEDGEMENT
We would like to express our sincere gratitude towards Asia-Pacific Telecommunity
for conducting HRD Programme for Exchange of ICT researchers through
Collaborative Research. This research opportunity provided us to explore some new
technologies which would be helpful in completing the project objectives.
I would like to personally thank my advisor Mr. Tapio J. Erke (Associate Professor,
AIT, Thailand) for his continuous help and guidance. His untiring willingness to help
and cheerful and friendly disposition made it very easy for me during the preparation
of this report. I am also thankful to Dr. Teerawat Issariyakul (Project Coordinator,
TOT Public Company Limited, Thailand) for providing me with the opportunity to
carry out this joint research with Asia-Pacific Telecommunity.
Also finally, I would like to extend my gratitude to Dr. R.M.A.P. Rajatheva
(TC/ICT coordinator, AIT, Thailand) for his valuable suggestions.
Project Members
Mr. Nipendra Kayastha
Mr. Tapio J. Erke (Advisor)
Dr. Teerawat Issariyakul (Project Coordinator)
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ABSTRACT
The main objective of this report is to study overlay dynamic spectrum sharing for
cognitive radios. The report provides a comprehensive description of cognitive radio
and a detailed overview of different overlay dynamic spectrum sharing techniques
that has been proposed so far.
The report aims to cover different aspects of Dynamic Spectrum Sharing in cognitive
radio environment for providing fairness among the cognitive radio users. Cognitive
users being the secondary users of the system are force to terminate whenever the
licensed users reappear and demand for the spectrum which is being used by the
cognitive users. The force termination leads to the dropdown of the ongoing
communication whose degradation is more severe than that of when the users are not
able to connect. These issues are studied and explored in this report with relevant case
studies and results. These case studies and background overview of cognitive radio
technologies are then used to formulate a simulation test bed which will be used to
study traffic behavior in a time dependent environment taking in consideration the
performance and fairness issues when using different overlay dynamic spectrum
sharing techniques.
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TABLE OF CONTENTS
Chapter Title Page
Acknowledgement i
Abstract ii
Table of Contents iii
List of Abbreviations vi
List of Figures vii
List of Tables ix
List of Symbols x
1 Introduction 1
1.1 Background 1
1.2 Objective 2
1.3 Report Outline 3
2 Literature Review 4
2.1 Dynamic Spectrum Sharing 4
2.2 Types of Dynamic Spectrum Sharing 4
2.2.1 Underlay Spectrum Sharing 4
2.2.2 Overlay Spectrum Sharing 5
2.3 Cognitive Radio 5
2.4 Issues of Cognitive Radio 7
2.4.1 Identification and Protection of Primary Users 7
2.4.2 Coexistence and Fairness issues in Spectrum Sharing 10
2.5 Significant Work in Overlay Dynamic Spectrum Sharing 11
2.5.1 Spectrum Pooling 11
2.5.2 Prioritized Primary Access for Spectrum Sharing 14
2.5.3 Spectrum Handoff using Optimal Channel Reservation for
CognCognitive Radio 17
2.5.4 Spectrum Sharing Among the Cognitive Users 19
2.6 Discussion 24
2.7 Comparison 25
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TABLE OF CONTENTS
Chapter Title Page
3 Research and Development 28
3.1 Methodology 28
3.2 System Model 29
3.2.1 Spectrum Sharing with Uncontrolled Access Scheme 30
3.2.2 Spectrum Sharing with Controlled Access Scheme 31
3.2.3 Spectrum Sharing with Cognitive Spectrum Hopping 31
3.2.4 Spectrum Sharing with Buffering of Cognitive Users 31
3.3 System Traffic Model 32
3.4 Performance Measurements 32
3.5 Performance and Fairness Analysis Methods 33
3.5.1 Analytical Method 34
3.5.2 Simulation Method 34
4 Analytical model 35
4.1 Markov chain Model for Uncontrolled Access Scheme 35
4.2 Markov chain Model for Controlled and Cognitive Spectrum
hophopping 35
4.3 Markov chain model for Spectrum sharing with Cognitive
BufBuffering 35
5 Simulation Model 38
5.1 Overview 38
5.2 Simulation Flow chart 38
5.3 Input Parameters 40
5.4 Traffic Generation: Arrival and Termination Processes 40
5.4.1 Arrival Process 40
5.4.2 Service Time 40
5.5 Spectrum Etiquette Rules 41
5.6 Admission Control Unit 41
5.7 Performance Parameter Calculation 41
5.7.1 Holding Time Measurement 42
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5.7.2 Service Time Measurement 42
5.7.3 Performance Parameter Calculation 43
TABLE OF CONTENTS
Chapter Title Page
5.8 Validation of Simulation Model 43
5.8.1 Simulation Parameters 44
6 Preliminary Results 46
6.1 Study of Different Access Schemes 46
6.2 Performance Analysis of Different cases 46
6.2.1 Effect of Buffering of Cognitive users in different Access
Schemes 60
6.3 Overview of Result 63
7 Conclusion 65
References 66
Apppendix A 69
Apppendix B 72
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LIST OF ABBREVIATIONS
A/D Analog-to-Digital
AGC Automatic gain control
CR Cognitive Radio
DFS Dynamic Frequency Selection
DSA Dynamic Spectrum Access
DSS Dynamic Spectrum Sharing
FCC Federal Communication Commission
FCC Federal Communications Commission
GoS Grade of Service
IEEE Institute of Electrical and Electronics Engineers
LAN Local Area Networks
OFDM Orthogonal Frequency Division Multiplexing
PLL Phase locked loop
PU Primary User
QoS Quality-of-Service
SARA Spectrum Agile Radios
SDR Software Defined Radio
SR Software Radio
SU Secondary User
TPC Transmit Power Control
VCO Voltage-Controlled Oscillator
WiMAX Worldwide Interoperability for Microwave Access
WLANs Wireless Local Area Networks
xG Next Generation
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LIST OF FIGURES
Figure Title Page
1.1 Spectrum Usage in an Urban Area 1
2.1 Spectrum Hole Concept 6
2.2 Markov model for Controlled Channel Assignment 12
2.3 Markov model for Uncontrolled Channel Assignment 13
2.4 Three Dimensional Markov Chain Model 15
2.5 Modified Markov Chain Model for Reservation Case 17
2.6 Rate diagram of state (i, j) with Spectrum Handoff and Channel Reservation
Reservati Reservation 18
2.7 Frequency Channel use by two different types of radio systems A & B 20
2.8 Markov Chain to Model the Unlicensed Spectrum Access Process with
waiting waiting 21
2.9 Packing Behavior Example 22
3.1 System Diagram of Cognitive Radio Environment 29
3.2 Frequency Band of two users 29
3.3 The System Model 30
4.1 State Diagram for Controlled Access Scheme with Buffering 36
5.1 Simulation Model 38
5.2 Simulation Flow chart 39
5.3 Statistical Data measurement in Simulation Model 42
5.4 Measurement of Holding Time 42
5.5 Measurement of Carried Traffic 43
6.1 Performance Analysis of 1 server with 0.2 Erl Primary Offered Traffic 48
6.2 Performance Analysis of 1 server with 0.4 Erl Primary Offered Traffic 49
6.3 Performance Analysis of 2 servers with 0.4 Erl Primary Offered Traffic 50
6.4 Performance Analysis of 2 servers with 0.7 Erl Primary Offered Traffic 51
6.5 Performance Analysis of 5 servers with 1 Erl Primary Offered Traffic 52
6.6 Performance Analysis of 5 servers with 2.5 Erl Primary Offered Traffic 53
6.7 Performance Analysis of 20 servers with 10 Erl Primary Offered Traffic 54
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6.8 Performance Analysis of 20 servers with 14 Erl Primary Offered Traffic 55
LIST OF FIGURES
Figure Title Page
6.9 Performance Analysis of 50 servers with 20 Erl Primary Offered Traffic 56
6.10 Performance Analysis of 50 servers with 30 Erl Primary Offered Traffic 57
6.11 Performance Analysis of 100 servers with 40 Erl Primary Offered Traffic 58
6.12 Performance Analysis of 100 servers with 75 Erl Primary Offered Traffic 59
6.13 Performance Analysis of 1 server with 0.4 Erl Primary Offered Traffic with
buffer buffer 61
6.14 Performance Analysis of 2 servers with 0.7 Erl Primary Offered Traffic with
buffer buffer 61
6.15 Performance of Cognitive Users for different give up time 63
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LIST OF TABLES
Table Title Page
2.1 Comparison of different spectrum Overlay Approaches 25
3.1 Different Parameters for Performance and Fairness Analysis 33
5.1 Input Parameters 40
5.2 Performance Parameter Calculation 43
5.3 Initial Simulation Parameters 44
5.4 Simulation Parameters to Validate Erlang Loss System 44
5.5 Simulation Parameters to Validate Different Access Schemes 44
6.1 Different Access Scheme Considered for Simulation 46
x
LIST OF SYMBOLS
Symbols Definition
Equal number of Sub bands
Total Bandwidth
Capacity of Secondary Users
Bandwidth of i type user
Weight Fairness
Number of primary sub bands
Number of Primary users
Number of Players
Number of spectrum bands
Number of Type i radio system
Steady State Probability
Maximum allowable Blocking Call Probability
Blocking Probability of the Cognitive users
with r reserve channel
Block Call Probability of Secondary Users
Maximum allowable Drop call Probability
Drop call Probability of Secondary Users
Forced Termination Probability of cognitive
users
Forced Termination Probability of the Cognitive users
with r reserve Channel
State Probability
Number of reservation channels for cognitive user
Number of reserve channel for primary user
Time Interval of Secondary users to vacate
Time limit for simulation
Time limit for simulation
Utility Factor
Players Payoff
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LIST OF SYMBOLS
Symbols Definition
Envy factor
Random variable which describes the occupied
bandwidth
Guilt factor
Bandwidth Utilization
Transition rate from state (i, j) to state (i−k, j +1)
Arrival rate of Cognitive users
,
Arrival rate of Primary/Owner/Licensed users
Arrival rate for radio system i
Forced termination rate.
Small Observation period
Termination rate of Cognitive users
Termination rate of Primary users
Termination rate for radio system i
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CHAPTER 1
INTRODUCTION
1.1 Background
Today the wireless communication technology is developing at a rapid pace and the availability
of the network is being more ubiquitous. But this advancement in the technology is creating
more challenges in the field of communication. One key area that is being affected is spectrum
access policies and spectrum management for wireless network since its demand is increasing
day by day. But the radio spectrum which is a natural resource is scarce and hard to manage on
real time.
Moreover, this concept of scarcity of spectrum availability is more prominent from the intensive
usage of the spectrum below 3 GHz in an urban area as shown in Figure 1.1. It can be seen that
the usage of the frequency band below 3 GHz is drastically high whereas the usage of spectrum
at higher frequency is quite low. This shows that, not all the radio spectrums are occupied by the
users at given time and location making its usage quite random in nature. A quick look of the
Fig 1.1 shows that, spectrum usage in 3-4 GHz is quite moderate and is low at 4-5 GHz. This
availability of the unused spectrum is opening a new paradigm in the spectrum access policies.
Figure 1.1: Spectrum Usage in an Urban Area
(Adapted from [Brodersen et al., 2004])
These unoccupied spectrums can be considered as Spectrum Holes in the spectrum band which
can be defined as “a band of frequencies assigned to a primary user, but, at a particular time and
specific geographic location, the band is not being utilized by that user” [P. Kolodzy et al.,
2001, cited in Haykin, 2005].
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This is in fact contradictory to the very fact that there is spectrum shortage because we see here
that the most of the spectrum are free and this problem of scarcity in fact is outcome of
regulatory and licensing process [Brodersen et al., 2004]. So some kind of approach or radio
system is needed which can extend the usage of spectrum holes to other unlicensed users without
interfering with the present users who have legacy right to use it.
As a consequence, a new radio system called Cognitive Radio (CR) [Mitola III, 2000] was
proposed which is able to sense the spectral environment for spectrum holes, detect the
presence/absences of legacy users over a wide available band, and use the spectrum only if
communication does not interfere with the legacy users [Brodersen et al., 2004]. Thus the
cognitive radio has been proposed by FCC in order to efficiently use the available spectrum by
learning and changing certain parameters like frequency selection, transmit power, carrier
frequency, and modulation schemes to adapt to the variation in the available spectrum in order to
maximize the utilization of radio spectrum [Haykin, 2005]. The key characteristic of CR is
dynamic spectrum allocation (DSA) whose ability is to adapt their operation by sensing the radio
environment around it in opportunistic manner [Akyildiz et al., 2006].
This usage of spectrum holes allows sharing of the licensed bands by unlicensed or cognitive
users provided that there is no interference to the licensed users. This phenomenon of sharing is
called Dynamic Spectrum Sharing (DSS) which is the key characteristic of DSA which is
responsible for providing fairness and efficiency in spectrum access among the different users of
cognitive radio and also among licensed and unlicensed users (or Primary users and Secondary
Users). Although sharing can be either in the form of underlay using Ultra Wide Band (UWB) or
in the form of opportunistic overlay usage of spectrum holes by the cognitive radio [Nekovee,
2006]. In this research work focus is given to the overlay spectrum access and the fairness issues
concerned with it.
In case of overlay spectrum access, the cognitive users have to vacate the spectrum whenever the
legacy user wants to use it for their communication. In order to do so the CR should be able to
detect the presence of the Primary users in order to ensures noninterference to licensed or legacy
users. So the spectrum sensing should involve more sophisticated techniques than simple
determination of power in a frequency band. Also the force termination of cognitive users affects
the communication link decreasing the overall throughput and performance of the cognitive radio
users. This evokes the issue of fairness among the cognitive radio as there is a chance of force
termination of the communication by the licensed or legacy users [Zhu et al., 2007].
1.2 Objective
The main objective of this report is to give an insight into the emerging technology called
Dynamic Spectrum Sharing using cognitive radios and to discuss the fairness issues related to the
cognitive radio. The main focus of this report can be summarized as:
1. To study Dynamic Spectrum Sharing and related issues.
2. To study various overlay spectrum sharing techniques which address fairness issues in
the cognitive radio environment.
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3. To provide a basis for the further research work in the area of traffic modeling and
analysis under the Cognitive radio environment.
1.3 Report Outline
The main objective of this report is to study fairness issues under various Dynamic Overlay
spectrum sharing techniques in Cognitive Radio environment. The outline of this report is as
follow. The basic introduction is given in Chapter 1. Chapter 2 discuss the details of Dynamic
Spectrum Sharing Cognitive Radios and provide different traffic models for spectrum sharing
with detailed reviews of the spectrum sharing techniques for both licensed and unlicensed
spectrums in the literature. Chapter 3 presents research methodology as well as the current status
of the project. Chapter 4 gives the analytical modeling overview which is followed by simulation
model and parameters in Chapter 5. In Chapter 6 preliminary results of the proposed simulation
test bed is discussed. Finally the conclusion is given in Chapter 7.
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3.5.1
CHAPTER 2
LITERATURE REVIEW
2.1 Dynamic Spectrum Sharing
The need for efficient usage of spectrum has always been one of the concerned topics for
regulating bodies. With the knowledge that a majority of licensed spectrum is underutilized in
time and frequency, the concept of dynamic spectrum allocation (DSA) has been proposed as a
solution to the potential spectrum scarcity problem, where unlicensed users temporarily
“borrow” frequency bands from spectrum licensees while simultaneously respecting the rights of
the incumbent license holders. The key characteristic of DSA is Dynamic Spectrum Sharing
(DSS) which allows the legacy user to share their unused spectrum with other unlicensed users
by providing fair and efficient spectrum allocation between them [Ji and Ray Liu, 2007]. DSA
has been already employed in cellular mobile communication. But we should keep in mind that
the existing DSA schemes for general cellular networks are not suitable for a CR network.
Because the number of available spectrum is not fixed owing to fact that cognitive user uses the
spectrum form licensed users and which could change from one region to another in case of CR
networks.
For CR to utilize the underutilize spectrum, DSA networks employing cognitive radios are being
considered. In order to utilize these „Spectrum holes‟, the FCC has issued a Notice of Proposed
Rule Making [FCC I, 2003] advancing CR technology as a candidate to implement negotiated or
opportunistic spectrum sharing. Today, DSA and CR are like interchangeable terms which have
revolutionize the telecommunications industry, significantly changing the way we use spectrum
resources, and design wireless systems and services. Cognitive radios have been identified as a
key enabler for DSA networks, where the operating parameters of the unlicensed device can be
rapidly reconfigured to the changing requirements and conditions of the transmission
environment.
That‟s why there are number of projects that have their focus on DSA architecture where the CR
operates autonomously. Defense Advance Research Project Agency (DARPA) xG Program in
USA [DARPA] is one of the most motivated projects for DSA based on CR technology.
2.2 Types of Dynamic Spectrum Sharing
The DSS functions in a way such that it tries to sense the spectrum looking for spectrum
opportunity. In case there is no primary user, the device identifies the spectrum opportunity and
makes the legacy spectrum available to the licensed users and at the same time avoiding
interference. There are two techniques for spectrum sharing as defined in [Berlemann et al.,
2005]:
2.2.1 Underlay Spectrum Sharing
This method of spectrum sharing provides access to the radio spectrum to those systems that
have minimal transmission powers. The signals are emitted over a large band of spectrum so as
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to put the unwanted signal power that might be caught by serving licensed radio devices below
the designated threshold level. The transmission power being limited in order to reduce
interference, the shared spectrum can be used if the interference is contained below the threshold.
For this purpose, FCC has defined space between the original noise floor and the licensed signal
of the incumbent radios called the “new opportunities for spectrum use”. Spread spectrum,
Multi-Band Orthogonal Frequency Division Multiplex (OFDM) or Ultra-Wide Band (UWB) are
examples of this kind of technique.
2.2.2 Overlay Spectrum Sharing
In this method, the network is accessible only when the spectrum is unoccupied by licensed
users. Due to this, only a small fraction of the radio spectrum is available as open frequency band
for unlicensed users and even this availability is difficult to trace. So, for the identification of
under-utilized spectrum without affecting other legacy radios, flexible spectrum access
techniques are implemented using CR. This kind of opportunistic spectrum access to under-
utilized spectrum is referred as overlay spectrum sharing.
2.3 Cognitive Radio
Cognitive radio (CR) is a paradigm for wireless communication in which either a network or a
wireless node changes its transmission or reception parameters to communicate efficiently
without interfering with licensed users. This alteration of parameters is based on the active
monitoring of several factors in the external and internal radio environment, such as radio
frequency spectrum, user behavior and network state.
The idea of CR was first introduced officially in the article by Joseph Mitola III and Gerald Q.
Maguire, Jr. (1999). Mitola described it as a particular extension of Software Define Radio with
learning and reasoning capabilities that works in application layer and higher [Mitola III, 2000].
Since CR is a new concept and it means different thing to different audiences, the FCC view is
quite simple and global to follow. According to [FCC II, 2003] “Cognitive Radio is a wireless
node or network that is capable of dynamically sensing and locating unused spectrum segments
and communicating by using the unused spectrum segments in ways that cause no harmful
interference to the Primary Users of the spectrum”.
The basic idea behind cognitive radio is the utilization of unused frequency bands of a primary or
a licensed user by a secondary or an unlicensed user without interfering the primary user‟s
communication. If we scan the portions of radio spectrum we would find that some frequency
bands are largely unoccupied most of the time while some other frequency bands are only
partially occupied and the remaining frequency bands are heavily used [Haykin, 2005]. This
leads to a term called spectrum holes which is defined in [P. Kolodzy et al., 2001, cited in
Haykin, 2005] as “a band of frequencies assigned to a primary user, but, at a particular time and
specific geographic location, the band is not being utilized by that user”. Thus from these
theories we can formulate that in order to improve the spectrum utilization more efficiently, there
should be some access technique which will allow the secondary users to access the spectrum
holes which are unoccupied by the licensed or primary user at a particular location and time as
shown in Figure 2.1. Figure 2.1 merely shows the availability of the spectrum holes and also
shows how the cognitive users or secondary users are moved to another spectrum hole when
there is need of the spectrum band by the primary users. In doing so the cognitive radios alters
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the power level so that to minimize the interference that may be caused due to secondary users
[Akyildiz et al., 2006]. Thus we see that knowing the availability of the spectrum hole only is
not enough to decide the usage of the unused spectrums. Many factors like frequency selection,
modulation schemes, and power level should be considered to capture the variation in radio
environment so as to avoid possible interference to other users. CR promises all these functions
and helps to utilize the spectrum band more efficiently.
“Spectrum Holes”
Time
Power
Frequency
Spectrum in Use
Dynamic
Spectrum
Access
Figure 2.1: Spectrum Hole Concept
( Adapted from Haykin, 2005 )
The Federal Communications Commission (FCC) has identified in [FCC III, 2005] the
following features that cognitive radios can incorporate to enable a more efficient and flexible
usage of spectrum:
Frequency Agility – The radio is able to change its operating frequency to optimize its use
in adapting to the environment.
Dynamic Frequency Selection (DFS) – The radio senses signals from nearby transmitters to
choose an optimal operation environment.
Adaptive Modulation – The transmission characteristics and waveforms can be
reconfigured to exploit all opportunities for the usage of spectrum
Transmit Power Control (TPC) – The transmission power is adapted to full power limits
when necessary on the one hand and to lower levels on the other hand to allow greater
sharing of spectrum.
Location Awareness – The radio is able to determine its location and the location of other
devices operating in the same spectrum to optimize transmission parameters for increasing
spectrum re-use.
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Negotiated Use – The cognitive radio may have algorithms enabling the sharing of spectrum
in terms of prearranged agreements between a licensee and a third party or on an ad-hoc/real-
time basis.
CR is an intelligent radio which is capable of listening to the outer environment which uses
methodology of understanding by learning the environment in order to adapt its internal states to
the requirements by changing the statistical variables like transmit power, modulation schemes,
carrier frequency to provide more reliable and efficient spectrum access. For this CR needs to be
programmed as it is not possible to change the hardware to do so. This is where the software
defined radio (SDR) comes into play.
SDR is the basis for the cognitive radio which was also introduced by Mitola in his dissertation.
At this point it is relevant to discuss the issues related to software radio (SR) and software
defined radio (SDR). SR is a radio which directly samples the antenna output but in case of SDR
receive signals are sampled after appropriate band filtering. That is why the SDR is considered as
practical version of SR since the analog to digital converter that can be employed in SR is not
realizable till today [Jondral, 2005]. But for future developments this direct digitization should
be the major goal for the researchers as it will increase the efficiency of the sampling.
On the other hand a cognitive radio (CR) is an SDR that is capable of sensing its environment,
tracks changes, and reacts upon its findings. We can consider a CR as an automatic unit which
frequently exchanges information with the networks accessible to it and also with other CRs in
the vicinity of each other. From this we can say that a CR is a redefined SDR [Jondral, 2005].
2.4 Issues of Cognitive Radio
Although cognitive radio opened a new dimension in spectrum access and spectrum sharing,
there are some issues which should be acknowledged in order to implement the cognitive radio
practically. There are two challenges associated with cognitive radios:
Identification of and Protection of Primary Users
Coexistence and Fairness issues in Spectrum Sharing
2.4.1 Identification and Protection of Primary Users
The cognitive radios (i.e. Secondary Users or unlicensed users) are considered to have lower
priority than the primary users (i.e. licensed users) for accessing the spectrum. And they use the
spectrum whenever the primary users are not using it which is considered as spectrum
opportunity for cognitive users. Spectrum opportunities can be detected if the cognitive radio can
detect the primary users such that in doing so it can have the information of the spectrum which
are not being used by the primary users. Also they have to vacate the spectrum whenever the
primary user reappears in the scenery within some time interval. That is why one of the
fundamental requirements of the cognitive users is to avoid interference to potential primary
users in their vicinity. Therefore, cognitive radios should be able to detect Primary Users
correctly so as to know the presence of spectrum opportunity through continuously sensing.
Detecting these spectrum opportunity is not an easy task and there are numerous technique for
detecting it.
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Since the Primary system allows the use of the unused spectrum to cognitive users, it should be
kept in mind that doing so the primary system should not be change. This is another issue
regarding the stability of the primary system. In order to retain the integrity of the primary
system, spectrum pooling can be used. Spectrum pooling is another innovative strategy which
can be used for spectral sharing, where all the spectral range from different spectral owner is
brought together into a common pool. The idea was to host this common pool by any licensed
system and let the secondary user to temporarily use the spectrum resources when the licensed or
primary user was idle. Doing so the licensed or primary system remains unchanged but other
secondary user can then access the spectrum pool whenever the primary users were idle. To put
in a simpler way, spectrum pooling is a collection of free spectral bands of licensed users that
can be accessed by the unlicensed users without destroying the transmission quality of the
licensed system. It is actually a resource sharing strategy, which allows a license owner to share
unused part of his licensed spectrum with the unlicensed user, until he needs it himself.
However, the license owner has the absolute priority to access the shared spectrum. This means,
the secondary user has to monitor the channel and extract the channel allocation information
(CAI), i.e. it has to detect, which parts of the shared spectrum the owner system accesses to, in
order to immediately vacate the frequency bands being required by the licensed owner and to
gain access to the frequency bands, which the license owner has stopped using. For this the
secondary system needs to be highly flexible which is achieved by using Orthogonal Frequency
Division Multiplexing (OFDM). A spectrum pooling architecture based on Orthogonal
Frequency Division Multiplexing (OFDM) has been presented by Weiss and Jondral [Weiss and
Jondral, 2004].
There are some techniques which have already been proposed for reliable detection of the
primary users. According to [Akyildiz et al., 2006], for detecting spectrums opportunity,
spectrum sensing can be classified as transmitter detection, cooperative detection, and
interference based detection.
2.4.1.1 Transmitter Detection
Transmitter detection is based on the detection of weak signal from a primary user transmitter
through local observation. The approach uses the concept of beaconing in which the primary user
broadcasts beacon in order to signal the availability of the licensed spectrum for cognitive user
usage. The beacon can permit or deny the usage of spectrum. So the usage of licensed spectrum
by the cognitive users is controlled and there is no need to detect the spectrum opportunity by the
cognitive radio. However, this method may not be reliable due to the fact that the beaconing
signal may not reach the cognitive user [Nekovee, 2006].
Other detection methods include match filter detection, energy detection and cyclostationary
feature detection [Cabric et al., 2004]. Match filters maximizes the received signal to noise ratio
and are thus suitable for primary user detection. However, it requires information of the
demodulation parameters like modulation type, order and pulse shaping formats beforehand in
order to be able to detect and decode the signal. Since the cognitive radio needs prior
information, dedicated receivers are required for primary users and this is the main drawback of
this approach.
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The energy detection method averages the frequency bins of a Fast Fourier transform to detect
the presence of use. The drawback of this simple approach was that it was unable to differentiate
between modulated signals, noise, and interference [Cabric et al., 2004].
The cyclostationary feature detection exploits the fact that modulation offered to any random
stationary process results in built in periodicity [Cabric et al., 2004] as a result of which
parameters can be set in cognitive radio to detect the primary users. These modulation signals are
termed as cyclostationary since their static mean and autocorrelation exhibits periodicity. Auto
correlation function can be used to detect any background noise since noise is a wide sensed
stationary signal without correlation. Thereffore, cyclostationary feature is better than energy
detector at detection.
2.4.1.2 Cooperative Detection
The performance of Transmitter detection techniques is limited by received signal strength which
may be severely degraded due to multi-path fading and shadowing. In most of the case the
primary users are separated from the cognitive users so there is no interaction between them. So
transmitter detection methods cannot avoid the interference caused by the lack of primary
receiver information. Also the transmitter detection methods cannot prevent the hidden terminal
problem which takes place when a cognitive radio cannot detect all of the radios with which it
might interfere because some of the radios are hidden from it [Akyildiz et al., 2006].
That is why new approaches are required to exploit for higher sensing and detection capabilities.
One method can be using the information of other cognitive radios in cooperative manner to
reliably detect the primary users. Analytical and simulative results reveal that the mentioned hard
requirements on the detection probability can only be fulfilled with a cooperation diversity
approach. Such cooperative sensing by exchanging sensing information between cognitive
radios, have a better chance of detecting the Primary User compared to individual sensing [Guo
et al., 2006].
Therefore, individual detection is not enough and cooperative detection between users is required
to combine the sensing results to have minimum probability of interference to the primary users.
Each user measures the local received signal. The local signals are then exchanged between all
the cognitive radio users which provide a global decision on the primary user being present or
not. Thus, cooperative detection is a spectrum sensing method in which information from
multiple cognitive radio users is included for primary user detection. Hence, cooperative sensing
provides more accurate result because the uncertainty associated with a single user‟s detection
can be minimized. In [Brodersen et al., 2004] a centralized cooperative network called
CORVUS is proposed where the cognitive users forms a group and they use group control
channel to communicate with each other and a universal control channel to communicate
between different groups. The access point collects the sensing information from all the users
which then use to alert the cognitive user about the primary user reappearance. The problem in
cooperative detection method is that it is difficult to combine the results of all the secondary
users because different secondary users have different sensitivities and sensing times. And they
also cause adverse effect in the network due to additional operations and overhead traffic.
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2.4.1.3 Interference based Detection
This detection method takes into account the interference present in the system which can be
controlled at the transmitter by changing the radiated power, the out-of-band emissions, and
location of individual transmitters [Akyildiz et al., 2006]. But interference actually takes place at
the receivers causing a progressive degradation of the signal and increasing the noise floor due to
the presence of various sources of interference. Therefore, a new model for measuring
interference, known as interference temperature was proposed by the FCC in 2003 [FCC IV,
2003]. The interference temperature acts as a threshold value that can be tolerated by the primary
user which helps in quantifying and managing the sources of interference in a radio environment.
The measurement of an interference temperature limit provides a worst case depiction of the
radio environment in a particular frequency band and at a particular geographic location such
that the receiver could operate satisfactorily. In other words, the cognitive radio must be able to
estimate the interference temperature that the primary user can tolerate. The interference
temperature thus provides an accurate measure for the acceptable level of RF interference in a
particular band of frequency. For this detection method, it can be considered that the primary
user can tolerate interference for certain time units such that the secondary user can take the
measurement of the interference temperature within that particular time unit and proceed for the
detection of the primary user [Brodersen et al., 2004]. After time unit all the secondary
users must vacate the frequency band of the primary user. The cognitive radio user should have
it‟s transmit power at a certain level such that it does not raise the noise floor of the primary
users beyond a specific value. Any transmission in a band is considered to be harmful if it
increases the noise floor above the interference temperature limit and if the interference
temperature is not exceeded in a frequency band then that frequency band is made available for
the use of secondary users.
2.4.2 Coexistence and Fairness issues in Spectrum Sharing
The fairness here means that the cognitive radio users have to terminate their communication
when the primary user reappears. This evokes the issue of fairness among the cognitive radio as
that can degrade the overall performance of the cognitive users [Zhu et al., 2007]. Even though
there are many techniques to sense the primary or legacy users in order to minimize the forced
termination of the cognitive radio, there are still some issues as how to manage the sharing
efficiently and fairly. Another challenge in DSA or CR systems is the existence of the different
cognitive radio systems in the network which constantly try to access the spectrum leading to
unfair spectrum sharing them also. As different cognitive users will be competing for the same
spectrum holes, there can be fairness and optimization issues regarding resource utilization
among the cognitive users due to heterogeneity among themselves [Wang et al., 2007]. There is
no guarantee of fair resource allocation among different cognitive users. So there should be a
level of coordination and cooperation between both primary users and secondary users. There
should be cooperation between the secondary users and also between the primary and secondary
users so as to access the spectrum opportunities effectively. For this a set of etiquette rules
should be defined so that the cognitive and primary radio system can operate in cooperative
manner. These etiquette rules should be made fair in order to promote effective sharing of
spectrum among devices. Spectrum etiquette is a set of rules for radio resource management to
be followed by radio systems that operate in an unlicensed band. It defines the rules for the
behavior of radio system in order to achieve fairness in access to shared radio resources and for
11
efficient usage of the spectrum. Although spectrum etiquette does not define a protocol or a
algorithm for doing so. As a matter of fact each radio system can apply its own algorithm within
the constraints of the spectrum etiquette. So basically spectrum etiquette provides a framework
for behavior for radio system which may restrict the degree of freedom for management of radio
resources by each radio system [Mangold and Challapali, 2003]. So the spectrum etiquette rule
is generally defined before any communication link is established.
2.5 Significant Work in Overlay Dynamic Spectrum Sharing
This report is more concentrated toward overlay spectrum sharing techniques where the
unlicensed users share the spectrum with the legacy users without interfering with the legacy
users maintaining fairness among the cognitive users.
Some of the approaches are discuss here in order to formulate the base of this report. They are:
2.5.1 Spectrum Pooling
Spectrum pooling is another innovative strategy which can be used for spectral sharing, where all
the spectral range from different spectral owner is brought together into a common pool. The
idea of spectrum pool was first introduced in [Mitola III, 1999], where depending on physical
characteristic of the spectrum, Mitola III defines four spectrum pools that allows to realize this
very concept of spectrum pooling. The four spectrum pools were [Mitola III, 1999], very low
band (26.9 - 399.9 MHz), low band (404 - 960 MHz), mid band (1390 - 2483 MHz) and high
band (2483 - 5900MHz). Depending on bandwidth requirement, propagation distance and other
traffic characteristics one of these pools can be used.
The idea was to host this common pool by any licensed system and let the secondary user to
temporarily use the spectrum resources when the licensed or primary user was idle. Doing so the
licensed or primary system remains unchanged but other secondary user can then access the
spectrum pool whenever the primary users were idle without destroying the transmission quality
of the licensed system. It is actually a resource sharing strategy, which allows a license owner to
share unused part of his licensed spectrum with the unlicensed user, until he needs it himself.
However, the license owner has the absolute priority to access the shared spectrum.
[Capar et al., 2002] defines spectrum pools as “contiguous spectrum, which can be used by
renter process (=cognitive users). According to [Capar et al., 2002], these pools can be grouped
into pool group which supports the same kind of application. The pool can be characterized by
its bandwidth Bi which can be divided into equal bandwidth sub bands bi. Then the maximum
number of communication links n = . The renters can use these subbands as long as the
licnesed user does not need it. If the licensed user needs the subbands the renter user or cognitive
user are using then the renters has to leave the subbands within the time interval Tp. Depending
upon the re appearance of the licensed users, the renter process (=cognitive users) have to cease
their transmission which mean deteorating their mode of communication. So reappearance of
licensed users can be model in two ways [Capar et al., 2002].
1. Controlled access scheme: where the licensed users searches for the free spectrum within its
frequency range. Being the legacy user, it has the right to reclaim the spectrum used by the
12
unlicensed or cognitive users who are operating within the same band. In doing so licensed
users must be able to detect the presence of the cognitive users and possibly even to
communicate with them. The communication of the renters (=cognitive users) can persist as
long as the there are channel for the licensed users to process for. That means if all the sub
bands are full then the licensed user can occupy the sub bands used by the renters.
2. Uncontrolled access scheme: Where the licensed users have no knowledge of whether a sub
band is occupied by a renter (cognitive users) or not, so it treats all the sub bands as free sub
bands. In this case the renter‟s process can be forced to be terminated or interrupt even if
there are other free bands available.
For analyzing both the cases, these processes were modeled as Markov model with Poisson
arrival rates of λP and λC for the owner process (=licensed users) and renters (=cognitive users)
process with service rate of µO and µR respectively. The number of sub bands was assumed to be
m which was supposed to be share by both the users. The Markov model for both case are shown
in Figure 2.2 and Figure 2.3 respectively.
.
0,0 0,1
λC
µC
λC
2µC
0,n-10,j 0,n
λC
jµC
……... ……...
(j+1)µC
λC λCλC
(n-1)µC nµC
1,0 1,1 2µC 1,n-11,jjµC…….. ……...
(j+1)µC(n-
1)µC
λP
λP
λpλPλpµP
2µP
µPµP
λpλp
2µP 2µP
i,0 i,1µC 2µC
i,jjµC
……...
(i+1)µP
λp
λp
iµP
iµP iµP
(i+1)µP
……
……
……
λP
n-1,0 n-1,1
λC
µC
(n-1)µP(n-1)µP
n,0
nµP
……
……
Cognitive Process, j
Pri
ma
ry P
roce
ss,
i
µP
λPλC
µC
λP
λP
λP
λP
λC
……
……
λC
λC λC
λC λC λC
λP
λP
λP
λP
λP
λP
i is for Active Primary
Users
j is for Active Cognitive
Users Arrival rate of Primary λP
µP Termination rate of Primary
λC Arrival rate of Cognitive
µC Termination rate of Cognitive
Figure 2.2: Markov model for Controlled Channel Assignment
( Adopted and Modified from Capar et al., 2002 )
Since licensed user does not know whether the renter processes are occupying the pool or not so
it treats all the pool as free in case of uncontrolled access scheme. Due to this the renter process
13
may have to terminate forcefully with the rate even if other spectrums are free.
This means that the forced termination of the renter process is possible even if the system is not
fully loaded which tends to increase with the increase of the licensed users in the system. Unlike
controlled channel scheme, the forced termination can occur in every state except the state (i,0),
i = 0,1….n. Clearly we can say that the forced termination is much higher in this case than the
controlled channel assignment.
Also it should be kept in mind that the forced termination is independent of the arrival rate λC in
uncontrolled access scheme case as the termination of the renter process depends on the size of
the licensed process which is independent of λC.
0,0 0,1λC
µC
λC
2µC
0,n-10,j 0,nλC
jµC
… ……...
(j+1)µC
λC λCλC
(n-1)µC nµC
1,0 1,1 2µC 1,n-11,jjµC…….. ……...
(j+1)µC(n-
1)µC
λPµP
2µP
µPµP
2µP 2µP
i,0 i,1µC 2µC
i,jjµC
……...
(i+1)µP
iµPiµP
iµP
(i+1)µP
….
……
……
n-1,0 n-1,1
λC
µC
(n-1)µP (n-1)µP
n,0
nµP
……
……
Cognitive Process, j
Pri
ma
ry P
roce
ss, i
µP
λPλC
µC
λP
λP
λP
λP
λC
……
……
λC
λC λC
λC λC λC
i is for Active Primary
Users
j is for Active Cognitive
Users
Arrival rate of Primary λP
µP Termination rate of Primary
λC Arrival rate of Cognitive
µC Termination rate of Cognitive
λFi,j
Force Termination Rate of
Cognitive Users
λFi,j
=
λPi,j
= λP
in
j
in
jin
λP
λF0,j
λF
0,1
λP0,1
λF0,2
λP
0,j
λF0,j+1
λF
0,n-1 λ P
0,n
-1
λF0,n
λF1,1
λP
1,1
λF1,j
λFi-1,j+1
λP1,j
λPi-1 , j
λF1,n-1
λFi-1, 1
λPi-1, 1
λFi-1, j
λFi, 1
λFn-2,1
λPi, 1
λPn-2,1
λFn-2, j+1
λFn-1, 1
λFi, j
Figure 2.3: Markov model for Uncontrolled Channel Assignment
( Adopted and Modified from Capar et al., 2002 )
14
For analysis the bandwidth utilization (BU) is calculated which is define as the ratio of the
expected value of the occupied bandwidth to the total available bandwidth [Capar et al., 2002].
( 2.1 )
Where β1 is the random variable which describes the occupied bandwidth. With BU the
utilization of the spectrum can be measured if the spectrum pooling is used. And also there can
be probability that the renter process which wants to use the pool may be denied as all the
spectrum is being used by the licensed user. This probability is measured as probability of
blocking for renter process which is given as [Capar et al., 2002]
( 2.2 )
Where (i, j) represents the number of active renter and owner processes, respectively, p(i,j) is the
state probability for the corresponding state and m is the number of available channels. The
blocking probability for both the case was found to be almost same. But it was shown that the
forced termination probability is more in case of uncontrolled access scheme than in controlled
access scheme.
2.5.2 Prioritized Primary Access for Spectrum Sharing
An overlay spectrum sharing scheme considering the access priority of Primary or licensed user
is shown in [Tang et al., 2006]. That means the licensed user have the priority and also it uses
uncontrolled access scheme to access the spectrum. This means that when primary users wished
to use the spectrum, which is used by the secondary users for their transmission, then the
secondary users have to cease their transmission in short span of time which results to secondary
drop call. Also there can be secondary block calls where the secondary users needs the spectrum
but cannot access as it is being used by others. This is modification of the work done by [Capar
et al., 2002] which provides a detail study about the call blocking probability and call dropping
probability of cognitive or secondary users in free access scheme for various cases and also
propose a simple reservation scheme to reduce the call dropping probability.
A three dimensional Markov chain is used to dimension the spectrum sharing of primary and
secondary users with Poisson arrival process with mean rates λP and λS respectively with
negative exponential service time distribution of 1/µP and 1/µS respectively. The state in the
model is denoted as (i, j, k) as shown in Figure 2.4.
15
Figure 2.4: Three Dimensional Markov Chain Model
( Adopted and Modified from Tang et al., 2006 )
In the given Figure 2.4, i and j represents the number of primary and secondary users
respectively and k indicates the events, where
„0‟ denotes no collision of primary users and secondary users,
„1‟ denotes occurrence of drop call,
„2‟ denotes a block call state.
The steady state probability is denoted as P (i, j, k) and is calculated using the cut equation and
using
( 2.3 )
The secondary drop call and block call probability are calculated as [Tang et al., 2006]
16
( 2.4 )
( 2.5 )
Different analysis were made by varying λP, λS, µP, and µS to calculate call blocking and drop
call probability of the secondary users to find the best capacity performance of the secondary
users. It was shown that by controlling the number of secondary users admitted in the system by
using the notification messages, and reducing the average holding time of the secondary system,
both the blocking and dropping probability can be reduce almost to zero. As less holding time
means that most likely the secondary users can complete their transmission in time before
licensed users reappears, call blocking probability and call drop probability is more at initial
decreases with further decrease in the average holding time of the secondary users.
The capacity of the secondary system which is define as the maximum secondary users arrival
rate which satisfy the GoS requirement is given as [Tang et al., 2006]
( 2.6 )
Where pb is the maximum allowable blocking probability and pd is the maximum allowable call
dropping probability.
The notation is to denote the arrival rate of the secondary users with
condition on the blocking probability satisfied.
The notation is to denote the arrival rate of the secondary users with
condition on the dropping probability satisfied.
In order to further reduce the call dropping probability, a simple channel reservation technique is
presented in [Tang et al., 2006] where the primary users are allowed to reserve some channel in
advance only so that it can be used for future purpose. This indeed reduces the dropping
probability of the secondary users as they are not interrupted by the primary users but the
blocking probability may increase as the consequence. According to [Tang et al., 2006], an
optimal reservation channel can be found to increase the overall capacity performance of the
secondary users system.
For this primary system are allowed to reserve R (out of N) channels for their future access. So
that means this scheme limits the number of secondary users to N-R. Until the number of primary
users in the system is below the value R, the newly arrive primary users continues to use the
reserve channels. But as soon as the number of primary users are more than the value of R then
the primary system controller randomly allocates the channel to the newly arrive primary users.
This can be implemented by using primary base station to send notification messages to block
secondary users from using the reserve channels if there are any. The value of R must be chosen
such that it should increases the overall capacity performance of the secondary users
17
The modified Markov chain model is shown in Figure 2.5.
0,0,0 λS
µS
Pri
mar
y U
sers
(i)
Secondary Users (j)
1,(N-R),21,(N-R),01,1,0
0,(N-R),20,(N-R),00,1,0
N,0,0
R+1,0,0
1,0,0
(R+1),0,1
(R+1),1,0
λS
2µS
λS
(N-R)µS
λSµS
λS
2µS
λPµP
λP
(R+
1)µ
P
µP µP
1
λS
1
……
……
µP
Pλ
i-N
j-iN
Pλ
i-N
j
1
λSµS
N,0,1
N,0,2
λS
1
1
i denotes no. of primary users
j denotes no. of secondary users
K denotes the event
N no. of Bands
R no. of Reserve bands
Drop call State
Block call State
λP
R,(N-1),2R,(N-1),0R,1,0R,0,0 λSµS
λS
2µS
λS
1……
λS
λP
……
……
……
(R+1),
(N-R-1),
2
(R+1),
(N-R-1),
0
λS
1
(R+1),
(N-R-1),
1
Pλ
i-N
j
1
……
……
……
Figure 2.5: Modified Markov Chain Model for Reservation Case
( Adopted and Modified from Tang et al., 2006 )
The capacity of the secondary system under a given primary system traffic is now given as
[Tang et al., 2006]
( 2.7 )
Where the notation is to denote that the secondary arrival rate
such that the blocking probability is satisfied for primary user having R reserve channel. They
have shown that using reservation technique the dropping probability can be reduce only thing
needed is to find the optimal value of R which will give a larger secondary system capacity.
2.5.3 Spectrum Handoff using Optimal Channel Reservation for Cognitive Radio
Another Markov chain model for cognitive radio access in licensed bands using spectrum
handoff is presented in [Zhu et al., 2007] where forced termination probability, the blocking
18
probability and system throughput is calculated in order to achieve higher throughput. In a radio
network where both the primary users (licensed users) and cognitive users (unlicensed users) are
coexisting, the cognitive users need to let go those bands of primary spectrum whenever the
primary users needs to use them. And when this occurs the cognitive users may lose their
communication link and sometimes have to forced terminate the communication link.
Alternatively, the cognitive users can sense idle sub-bands and reconstruct the communication
links to them without terminating the current communication. This is called a spectrum handoff
[Zhu et al., 2007]. This reallocation of band can either be performed by the base station centrally
or by the cognitive radios through suitable distributed protocols.
The spectrum consists of M primary bands and each primary band is divided into N sub-bands.
According to [Zhu et al., 2007], the cognitive user uses channel A1 to ANM while the primary
users uses channels B1 to BM. Both the bands overlap each other. Also the primary users have the
priority to use the spectrum whenever they want even the sub-bands used by the cognitive users.
So that means the primary users are in control of the usage of the spectrum. The process of the
spectrum occupation is modeled as a continuous Markov chain with Poisson arrival processes for
both the cognitive and primary users with arrival rates λa and λb respectively. The service time are
assumed to be exponentially distributed with rates µa and µb respectively. The states are describe
as an integer pair (i,j), where i is the total number of sub-bands used by cognitive users and j is
the total no. of primary bands used by the primary users. The Markov chain model for the
spectrum handoff using r reservation channel is shown in Figure 2.6.
Figure 2.6: Rate diagram of state (i, j) with Spectrum Handoff and Channel Reservation
(Adapted from [Zhu et al., 2007])
So this means that as long as there are idle sub-bands, forced termination will not take place.
Thus for state (i,j), if i+jM ≤ (N-1)M, forced termination will not occur with the arrival of a
primary user; otherwise forced termination will move states (i,j) to state ((M-j-1)N, j+1) with
transition rate of λb.
19
When r channel are reserved to assist the spectrum handoff, it was shown that probability of
forced termination can be further reduced. This case is model as in Fig 2.6 (b). Here the
transition rate diagram of state (i,j) with r reserved sub-bands is shown. Moreover the cognitive
users are blocked only when the current bandwidth occupancy (i+Nj) plus r equals the total
bandwidth NM.
The blocking probability and the forced termination probability with r reserved channels are
[Zhu et al., 2007]
( 2.8 )
( 2.9 )
Using Equation ( 2.8 ) and Equation ( 2.9) PB ( r ) and PF ( r ) can be trade off by adjusting the
value of r.
The optimal r was selected where the throughput ρ(r) was considered to be maximum for the
cognitive users. Throughput is defined as the average number of service completion per second,
and is given as [Zhu et al., 2007]
( 2.10 )
Using these equations the optimal r can be calculated.
2.5.4 Spectrum Sharing Among the Cognitive Users
As cognitive user‟s uses the spectrum of licensed used in an opportunistic manner, there can also
be fairness issues among the cognitive radio due to dissimilarities between them. [Xing et al.,
2006] addresses the problem of unfairness among the cognitive radio in an open spectrum
wireless network in terms of airtime share. Open spectrum means the band of frequency for
which no license is needed for its usages making it more efficient and mobile. Spectrum sharing
is also supported by open spectrum as different radio system can coexist. Also the radio systems
can dynamically use and release spectrum as per the requirement inside the given radio
environment. There is a special kind of radio which can achieve this usage called Spectrum
Agile Radios (SARA). SARA can help to minimize unused spectral bands in the open spectrum
networks.
In order to achieve fairness among these radios, an etiquette rule can be defined which defines
the rules for the behavior of radio system in order to achieve fairness in access to shared radio
resources and for efficient usage of the spectrum. So basically spectrum etiquette provides a
framework for behavior for radio system which may restrict the degree of freedom for
management of radio resources by each radio system [Mangold and Challapali, 2003].
20
In [Xing et al., 2006], fairness issue is considered taking into account two types of radio systems
A and B where type A operate on three frequency channels (center frequencies), and the radio
systems of type B operate on nine frequency channels (center frequencies) as shown in Figure
2.7. Type A radio system can be considered as broad band radio systems and B as narrow band
radio system. By considering a simple Markov chain model they have shown that the broad band
radio system i.e. type A radio system is always dominated by narrow band type B radio system.
The arrival rate is model as Poisson random process with rate λi for radio system i, and the inter
arrival time is negative-exponentially distributed with mean time 1/λi ms. The radio system
access duration is also negative-exponentially distributed with mean time 1/µi ms, so the
departure of the radio system is another Poisson random process with rate µi.
Figure 2.7: Frequency Channel use by two different types of radio systems A & B
(Adopted from [Xing et al., 2006])
For analysis of the system following assumptions were made [Xing et al., 2006]
The radio systems only scan their own frequencies, for example, a radio system A, with
center frequency f2 looks only in its frequency and not any other frequency.
Every radio system requires its respective frequency to be idle before allocating the radio
resources, otherwise it will be dropped, i.e., no queuing allowed.
If there is collision between the radio systems due to the detection of same idle channel by
more than one radio system, one of the radio systems is randomly selected to allocate the
radio resource, the other radio systems are dropped.
Two of the most representative etiquette rules defined in [Mangold and Challapali, 2003] are as
follows.
Rule 4: A radio system of type A or type B should apply LBT when operating.
Rule 6: In order to protect other radio systems most efficiently, a radio system B that follows
Rule 4 should synchronize its LBT process in time across neighboring frequency channels
that overlap with the same A channels.
Equal traffic load is considered for each type of radio system with same occupation time. So for
simplicity one of the type A radio systems and the three type B radio systems are assumed whose
21
required spectrum is within the type A radio system‟s spectrum range. This spectrum range is
referred to as reference range. Also since collisions rarely happen especially with low traffic
load, in this Markov model, collision state is omitted. From this Markov model the probability of
being in state i, Pi are calculated and average Airtime per radio is also calculated. Airtime refers
to the ratio of allocation time per radio system type to the reference time ( say 1 hrs) [Mangold
and Challapali, 2003].
( 2.11 )
It was seen that the airtime for type B radio system were high then the airtime for type A system
which means that if the traffic load of the radio system B increase then radio system type A goes
to 0 which is not fair as for the part of type A being the broader band radio. According to [Xing
et al., 2006] it was seen that for the radio system following the given LBT rule, the narrow
bandwidth radio system B always dominates the airtime share over the broad band radio system
A.
Also they have shown that if queuing is introduces then the airtime share of the broad band
system increase while the airtime share of narrow band radio system is not affected so much.
This model was again characterized by Markov chain model as shown in Figure 2.8 where each
state is represented as (k1, k2) and ki equals to number of radio system i on the spectrum block.
The kiw means the number of waiting radio system of type i. With queuing the airtime share for
type A is fairly increased.
Figure 2.8: Markov Chain to Model the Unlicensed Spectrum Access Process with waiting
( Adopted and Corrected from Xing et al., 2006 )
22
Further they show that if the radio system were allowed to follow the Rule 6 i.e. a narrow radio
system (here B) that follows Rule 4 should synchronize its LBT process in time across
neighboring frequency channels that overlap with the same reference channels, then it is seen
that the broad band radio system are protected and thus the airtime share also increase. But the
payoff is that the airtime share of the narrow band (Here B) reduces significantly.
Another way to increase the efficiency in spectrum access is to pack all the radio systems tightly
together in spectral domain so that there are no spectral holes or white spaces in between them.
According to [Xing et al., 2006], “packing” behavior is describe as process where if one radio
spectrum releases the spectrum, the other radio spectrum will switch the operating frequency
band so that the vacant band is occupied as per the policies defined in that particular SARA.
Then a new accessing radio system scans to find spectrum opportunities from the beginning of
the spectral band and occupies the first idle spectrum opportunity it finds. In this way the entire
spectrum holes are packed by the neighboring radios and all the spectrum fragments can be
saved for the future accessing radio systems. For example, when radio system B2 releases the
spectrum, another radio system B4 or B3 then switch to the vacant frequency f5 as shown in
Figure 2.9. Doing so the spectrum are packed and there is sequential idle spectrum for other
radio system to access for. When there is a new accessing radio say A2 (which needs 3
frequencies), then it can occupy frequency f7, f8 and f9. Without SARA, it would have to queue
or be dropped. Although, all this switching may introduce signaling overheads and delays in the
system. So to overcome, these spectrum bands can be divided into blocks, and then the packing
can be then implemented in each block reducing the switching and scanning procedure.
f1 f2 f4 f7f5 f6f3 f8 f9
B1 A1 B4B3B2
f1 f2 f4 f7f5 f6f3 f8 f9
B1 A1 B3B4
Release
Switch
f1 f2 f4 f7f5 f6f3 f8 f9
B1 A1 A2B3B4
Occupy
Frequency Frequency
Frequency Figure 2.9: Packing Behavior Example
( Adopted from Xing et al., 2006 )
A Homo Egualis (HE) model is proposed in order to model the access scheme of both the radio
system which takes into account the fact that individual shows inequity aversion i.e. individual
are ready to give up something in order to have more equitable outcomes. They have shown that
in HE model the subject suffers more inequity when they are doing worse than other. This HE
23
was modeled using the utility function of player i, ui in an n0 player game as shown in eq 4.3.1
[Gintis, 2000 cited from Xing et al., 2006]
( 2.12 )
α = envy factor, β = guilt factor
Where player‟s pay off and the second term are measures the utility loss while
doing worse than other and the third term measures the loss while doing better than others [Fehr
and Schmidt, 1999]. And 0≤ β <1 and α ≥ β which exhibits that in HE society model the subject
suffer more inequity when they are doing worse than other. In order to use this concept, it was
assume that each radio system competes for the spectrum with probability pi which they learn by
themselves.
Also for the player pay off is defined where onlinetime is average cumulative
“ON” spectrum time per radio system of type i. That is this is the time that particular radio
system is online or uses the spectrum where
Li = weight fairness = θi λi
θi = priority parameter
λi = traffic load for type i radio system
The probability pi is updated as
( 2.13 )
Where n is the number of different radio system types, 0 < βi < αi reflects the fact that radio
system exhibits strong urge to reduce inequality when doing worse than other. This forces each
radio system to access spectrum in fair manner. The only thing needed now is the radio system
own history of the onlinetime and also the onlinetime of other radio systems in order to keep
record of the busy time of the required spectrum. These records are used in order to access the
spectrum opportunistically. Record of busy time of required spectrum is kept so that when there
are more than two radio system trying to access the same spectrum the priority can be given to
those owing to the use of that spectrum previously. Each radio system can access only its own
historical records and their respective λi. Then only the priority parameter is needed to be
broadcast by the radio system to access the spectrum.
It was shown that the fairness achieved by assuming this model for both the radio system was
almost equal and was much high than any other scheme proposed [Xing et al., 2006].
24
2.6 Discussion
This section covers a brief discussion, comparison and analysis of the traffic models mention in
chapter 4. There are various overlay approaches for spectrum sharing and some of them are
discussed in this report. For this radios like CR and SARA are used which uses flexible spectrum
access techniques for identifying under-utilized spectrum and to avoid harmful interference to
other radios using the same spectrum. Such an opportunistic spectrum access to under-utilized
spectrum, whether or not the frequency is assigned to licensed primary services, is referred as
overlay spectrum sharing.
It was seen that the secondary users (or unlicensed or cognitive users) must be able to find the
spectrum opportunities in order to make use of the licensed spectrum. The secondary users can
use the spectrum as long as it is not required by the licensed users. But they have to cease the
usage of the spectrum when the primary user tries to access the very spectrum they are using it.
This reappearance of the primary users makes the secondary users to be terminated (or drop)
forcefully after some negligible delay. The force termination (or drop call probability) has
always been center of focus in any communication system because it is the probability with
which the ongoing communication links are forced to terminate.
[Caper et al., 2002] shows the channel access scheme for the primary users based on certain
level of priority. According to them there are two possibilities: controlled and uncontrolled
access scheme and they have shown that the for reliable communication and lower force
termination probability, controlled access scheme is best where the secondary users can persist as
long as there are other free channels for the primary users and later is that primary users have no
knowledge of the presence of the secondary users and treats the channel used by secondary users
like a free channel.
Also [Tang et al., 2006] is modification of the work done by [Caper et al., 2002] which
provides a detail study about the blocking probability and dropping probability of secondary
users in free access scheme for various cases and also propose a simple reservation scheme to
reduce the call dropping probability. It was shown that by controlling the number of secondary
users admitted in the system by using the notification messages, and reducing the average
holding time of the secondary system, it was seen that more secondary users were able to
complete their transmission and the both the blocking and dropping probability was reduce
almost to zero
In [Zhu et al., 2007] a simple channel reservation technique together with spectrum handoff is
shown to reduce the force termination probability with slight increase in blocking probability. It
was shown that even though there is slight increase in blocking probability, the overall
throughput of the traffic increases.
Also according to [Nekovee, 2006], today most of the research on cognitive radio is being
carried out in operation of these radios in open spectrum. Open spectrum is defined as the band
of frequency for which there is no need of license to use it and no restriction in accessing it. But
25
the utilization is restricted as unlimited number of users are sharing the same Open (or
unlicensed) spectrum. Spectrum usage is allowed to all devices that satisfy certain technical rules
or standards (like limitation of transmission power or advanced coexistence capabilities) in order
to mitigate potential interference.
In [Xing et al., 2006], overlay spectrum access scheme of a narrow band radio (eg Bluetooth)
and broad band radio (eg WiFi) has been studied. They have shown that the operation of these
radios under simple Listen-before-talk rule results in unfair allocation of airtime where narrow
band radio gets the largest airtime share. Also a modified access protocol is also presented based
on Homo Egualis (HE) model which uses utility function in decision making step together with
inequality aversion to achieve fairness in air time share. They have also shown using this
protocol with spectrum agile radio provides superior airtime performance. Due to this today
spectrum agile radio are considered to be one of the best options for Dynamic Spectrum Access
in open wireless networks.
2.7 Comparison
The various access schemes which are mentioned above are classified and compared in the table.
Table 2.1 can give in brief the approach and analysis of different access scheme which was cover
in this report in Chapter 4.
Table 2.1: Comparison of different spectrum Overlay Approaches
Method and Approach Measurement
Parameters
Results Conclusion and
Comments
1. Controlled and Uncontrolled
spectrum access using spectrum
pooling: [Caper et al., 2002]
Case I: Controlled where SU can
persist as long as there are other
idle channel for PU
Case II: Uncontrolled where the
PU has no knowledge of presence
of the SU in the system and treats
all channels as free.
Blocking
probability
(Pblock),
Force
termination
probability
(Pforce) and BU
Case I has low blocking
Probability and Force
termination probability
(PForce ≤ 1% or 0.01)
Case II has also low
blocking probability but
high force termination
rate since PU does not
know SU exists and can
take the channel used by
SU (PForce ≥1% or 0.01)
In this approach it was
found that blocking
probability and BU are
almost same for both
case but the force
termination is much
higher in case II.
Reliability of the
uncontrolled uses can
be achieved if
intelligible
rescheduling is used
for secondary process.
26
Method and Approach Measurement
Parameters
Results Conclusion and
Comments
2. Spectrum Sharing with
Prioritized Primary Access:
[Tang et al., 2006]
Overlay approach for spectrum
sharing is used to find the overall
capacity performance of SU in
free access scheme and simple
reservation scheme to reduce call
blocking and dropping
Probability.
Blocking
probability
(Pblock), and
Dropping
probability
(Pdrop) for
determining
GoS
Various cases for the free
access scheme is consider
and was found that that
the call blocking and
dropping probability is
lowest in the case when
the holding time of the
SU were reduce.
Also by using reservation
scheme the overall drop
probability is reduce but
blocking probability is
increased slightly
By the use of a simple
reservation technique
the drop call
probability can be
reduce but due to
reservation there is
slight increase in the
blocking probability.
Optimal value of R
can be found such that
there is balance in
block call and drop
call probability
3. Optimal channel reservation
[Zhu et al., 2007]
In this approach a channel
reservation scheme for CR
spectrum handoff is proposed to
achieve higher throughput and
also to trade off the forced
termination and blocking
probability
Blocking
probability
(PB( r )),
Force
Termination
probability
(PF( r ))and
Traffic
throughput
It was shown that with
the use of spectrum hand
off the force termination
probability can be reduce.
Similarly using optimal
reservation the force
termination can be
reduced significantly.
There is significant
reduce in force
termination probability
with the introduction
of reservation with
very small increase in
Blocking probability.
This is one advantage
of this approach. The
overall traffic
throughput is high for
the spectrum handoff
and reservation case.
4. Dynamic Spectrum access in
Open Spectrum Networks
[Xing et al., 2006]
In this various overlay models
like simple Markov model
without queuing and queuing,
Homo Egualis (HE) model and
spectrum access using SARA
(Packing Behavior) for providing
fairness in a narrow band and
broad band radio systems are
discussed. They have shown that
the fairness can be improved for
the given radio system
Airtime share
which is ratio
of allocation
time per radio
system type to
reference
time.
It was shown that using
etiquette rule 6 with the
simple LBT (rule 4) the
broad band system can be
protected at the price of
reduction in airtime share
of narrow band systems.
Further they show that
the approach based on
HE society can provide
good fairness than using
the etiquette rule 6 alone.
Finally they have shown
Over all it was shown
greater fairness in
airtime can be
achieved if the radio
systems were allowed
to switch their
frequency which
enables them to pack
themselves in the
spectral domain.
27
that using SARA together
with HE model greater
airtime fairness and lower
blocking probability can
be achieved
From the above comparison we can say that for increasing the capacity performance of the
secondary users and also to decrease the force termination probability, the prioritize spectrum
sharing discuss in section 2.5.2 is better since it uses priority based access scheme and also uses
the channel reservation technique to limit the number of secondary users in the system. But as I
have mentioned before, the force termination of the secondary users can be minimized
significantly if intelligent rescheduling of the secondary user could be made possible which
allows them to reschedule their transmission when they are forced to terminate by the primary
users. For this the secondary users must be able to sense and hop between the different
frequencies available in the spectrum group. This hopping can be defined as spectrum hopping.
Also in case of the open spectrum access scenario where the radio systems are free to do
anything, spectrum agile radio together with HE protocol can have significant increase in airtime
share for the broad band radio system without degrading the other narrow band radio systems.
It was seen from this that if the CR or any radio system sharing the frequency spectrum either in
licensed or unlicensed bands, somehow switch their frequency in such a way that they are able to
use other frequency slots while they are communicating then greater fairness and capacity
performance can be achieved.
28
CHAPTER 3
RESEARCH AND DEVELOPMENT
Traffic modeling and analysis of any network help dimension the network more properly. Also it
is interesting to study different behavior of access schemes in a time dependent environment by
analyzing the performance and utilization of the radio resources. This research study has been
my base and motivation for my work entitled “Performance and Fairness study of Overlay
Spectrum Sharing in Cognitive Radio Environment”. The study of the traffic transitions and its
adaption in different overlay schemes helps understand more about its behaviors which can be
used to achieve the desired performance and fairness among the cognitive users.
The main objectives of this research work can be summarized as:
1. To study the fairness issues in cognitive radio environments for different user groups --
licensed and cognitive users.
2. To develop a generic simulation platform for studying various overlay spectrum sharing
techniques for measuring the performance of different cognitive users in terms of
utilization of the radio resource, loss and airtime share.
3. To observe and compare the performance of different cognitive radio system in a time
dependent environment by analyzing the arrival rates, holding time and process
occupation model using the proposed simulation model.
4. To study the temporal traffic behavior of the cognitive users in a time dependent
environment.
3.1 Methodology
The main concern of this research work is to study performance and fairness issues regarding
different overlay spectrum sharing in a cognitive environment by constructing a simulation
environment for it. In a dynamic spectrum access network, there are multiple unlicensed users
which are allowed to access the unused spectrum bands of the primary users or licensed users
without disturbing the usage of the primary users as shown in Figure 3.1. The primary users are
denoted by P1 and P2 and the unlicensed or cognitive users are denoted as A and B. These
cognitive users can be narrow band users or broad band users depending upon their bandwidth
requirements.
29
Sen
sing
SensingBase Station
Primary User
P1
Cognitive User A
(Broad Band)
Cognitive user B
(Narrow Band)
Primay Users
Cognitive Users
Primary User
P2
Frequency Grid
Used by Primary user
Used by Narrow band cognitive radio
Used by broad band cognitive radio
Figure 3.1: System Diagram of Cognitive Radio Environment
3.2 System Model
For considering the appropriate system model, first we consider the spectrum bands of the users.
Since the unlicensed users utilizes the unused spectrum band of the licensed users so for system
model two types of users are considered in general, the Licensed or Primary user and cognitive
users both operating in same spectrum.
We consider the concept of spectrum pooling where there are n bands (or n server) and both the
primary and cognitive users share the spectrum as shown in Figure 3.2. For this research work, it
is considered that one bandwidth unit (BU) is equivalent to one band or server.
1 2 nn-1n-2
Figure 3.2: Frequency Band of two users
The system model is shown in Figure 3.3. The primary user is denoted by P and since they are
the legacy users of the spectrum, its access should not be disturbed by the operation of any
cognitive users. For this the priority to access the spectrum is given to licensed or primary users.
The cognitive users are denoted by C and have less priority than the licensed users.
30
System with “n”
bands for Licensed
or Primary users
and Cognitive users
“n” bands
Cognitive
Users, C
Pλ
Primary
User, P
Cλ
1
NP
1
NC
Service Type
Identifier
Primary
Controller
Unit
Cognitive
Controller
Unit
Admission
Control
Unit
Primary
Cognitive
Spectrum
Etiquette
Rules
Figure 3.3: The System Model
There can be NP primary users and NC cognitive users. In this research work the simulation
platform can handle two primary users and two cognitive users simultaneously with different
bandwidths. The arrival process is considered as independent Poisson arrival process with rate
for primary users and for cognitive users. The service time is considered as negative –
exponentially distributed with mean time of and for primary and cognitive users respectively.
The service type identifier identifies which type of service is trying to access the system and the
Admission Control Unit determines whether to accept the process or not depending on the
algorithm or rules defined in each of the control units. The Admission Control Unit consist of
two control units for respective processes which is responsible for controlling the sharing of the
spectrum in opportunistic manner. These rules are termed as spectrum etiquette rules which
restricts the degree of freedom of radio systems for management of radio resources by each
system [Mangold and Challapali, 2003]. In a way these rules governs the radio systems to
efficiently share the radio resources among themselves.
It is considered that the system consist of n number of bands or servers which is shared among
the primary and cognitive users using spectrum pooling concept.
This research work is concentrated on making a generic simulation platform which can
accommodate different overlay models in cognitive traffic environment which can be used to
study fairness and performance of the spectrum sharing. In this research work, four type of
overlay spectrum sharing is considered. Depending upon the access scheme of primary and
cognitive users they are classified as follow:
3.2.1 Spectrum Sharing with Uncontrolled Access Scheme
In this spectrum sharing technique, the primary users are allowed to access the spectrum pool in
uncontrolled fashion such that it doesn‟t know that the cognitive users coexist in the system.
31
Cognitive users on the other hand use the spectrum bandwidth opportunistically whenever there
is free frequency band in the system. That means the primary users have no control over the
existence of the cognitive user in the system. But primary user being the licensed user of the
system, it has the priority over the cognitive users and thus they can force terminate the cognitive
users if the cognitive users are using the frequency band needed by the primary users. More to
that due to the uncontrolled access scheme of the primary users, the cognitive user have high
probability of getting forced terminated even if there are other idle spectrum for the primary
users to use. This case is considered as the base case for this research work. An attempt is made
to reduce the force termination probability and to increase the bandwidth utilization of the
cognitive users by considering different spectrum sharing techniques.
3.2.2 Spectrum Sharing with Controlled Access Scheme
In this spectrum sharing technique, the primary users are in control of the existence of the
cognitive users in the system. The primary users force the cognitive users to terminate only when
the system is full and there are no idle spectrums available for them to use. In a way the primary
users acknowledge the presence of the cognitive user in the system. The force termination of the
cognitive user is reduced using this access scheme and hence increases the bandwidth utilization
of the cognitive users.
3.2.3 Spectrum Sharing with Cognitive Spectrum Hopping
The cognitive users are allowed to hop to another available idle frequency band whenever the
primary users need the spectrum band used by the cognitive users. In this way the cognitive
user‟s communication is not interrupted when the force termination occurs. Since the cognitive
users uses cognitive radio for communication purpose, cognitive users can sense the arrival of
the primary users and hop to new available frequency in order to finish the communication.
In a way this spectrum sharing is same as of controlled access scheme but the difference is that in
this the cognitive user alters the sharing strategy in order to complete its communication. By
doing so, the integrity of the primary users is preserved as they are the licensed user of the
system and it is not necessary for them to acknowledge the presence of the cognitive users in the
system. Hence the forced termination probability and the bandwidth utilization is same as of
controlled access scheme.
3.2.4 Spectrum Sharing with Buffering of Cognitive Users
In this scheme the cognitive users are allowed to wait in the buffer whenever they are interrupted
by the primary users or whenever they are not allowed to enter the system. So that mean
whenever there is force termination of the cognitive user then the interrupted traffic of the
cognitive user is stored in buffer and cognitive users scans the spectrum band continuously for
the idle spectrum. The retransmission starts as soon as the spectrum band becomes idle again.
Depending upon this there can be two type of buffering systems. They are:
1. Infinite Buffering System:
32
The buffer is considered to be of infinite length and with First In – First Out (FIFO) principle. If
infinite buffering is allowed then both the forced termination and blocking becomes zero
increasing the bandwidth utilization of the cognitive users. The price to pay is that they spend
infinite time in the buffer hence decreasing the efficiency of usage.
2. Finite Buffering with Impatient Cognitive Users
Impatient users are those which waits exactly time unit in the buffer before it totally gives up
for entering into the system. As cognitive users are the unlicensed users of the system, this trend
is more imminent in their behavior. This case is same as that of the case with cognitive buffering,
but the cognitive user waits for some time unit and after that they again retry to acquire the
idle spectrums. This time unit that the cognitive user waits in the buffer has a directly effect on
the force termination probability and as well as the bandwidth utilization of the cognitive users.
3.3 System Traffic Model
The traffic patterns for both primary and cognitive user are considered to be random in nature to
simulate the real life environment. Since the primary users are the legacy users of the system and
it can be of any type meaning that the source type can be finite and infinite depending upon the
nature of the primary users. Moreover the cognitive users are unlicensed users of the system
which access the unused spectrum of the primary users, there can be infinite number of cognitive
users that can access the system. So depending upon this there can be many type of system traffic
model. In this research work, the system traffic is modeled as Markov chain model with state ( i,
j) where i represents the primary users and j represents the cognitive users.
3.4 Performance Measurements
The performance of the both the primary and cognitive users are measured based on the total
number of the process served, total number of process blocked or forced terminated, their
holding time, service time and bandwidth utilization. The performance parameters considered are
briefly defined here as follows:
Blocking : Blocking for primary user refers to the case where the primary user cannot
access the spectrum band as all the available spectrum band is occupied by other primary
users. Whereas for the cognitive user, blocking is defined as the case where it cannot
access the spectrum band as the spectrum band is occupied by either primary users or
cognitive users.
Forced Termination : Forced termination occurs for cognitive users only and is
define as the case where the primary users tries to use the frequency band already in used
by the cognitive users. The forced termination is the count of number of incomplete
process of cognitive user due to interruption by the primary users. Forced termination
probability is measured with reference to:
o Offered calls
The forced termination is measured out of the total offered which includes the
blocked calls also.
33
o Served calls
The forced termination is measured out of the connected or served calls.
Bandwidth Utilization : Bandwidth utilization means the overall utilization of the
available bandwidth or frequency band by the primary or cognitive users.
All these parameters help to measure the Grade of Service (GoS) of both the primary and
cognitive user transmission. So in order to measure the performance of the system, different
parameters like blocking probability, forced termination probability, carried traffic, bandwidth
utilization are calculated. These raw results are then used to analyze the performance of the
system.
3.5 Performance and Fairness Analysis Methods
In order to analyze the performance and fairness of the overlay spectrum sharing in cognitive
environment both analytical and simulation methods are used. The analytical method is done in
order to verify the simulation method. The parameters considered for both the case are
summarize in Table 3.1.
Table 3.1: Different Parameters for Performance and Fairness Analysis
Parameters
Analysis Method
Analytical Simulation
Number of Servers (n) Finite Finite
Arrival Process (λ) Poisson Any
Service Time Exponential Any
Traffic Model Markov Chain Markov Chain
Bandwidth Unit (BU) 1 BU = 1 server 1 BU = 1 server
Number of Primary Source Finite Finite and Infinite
Primary Users Type 1 or 2 1 or 2
Bandwidth of Primary Users (dP) 1 or 2 1 or 2
Number of Cognitive Source Infinite Infinite
Cognitive Users Type 1 or 2 1 or 2
Bandwidth of cognitive Users (dC) 1 or 2 1 or 2
34
3.5.1 Analytical Method
For the validation purpose, analytical methods are done for small cases to calculate the state
probability and performance parameters like blocking probability, forced termination probability,
carried traffic and bandwidth utilization of each service type. The complexity of the analytical
method grows as we increase the number of servers. In this research work, generalize traffic
model was developed for the mentioned access schemes. For the sake of verification, those
traffic model are solved for n = 1 and n = 2 servers. Detail description is given in Appendix B.
3.5.2 Simulation Method
For simulation of this traffic model, event based simulation model is developed using C and C
++ language. The simulation model is capable of performing analysis for very large number of
servers with finite and infinite number of primary and cognitive user.
35
CHAPTER 4
ANALYTICAL MODEL
The different overlay spectrum sharing are model as Markov Chain model in order to verify the
simulation model. It is assumed that there is a spectrum pool of number of frequency band
which is shared by both the primary and cognitive users. The arrival rates for the primary users
and cognitive users are considered to be Poisson process with arrival rate
respectively. The service time distribution is exponentially distributed with mean rate of
respectively. An attempt is made to calculate the general flow equations for each of the different
Markov chain models. The analytical models of the four cases are solved to some extend in order
to validate the simulation model.
4.1 Markov chain Model for Uncontrolled Access Scheme
For numbers of frequency bands available for the primary and cognitive users, the
uncontrolled access scheme is modeled as Markov chain model as discuss in [Capar et al.,
2002] section Error! Reference source not found.. The state is represented as where
epresents the number of active primary users and represents the number of active cognitive
users in the system. But in this case the blocking for primary is also considered so as to make the
case more realistic. The state diagram is same as shown in section Error! Reference source not
ound. Error! Reference source not found..
The state probabilities are calculated by numerical solution using Matlab by generating the
infinitesimal generation matrix. Once the state probabilities are calculate the blocking for
primary and cognitive users, forced termination probability and bandwidth utilization can be
calculated.
4.2 Markov chain Model for Controlled and Cognitive Spectrum hopping
As mentioned in section Error! Reference source not found., in this access scheme the primary
sers are in control of letting the cognitive user to coexist in the system. Also model wise the both
control and cognitive spectrum hopping access scheme are same. Both the access scheme is
modeled using same Markov chain model with infinite source as discuss in section Error!
eference source not found.. The primary losses are considered in this case also and similarly as
in case of the uncontrolled access scheme we can calculate the state probability using numerical
solutions using Matlab. Once the state probabilities are calculate the blocking for primary and
cognitive users, forced termination probability and bandwidth utilization can be calculated.
4.3 Markov chain model for Spectrum sharing with Cognitive Buffering
In this case the cognitive users which are interrupted are made to wait in some buffer and the
service is resumed as soon as there are idle servers available. Also the cognitive users which are
not allowed to enter the system are made to wait in the buffer until any idle servers are available.
The buffer is infinite in structure and follows FIFO principle. The state diagram is represented by
36
three integer pair where is for the active primary users, is for active cognitive users
and represents the number of cognitive waiting in the system. Figure 4.1 shows the state
diagram for controlled Access scheme with infinite buffer.
0,0,0 0,1,0
λC
µC
λC
2µC
0,n-1,00,j,0 0,n,0
λC
jµC
……... ……...
(j+1)µC
λC λCλC
(n-1)µC nµC
1,0,0 1,1,0
2µC
1,n-1,01,j,0jµC
…….. ……...
(j+1)µC (n-1)µC
λP
λP
λpλP
λpµP
2µP
µPµP
λpλp 2µP2µP
i,0,0 i,1,0
µC 2µC
i,j,0
jµC
……...
(i+1)µPλp
λp
iµP
iµP iµP
(i+1)µP
……
……
……
λP
n-1,0,0 n-1,1,0
λC
µC
(n-1)µP (n-1)µP
n,0,0
nµP
……
……
Cognitive Process, j
Pri
ma
ry P
roce
ss, i
µP
λP
λC
µC
λP
λP
λP
λP
λC
……
……
λC
λCλC
λC λCλC
λC
λP
λC
λC
λC
λP
0,n,1
1,n-1,1
µP
1,n-1,2
µP
nµC
λC
2µP
i,j,1
nµCjµC
λC
……
λP 2µP
……
λP 2µP
λp iµP
nµC nµC
λC
1,n-1,2 1,n-1,3
λC
nµC
λpiµP λp
iµP
λp λp
nµC
……...
λp (i+1)µPλp (i+1)µP
λp (i+1)µP λp (i+1)µP
……...
……...
……...
……
……
……
nµC
λC
nµC
λC
nµC
λC
nµC
n-1,1,1
nµC
λpiµP
λC λC
i,j,1
nµCnµC
λC
λp iµP
λC
1,n-1,2 1,n-1,3
λC
nµC
λpiµP λp
iµP
nµC
……...
λp nµP λp nµPλp
nµP λpnµP
……...
λC
nµC
n,0,1
nµC
λC λC
n,0,2
nµCnµC
λCλC
n,0,n-1 n,0,n
λC
nµCnµC
……...
λC
nµC
λC
……... n,0,n+1
nµC
λP λP λP λP λP
……...
……...
λC
nµC
i is for Active Primary
Users
j is for Active Cognitive
Users
k is for the waiting of
cognitive users
Arrival rate of Primary λP
µP Termination rate of Primary
λC Arrival rate of Cognitive
µC Termination rate of Cognitive
i,j,k Represents waiting state
λp nµP
Figure 4.1: State Diagram for Controlled Access Scheme with Buffering
37
CHAPTER 5
SIMULATION MODEL
5.1 Overview
The simulation platform models the spectrum sharing schemes for various types of traffic
generated by primary and cognitive users. The flow diagram of the simulation is shown in Figure
5.1.
Traffic
Generation
Performance
Parameters
Calculation
Spectrum
Etiquette
Rules
Until
t ≤ T
Admission
Control
Unit
Primary
Control
Unit
Cognitive
Control
Unit
Server
Allocation
Unit
Input
Parameters
Figure 5.1: Simulation Model
The simulation is done on event basis meaning that at particular given time there can be only one
event in the system. The event can be arrival or Termination of the primary user and cognitive
users or the forced termination of the cognitive user. Depending upon the event, different
algorithms or rules are implemented in order to access the available spectrum or bands.
5.2 Simulation Flow chart
The overall flowchart of the simulation program is shown in Figure 5.2. The detail blocks are not
shown in this flow chart which can be seen in the Appendix A.
38
Figure 5.2: Simulation Flow chart
39
5.3 Input Parameters
In order for the simulation to start certain input parameters should be set for generating the traffic
and also for considering the spectrum etiquette rule to be applied. The list of input is summarized
in the Table 5.1. Depending upon the system, these parameters can be set where „0‟ means false
and „1‟ means true for 0/1 option.
Table 5.1: Input Parameters
Parameters value
Any
Number of Repetition Any
Simulation time Any
Observation start time Any
Number of Servers (n) Finite number
Number of Source (N) Finite/Infinite
Primary Arrival Process Any
Primary Service Time Any
Cognitive Arrival Process Any
Cognitive Service Time Any
Bandwidth of Primary Users (dP) 1 or 2
Bandwidth of cognitive Users (dC) 1 0r 2
Queuing Enable/Disable
5.4 Traffic Generation: Arrival and Termination Processes
Traffic generation block generates processes based on arrival process and service time which
will be used as input to the system. This block is responsible for generating initial process and as
well as random arrivals or terminations of both the radio systems or forced terminations of the
cognitive users.
5.4.1 Arrival Process
In this report, the simulation model can handle Poisson arrival process with rate for primary
users and for cognitive users. The inter arrival time, holding time and service time are
generated randomly to make the simulation more realistic. The inter arrival time is calculated as
( 5.1 )
5.4.2 Service Time
For the verification purpose, the spectrum access duration is assumed to be negative –
exponentially distributed with mean time of and for primary and cognitive users respectively.
That means the departure rate of primary and cognitive users are and respectively.
Although in simulation, uniform and constant service time distribution can also be considered.
40
5.5 Spectrum Etiquette Rules
In order to achieve fair share of the spectrum, rules should be define for their access scheme.
These rules must be defined in order to coordinate the sharing of the system resource for
achieving fairness and efficiency in spectrum usage. These rules can be defines as spectrum
etiquette rules which basically provides a framework for behavior for radio system which may
restrict the degree of freedom of radio systems for management of radio resources by each radio
system [Mangold and Challapali, 2003]. The rules considered in this work are:
1. The access scheme of Primary users can be controlled so as to minimize the forced
termination of the cognitive users.
2. All the cognitive users must use listen before talk (LBT) rule. This rule helps to distribute
the control of radio access among cognitive users.
3. Cognitive users can perform spectrum handoff. That means cognitive users are allowed to
move to another available idle bands when the primary user tries to use the spectrum
band used by the cognitive users.
4. Cognitive users are allowed to wait until the desired spectrum is free for their access.
5.6 Admission Control Unit
For analyzing the different access schemes depending upon the spectrum etiquette rule defined,
the admission control unit manages the arrival and termination of the process for both the uses
type. In order to do so the admission control unit generates traffic models depending upon the
spectrum etiquette rules defined. For simulation purpose the traffic model is modeled as Markov
chain model for analysis of different overlay spectrum schemes. There are independent control
units for the primary and cognitive users which controls the behavior of the radio system.
Depending upon the decision made by these control units the available server is assign to
respective processes.
5.7 Performance Parameter Calculation
In order to measure the performance of the system, different performance parameters like
blocking probability, forced termination probability, carried traffic, bandwidth utilization are
calculated. The simulation measurements are performed as shown in Figure 5.3.
t1 = 0
Simulation
Start
∆t ∆t∆t∆t∆t
1 2 3 Nt2 = ∆t
Observation
Start
Total Simulation Time, T
Total Observation Time, Tobs
tstop = t1 + N∙∆t
Simulation
Stops
41
Figure 5.3: Statistical Data measurement in Simulation Model
The total simulation time is defined as T which is divided into N numbers of small ∆t time units.
The simulation starts at t1 = 0 whereas the measurements are taken after time t2 = ∆t. For each ∆t
time period, statistical data such as number of offered process, block process, served process, and
forced terminate process are measured and then the performance parameters like blocking
probability, forced termination probability, carried traffic, bandwidth utilization are calculated.
After N repetition, the average of the measured data is calculated.
5.7.1 Holding Time Measurement
The holding time of each radio system is measured for each ∆t as well as for overall simulation
period T. The holding time is calculated only for those processes which are able to complete their
process within time ∆t as shown in Figure 5.4.
t = t1 t = t1 + ∆t
∆t
tmp1 tmc1 tmc2 tmp2
Primary Arrival
Cognitive Arrival
tmc3 tmp3
Figure 5.4: Measurement of Holding Time
The holding time for the primary users is defined by as
( 5.2 )
Similarly the holding time for the cognitive users is defined by as
( 5.3 )
5.7.2 Service Time Measurement
The service time for each ∆t is measured as shown in Figure 5.5. The service time for the
primary and cognitive users are denoted as and respectively.
42
∆t
t = t1 t = t1 + ∆t
tsp1 tsp2tsc1 tsp2 tsp4
t1 = 0
Simulation
Start
tsc2
Primary Arrival
Cognitive Arrival
Figure 5.5: Measurement of Carried Traffic
5.7.3 Performance Parameter Calculation
The performance calculation for the given system is calculated as shown in the Table 5.2. Here
represents the type of radio systems.
Table 5.2: Performance Parameter Calculation
Parameters Calculation
Blocking Probability
Forced Termination Probability
Carried Traffic
Bandwidth Utilization
servers
5.8 Validation of Simulation Model
The simulation model for each access scheme is validated by comparing the result with the
purposed analytical solutions. However for the access scheme with cognitive buffering is
validate by validating the program flow consistency with an Erlang delay system (M/M/n). In all
the cases the result obtain was almost same as that of the analytical solutions. The detail
calculation is given in Appendix B.
43
5.8.1 Simulation Parameters
For running the simulation efficiently, certain simulation parameters are set in the beginning of
the simulation like the observation period, total simulation time, small observation time and
number of repetition. For validating the simulation model following parameters are considered.
These are summarized as shown in Table 5.3
Table 5.3: Initial Simulation Parameters
Parameters value
1000 min
Number of Repetition 10
Simulation time 10000 min
Observation start time 1000 min
The simulation parameters as shown in Table 5.4 are used to validate the Erlang loss system for
varying offered traffic.
Table 5.4: Simulation Parameters to Validate Erlang Loss System
Parameters value
Number of Servers (n) Finite
Arrival Process (λ) Poisson
Service Time Exponential
Bandwidth Unit (BU) 1 BU = 1 server
Primary Users Type 1
Bandwidth of Primary Users (dP) 1 or 2 BU
Cognitive Users Type 1
Bandwidth of cognitive Users (dC) 1 or 2 BU
Similarly for validating the different access scheme, the following parameters as shown in Table
5.5 are used.
Table 5.5: Simulation Parameters to Validate Different Access Schemes
Parameters value
44
Number of Servers (n) Finite
Number of Source (N) Infinite
Primary Arrival Process Poisson
Primary Service Time Exponential
Cognitive Arrival Process Poisson
Cognitive Service Time Exponential
Bandwidth Unit (BU) 1 BU = 1 server
Primary Users Type 1
Bandwidth of Primary Users (dP) 1 BU
Cognitive Users Type 1
Bandwidth of cognitive Users (dC) 1 BU
Queuing Infinite
45
CHAPTER 6
PRELIMINARY RESULTS
6.1 Study of Different Access Schemes
In this research work, the simulation model is capable of handling the spectrum etiquette rules
shown in Table 6.1.
The buffering or waiting of the cognitive users in the buffer is optional that is it can be enabled
or disable depending upon the input parameters. So with buffering of the cognitive users all
together the simulation model is capable of handling four types of traffic model. These access
schemes are then analyzed in order to study the performance and fairness issues of the cognitive
users in a cognitive environment. Each system is simulated for different number of servers
(Spectrum Bands) with constant primary offered traffic with varying cognitive offered traffic in
order to compare the performance of these entire schemes on a flat line.
Table 6.1: Different Access Scheme Considered for Simulation
Case Type of Access Scheme Buffering
1 Uncontrolled Access Scheme for Primary users Optional
2 Controlled Access Scheme for Primary users Optional
3 With Spectrum Hopping for Cognitive users Optional
The performance of these systems in terms of blocking , forced termination probability
and bandwidth utilization are compared to observe the influence of different
access schemes
6.2 Performance Analysis of Different cases
The performance analysis of the different cases was performed taking into account small cases
with 1 server up to big system of 100 servers. Depending upon the number of servers (or
frequency band) available, the analysis is divided into three categories. They are
1. Small Systems
With servers or frequency slots (n) ranging from
2. Medium Systems
With servers or frequency slots (n) ranging from
3. Big systems
46
With servers or frequency slots (n) ranging from
For each system, the behaviors of the cognitive radio are studied by varying its offered traffic
over a constant primary offered traffic. For this the holding time of both the users are made
constant and only the cognitive arrival rates are varied for a particular case. Simulation was
performed for different case of primary offered traffic from low to high traffic for the systems
mentioned above. These runs were made in order to see the behavior of the cognitive radio
system in different primary offered traffic. The simulation results for 1 server with primary offer
traffic of 0.2 Erlang are shown in Figure 6.1.
(a)
(b)
47
(c) (d)
Figure 6.1: Performance Analysis of 1 server with 0.2 Erl Primary Offered Traffic
It is seen from the Figure 6.1(a) that for low primary offered traffic of 0.2 Erl, the blocking for
primary radio system is not so high whereas the blocking for the cognitive radio systems
increases with the increase in its offered traffic. The force termination probability out of offered
decreases as the offered traffic increase as shown in Figure 6.1(b). The reason for this is simply
as the cognitive offered traffic increases, more of them are blocked and consequently the ratio of
the incomplete ones is less than the total offered ones. Whereas the force termination probability
out of served ones is constant as the number of cognitive users which will be served will always
be constant as shown in Figure 6.1(c). The overall and individual bandwidth utilization is shown
in Figure 6.1(d) and we can see that bandwidth utilization of the cognitive user‟s increases with
the increase in the cognitive offered traffic.
(a)
(b)
48
(c) (d)
Figure 6.2: Performance Analysis of 1 server with 0.4 Erl Primary Offered Traffic
Figure 6.2 shows the analysis for 1 server with primary offered traffic of 0.4 Erl. It can be seen
that with increase in the primary offered traffic there is increase in the force termination
probability of the cognitive users which results in the low bandwidth utilization also.
Similarly the simulation is perform for 2 server system with primary offered traffic of 0.4 Erl and
the results are shown in Figure 6.3. We can see that there is significant growth in the bandwidth
utilization of the cognitive users as its offer traffic increases with not much increase in the force
termination probability. The control and the one with hopping have less force termination
probability in that case.
(a)
(b)
49
(c)
(d)
Figure 6.3: Performance Analysis of 2 servers with 0.4 Erl Primary Offered Traffic
With increase in the primary offered traffic to 0.7 Erl, there is increase in the force termination
probability of the cognitive user as the primary user tries to get more and hence there is decrease
in the bandwidth utilization of the cognitive users as shown in Figure 6.4 (d).
(a)
(b)
50
(c)
(d)
Figure 6.4: Performance Analysis of 2 servers with 0.7 Erl Primary Offered Traffic
For medium systems, if the primary offered traffic is low then the force termination of the
cognitive user is also low and hence this increases the bandwidth utilization of the cognitive
users. As seen in Figure 6.5 (a), the blocking probability of the cognitive users increases as the
offer traffic increases. The force termination probability for the uncontrolled case shows unusual
pattern such that as the offered traffic increase the force termination for that case decreases as
shown in Figure 6.5 (b) and (c) . This is due to the fact that in uncontrolled access scheme, the
primary users hits the spectrum randomly and if the number of server or frequency slots
increases then for the primary users with constant arrival rate and holding time, the probability of
hitting the cognitive decreases. That is there is high probability of hitting the empty spectrum.
But in case of the controlled and hopping case the force termination probability increase as the
offered traffic increases due to the fact that in those case the primary users first tries to hit the
empty spectrum and if there are no empty spectrum then only it tries to force terminate the
cognitive users. There is not so high drop rate in this case and the bandwidth utilization is quite
high for the cognitive users.
51
(a)
(b)
(c)
(d)
Figure 6.5: Performance Analysis of 5 servers with 1 Erl Primary Offered Traffic
Similarly for the case with primary offered traffic of 2.5 Erl, the force termination increases and
the bandwidth utilization also decreases as more primary radio systems tries to occupy the
available radio resources. But we can see that even though there is increase in the primary
offered traffic, there is not so much decrease in the bandwidth utilization of the cognitive users.
This shows that in medium or bigger systems the coexistence of the cognitive users is more
likely to occur then in small systems. Although there is increase in the cognitive blocking but it
can be minimize by letting the cognitive to buffer in the system until it is served.
52
(a)
(b)
(c)
(d)
Figure 6.6: Performance Analysis of 5 servers with 2.5 Erl Primary Offered Traffic
Similarly, for a system of 20 servers or frequency slots, the performance parameters are shown in
Figure 6.7. The blocking for both the primary and cognitive is not so high which implies much
more cognitive traffic can be served to the system with not much increase in the force
termination probability.
53
(a)
(b)
(c)
(d)
Figure 6.7: Performance Analysis of 20 servers with 10 Erl Primary Offered Traffic
Figure 6.8 shows the simulation results for system with 20 servers with 14 Erl of primary offered
traffic. There is not much blocking for both the users and the force termination for the control
case is almost low as 20 %. This again shows that for bigger systems the primary users doesn‟t
have much effect to the cognitive users in terms of force termination or bandwidth utilization.
Although the uncontrolled access scheme has quite high force termination probability but this
54
can be overcome by simple change in strategy as controlled access scheme or if the cognitive
users are allowed to hop whenever they are forced to terminate.
(a)
(b)
(c)
(d)
Figure 6.8: Performance Analysis of 20 servers with 14 Erl Primary Offered Traffic
For really big systems, when the offer primary traffic is moderate, the blocking for both the users
are almost zero or less than 1%. Since there is such a low blocking, the force termination of
offered and of served are almost the same. Also it is quite low for the control access scheme. The
55
bandwidth utilization of cognitive users increase with increase in the offer traffic and it really
can hold high cognitive traffic as shown in Figure 6.9.
(a)
(b)
(c)
(d)
Figure 6.9: Performance Analysis of 50 servers with 20 Erl Primary Offered Traffic
The increase in the primary traffic has direct influence in the bandwidth utilization of the
cognitive users. But since there is not much blocking together with not so high force termination
56
in case of controlled or hopping case, the cognitive traffic can really be increase and the
coexistence is beneficial to both the users. The overall bandwidth utilization can be up to 80 to
90 % depending upon the traffic offered for both the users. Figure 6.10 to Figure 6.12 shows this
kind of trend for the cognitive users.
(a)
(b)
(c)
(d)
Figure 6.10: Performance Analysis of 50 servers with 30 Erl Primary Offered Traffic
57
(a)
(b)
(c)
(d)
Figure 6.11: Performance Analysis of 100 servers with 40 Erl Primary Offered Traffic
Thus we can see that as the number of the available spectrum slots or servers increases, there is
more chance for the cognitive system to coexist with the primary radio systems. The
performance of the cognitive radio system gets better when the system is big. More servers mean
more opportunity for the cognitive users to use the available spectrum. Whereas if the system is
small, then there is less opportunity for the cognitive users to coexist with the primary users.
58
(a)
(b)
(c)
(d)
Figure 6.12: Performance Analysis of 100 servers with 75 Erl Primary Offered Traffic
These initial runs shows that coexistence of the cognitive users is catastrophic when the system
is really small and in the other hand for the medium and big system there is really high
probability for coexistence of both the users.
59
6.2.1 Effect of Buffering of Cognitive users in different Access Schemes
Buffering of the cognitive users means that they are allowed to wait in the buffer whenever they
are interrupted by the primary users and also when they cannot be served instantly as all the
available spectrum band are occupied by either primary or cognitive users. The priority is given
to those which are interrupted in the buffer. Basically two types of buffering are considered.
They are:
1. Infinite Buffering System:
If the buffer or queue is of infinite size i.e. it can hold as many as it can then all of the cognitive
users can be served. But the mean waiting time in the buffer increase as the offered traffic
increases. For all the cases with buffering the performance parameters are almost same as the
buffer size is infinite. Figure 6.13 shows the simulation results for 1 server with cognitive
buffering. Since the buffer is infinite, there is no blocking and no force terminations in this case
as all are served eventually.
(a)
(b)
(c)
(d)
60
Figure 6.13: Performance Analysis of 1 server with 0.4 Erl Primary Offered Traffic with
buffer
Buffering of the cognitive user allows reducing the blocking and forcing termination probability
of the cognitive users. Random simulation results for small and medium system are shown in
Figure 6.13 to Error! Reference source not found.. We can see that with the introduction of the
cognitive buffering the bandwidth utilization almost increases by double. The only price the
cognitive users have to pay is infinite time in the queue.
(a)
(b)
(c)
(d)
Figure 6.14: Performance Analysis of 2 servers with 0.7 Erl Primary Offered Traffic with
buffer
61
The buffering case is studied in small systems as it has profound effect in these systems. For big
system also the buffering can be allowed to really control the blocking and force termination
probabilities of the cognitive users.
2. Finite Buffering with Impatient Cognitive Users
In this case the cognitive user waits in the buffer for exactly τ time units before they give up.
That means the cognitive users scans for the idle spectrum for τ time unit and gives up after that
time unit. These types of customer are called impatient customers and since cognitive users are
the unlicensed users, this nature is observable in them. In this research work, this very nature is
studied for different give up rate of these impatient cognitive users for a moderate system of 5
servers with primary offered traffic of 2.5 Erl. The simulation result are shown in
(a)
(b)
62
(c)
Figure 6.15: Performance of Cognitive Users for different give up time
Figure 6.15 (a) shows the bandwidth utilization of the cognitive users with increase in the
waiting time unit, . As we can see, the bandwidth utilization increase as increases and as
all the cognitive process in the buffer are served. The price is the waiting time is infinite.
Figure 6.15 (b) and (c) shows the relation of the forced termination probability with the give up
time of the cognitive users in the buffer. It is seen that as the waiting time in buffer increases the
forced termination probability decreases and becomes zero when the waiting time is infinite.
6.3 Overview of Result
In this research work, different overlay access scheme are considered for the primary and
cognitive users to access the spectrum so as to increase the overall performance of the cognitive
users. The most primitive form of access strategy is the uncontrolled scheme in which the force
termination probability of cognitive use is quite high and bandwidth utilization is quite low
compare to the primary users. It was also shown that the forced termination of the cognitive
users can be decrease simply by using the controlled access scheme by the primary users. Even
though the force termination probability is reduced, there is no significant increase in the
bandwidth utilization of the cognitive users. These are studied by studying the various systems
with different traffic inputs.
Another scheme is to allow the cognitive users to change their frequency band to the available
idle bands whenever the spectrum band used by them is needed for the primary users to transmit.
This is a simple strategy which can be applied for the uncontrolled access scheme where the
cognitive user performs spectrum hopping by sensing the primary users. This scheme is same as
of the controlled scheme where the difference is on the implementation of the technology. The
latter case is more feasible as it preserves the integrity of the primary users and the cognitive user
performs spectrum hopping using cognitive radio.
Further the forced termination probability can be reduces if the cognitive users are allowed to
wait in the buffer whenever they are interrupted by the primary users such that they continue
retransmission as soon as there is any idle spectrum band available. The buffer can hold those
cognitive users also which are not able to enter the system as there were no idle spectrum band
63
available for them to transmit. The case study was performed using different waiting time of
cognitive users in the buffer as shown in Figure 6.15. It was seen that the force termination
probability decreases as the waiting time increases and it is almost zero when the waiting time is
infinite. Similarly the bandwidth utilization increases with increase in the waiting time .
64
CHAPTER 7
CONCLUSION
The usage of the available spectrum in fair and efficient manner is one of the most discussed
issues in wireless communication. Various researches are going on to ensure effective use of this
available spectrum holes. One of the solutions is the use of Dynamic Spectrum Sharing in
Cognitive Radio Networks.
This research study report has tried to explain various overlay spectrum sharing techniques
which can be used to share the available radio spectrum in opportunistic manner without
interfering the transmission of the ongoing systems. Emphasis is given to analysis of these
overlay spectrum sharing techniques from traffic modeling aspects. An effort is made to analyze
and understand the Markov Chain modeling for the dynamic spectrum sharing among different
cognitive users using the simulation test bed.
65
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68
APPPENDIX A
SIMULATION MODEL
A.1 Arrival Process
The arrival process is assumed to be Poisson process with mean arrival rate for
primary and cognitive users. For the calculation purpose is considered where represents
either primary or cognitive user.Therefore the probability density function of exponential
distribution is given as
( A.1 )
The cumulative density function is then calculated as
( A.2 )
From this we can calculate
( A.3 )
Thus the inter arrival time is calculated as
( A.4 )
A.2 Service Time
The service time is considered to be exponentially distributed in most of the case and is used
mainly for the verification of the simulation process. But the simulation program can generate
different service time distributions depending upon the user input. Some of the detail calculation
of different service time are shown below.
A.2.1 Exponential
The probability density function is given as
( A.5 )
Where is the holding time of particular service i.e. primary or cognitive users.
The cumulative density function is then calculated as
69
( A.6 )
From this we can calculate
( A.7 )
Thus the inter arrival time is calculated as
( A.8 )
A.2.2 Uniform
For the uniform distribution the probability density function is given as
( A.9 )
Then the cumulative density function is calculated as
( A.10 )
With mean holding time
For
( A.11 )
The service time is then calculated as
( A.12)
A.2.3 Constant
For the constant distribution the probability density function is given as
( A.13 )
Then the cumulative density function is calculated as
70
( A.14 )
The service time is then calculated as
( A.14)
A.3 Flow chart of Simulation
The main core of the program flow is shown in the figure below.
Check Event_list
If
Evnt_time < ∆t
Update serve
count of P
Check
Event
ArrivalTermination
Primary(P) or
Cognitive(C) ?
Primary Cognitiv
e
If
BW_cnt<=n-
dP
Update serve
count of C
Increase state XP
XP=XP+1
Increase State XC
XC=XC+1
Increase BW cnt
BW_cnt=BW_cnt+dP
Increase BW cnt
BW_cnt=BW_cnt+dC
Primary(P) or
Cognitive(C) ?
Update
Blk
count of P
Update Blk
count of C
Decrse BW cnt
BW_cnt=
BW_cnt - dP
Decrse BW cnt
BW_cnt=
BW_cnt - dC
Primary Cognitive
Decrease
State
XP=XP - 1
Decrease
State
XC=XC - 1
Move to Next
observation ∆tYe
s
No
Scan and allocate
server
Yes
Yes
No
NoCheck for
idle servers
Update serve
count of P
Increase state XP
XP=XP+1
Yes
No
Decrease
State
XC=XC - 1
Increase Force
termination
Cnt
P = Primary Users
C = Cognitive Users
Update
Server_list
Update
Server_list
Update
Server_list
Generate New
Primary arrival,
Add to Event_list
Update
Server_list
Generate New
Primary arrival,
Add to Event_list
Update
Server_list
Generate New
Cognitive arrival,
Add to Event_list
Delete Current
Event
Delete Current
Event
Delete Current
Event
Delete Current
Event
Delete Current
Event
Check for
idle servers
Check
occupancy of
cognitive
Generate Service
Time
Generate Service
Time
Is
Buffer
Enable
Add
Conitive
into buffer
Is
Buffer
Enable
Generate Service
Time
Add
Conitive
into buffer
Yes
Is
Buffer
Enable
Is
Buffer
Enable
Add the
buffer event
to Even_list
YesYes
No No
Figure A.1: Flow Chart for core of the Simulation
71
APPPENDIX B
VALIDATION OF SIMULATION MODEL
This chapter contains the validation of the simulation model. The consistency of the program
flow is first verified using Erlang loss model and as well as using Erlang delay model for the
case of buffering of the cognitive users. Also the validation of the mentioned access scheme is
verified by comparing the results of analytical solutions.
B.1 Validation of Program Consistency
B.1.1 Using Erlang Loss System
This is validated by simulating the Erlang loss system with only one type of service in the system
i.e. either primary or cognitive user. The arrival process is considered to be Poisson process with
exponentially distributed service time. The simulation was done for different size of the system
and was compared with the theoretical values from Erlang loss table.
Table B.1: Validation for Erlang Loss System
n = 10 n = 100
Offered
Traffic
(Erl)
Theoretical Simulated
Offered
Traffic
(Erl)
Theoretical Simulated
2 0.00004 0.00000 50 0.00000 0.00000
4 0.00531 0.00638 70 0.00014 0.00040
6 0.04314 0.04473 90 0.02696 0.03501
8 0.12166 0.12260 110 0.13612 0.14775
10 0.21458 0.21816 130 0.25163 0.25849
12 0.30193 0.30112 150 0.34537 0.35131
14 0.37728 0.37488 170 0.41966 0.41613
16 0.44056 0.44100 190 0.47928 0.47860
18 0.49348 0.49281 210 0.52800 0.52619
20 0.53796 0.53562 230 0.56848 0.56758
72
(a) For number of server n = 10
(b) For number of server n = 100
Figure B.1: Validation for Program consistency with Erlang Loss System
B.1.2 Using Erlang Delay Sytem
For verifying the queuing system in the simulation model, the simulation model is run for 1
server Erlang Delay system with infinite queue. M/M/1 is a delay system where the offered
traffic is Poisson and the interval arrival times and service time are exponentially distributed.
Since it is a delay system, the queue size is infinite and the customer in the queue doesn‟t give
up. The general state diagram is shown in Figure B.2.
73
0
λ
µ
1+q1
λ
µ
q = 0
…………………….
λ
µ
2
λ
µ
x ≥ 1
Arrival rate λ
µ Termination rate
Figure B.2: General Erlang Delay system for n servers
The queue follows the FIFO principle. For the verification of the queuing algorithm in the
simulation, M/M/1 is calculated analytically and compared it with the simulation results in Table
B.2.
Table B.2: Validation of M/M/1 System
A Dn(A) Analytical Simulated Analytical Simulated Analytical Simulated Analytical Simulated
0.2 0.2 1.2500 1.2063 0.0500 0.0545 0.2 0.2042 0.2 0.2042
0.3 0.3 1.4286 1.3530 0.1286 0.1252 0.3 0.3034 0.3 0.3034
0.4 0.4 1.6667 1.7750 0.2667 0.2910 0.4 0.4059 0.4 0.4059
0.5 0.5 2.0000 2.2828 0.5000 0.5886 0.5 0.5049 0.5 0.5049
0.6 0.6 2.5000 2.5183 0.9000 0.8934 0.6 0.5856 0.6 0.5856
0.7 0.7 3.3333 3.0842 1.6333 1.5163 0.7 0.6964 0.7 0.6964
0.8 0.8 5.0000 4.8826 3.2000 3.1410 0.8 0.7951 0.8 0.7951
0.9 0.9 10.0000 9.3613 8.1000 7.8933 0.9 0.9095 0.9 0.9095
Mean Waiting Time
tw
Mean Queue Length
E (q )
Carried Traffic BW Utilization
74
(a)
(b)
(c)
Figure B.3: Validation of M/M/1 System
75
B.2 Validation for Different Access Scheme
For the validation different cases, analytical solution is done taking different number of spectrum
bands. The simulation model can consider any number of spectrum bands but for the validation
purpose two cases are solved analytically with one server and 2 servers. For either case the
arrival rate is Poisson process with exponentially distributed service time. The calculations are
made by varying the termination rate of the cognitive users.
B.2.1 With One Server
For one server (Spectrum Band) case, the Markov Chain model is almost same for all the cases
i.e. uncontrolled, controlled and with cognitive spectrum hopping. Figure B.4 shows the state
diagram for all the cases.
0,0
λC
µC
Pri
ma
ry
Use
rs (
i)
Secondary Users (j)
0,1
1,0
λP
µP
λP
λC
λC
λP
i denotes no. of primary users
j denotes no. of secondary users
Arrival rate of Primary λP
µP Termination rate of Primary
λC Arrival rate of Secondary
µC Termination rate of Secondary
Figure B.4: Markov Chain model for 1 server
Then solving analytically using the general equation as derived in section 4.1, we have
For Node (0,0) (B.1)
For Node (0,1)
(B.2)
For Node (1,0)
(B.3)
Then normalizing with
76
( B.4 )
Once the state probabilities are calculated then we can calculate the parameters like
Blocking
( B.5 )
( B.6 )
Force Termination Probability for Cognitive users
( B.7 )
Bandwidth Utilization
( B.8 )
( B.9 )
The values are calculated and shown in Table B.3 for primary users and Table B.4 for cognitive
users.
Table B.3: Validation for Different Access Scheme with 1 server for Primary Users
Blocking Carried Traffic Bandwidth Utilization
AP AC Analytical Simulation Analytical Simulation Analytical Simulation
0.4 0.8 0.2857 0.2808 0.2857 0.2840 0.2857 0.2771
0.4 0.4 0.2857 0.2856 0.2857 0.2834 0.2857 0.2896
0.4 0.26667 0.2857 0.3122 0.2857 0.2817 0.2857 0.3006
0.4 0.2 0.2857 0.2883 0.2857 0.2782 0.2857 0.2921
0.4 0.16 0.2857 0.2877 0.2857 0.2771 0.2857 0.2856
0.4 0.13333 0.2857 0.2818 0.2857 0.2790 0.2857 0.2921
0.4 0.11429 0.2857 0.2917 0.2857 0.2796 0.2857 0.2880
0.4 0.1 0.2857 0.2877 0.2857 0.2801 0.2857 0.2878
0.4 0.08889 0.2857 0.2978 0.2857 0.2839 0.2857 0.2839
Table B.4: Validation for Different Access Scheme with 1 server for Cognitive Users
77
AP AC Analytical Simulation Analytical Simulation Analytical Simulation AnalyticalSimulation
0.4 0.8 0.5455 0.5478 0.2597 0.2601 0.1299 0.1315 0.2597 0.2638
0.4 0.4 0.4643 0.4702 0.1786 0.1789 0.0893 0.0900 0.1786 0.1729
0.4 0.2667 0.4283 0.4283 0.1361 0.1379 0.0680 0.0678 0.1361 0.1337
0.4 0.2 0.3956 0.3958 0.1099 0.1126 0.0549 0.0547 0.1099 0.1059
0.4 0.16 0.3779 0.3761 0.0922 0.0950 0.0461 0.0451 0.0922 0.0922
0.4 0.1333 0.3651 0.3629 0.0794 0.0824 0.0397 0.0391 0.0794 0.0790
0.4 0.1143 0.3554 0.3541 0.0697 0.0711 0.0348 0.0350 0.0697 0.0714
0.4 0.1 0.3478 0.3444 0.0621 0.0634 0.0311 0.0314 0.0621 0.0617
0.4 0.0889 0.3417 0.3511 0.0560 0.0573 0.0280 0.0268 0.0560 0.0573
Blocking Carried Traffic Bandwidth UtilizationForce Termination
Graphs are plotted for comparison between analytical and simulated values as shown in Figures
below
Figure B.4: Validation for Different Access Scheme for 1 server
Primary users
Cognitive users
78
Figure B.5: Validation Different Access Scheme for 1 server
B.2.2 With Two Server
B.2.2.1 Uncontrolled Access Scheme
Figure B.6 shows the Markov chain model for uncontrolled access scheme for two server‟s case.
Primary users
Cognitive users
79
0,0 λC
µC
Pri
ma
ry
Use
rs (
i)
Cognitive Users (j)
1,1
0,20,1
2,0
1,0
λC
2µC
λC
µC
λP
µP
λP 2µP
µP
λC
λ P0
,1=1
/2λ
P
λF0,2
= λP
λC
λC
λF1,1
= λP
λF 0,1
=1/2λ
P
i denotes no. of Primary users
j denotes no. of Cognitive users
Arrival rate of Primary λP
µP Termination rate of Primary
λC Arrival rate of Cognitive
µC Termination rate of Cognitive
λFi,j
= Pin
j
in
ijn
λP
i,j = λP
λP
Figure B.6: Markov Chain Model For 2 Servers
From the general equation as describe in section 4.1, the node equations can be generated as
For Node 0,0
(B.10)
For Node 01
(B.11)
For Node 02
(B.12)
For Node 10
(B.13)
For Node 11
(B.14)
80
For Node 20
(B.15)
Then normalizing with
( B.16 )
Once the state probabilities are calculated then we can calculate the parameters like
Blocking
( B.17 )
( B.18 )
Force Termination Probability for Cognitive users
( B.19 )
Bandwidth Utilization
( B.20 )
( B.21 )
The values are calculated and shown in Table B.5 for primary users and Table B.6 for cognitive
users.
81
Table B.5: Validation for Uncontrolled Scheme with 2 server for Primary Users
Blocking Carried Traffic
Bandwidth
Utilization
AP AC Analytical Simulation Analytical Simulation Analytical Simulation
1 2 0.2000 0.2031 0.8000 0.8066 0.4000 0.4033
1 1.2 0.2000 0.1974 0.8000 0.8035 0.4000 0.4017
1 0.6 0.2000 0.1875 0.8000 0.7991 0.4000 0.3996
1 0.4 0.2000 0.2125 0.8000 0.8023 0.4000 0.4012
1 0.3 0.2000 0.2020 0.8000 0.8031 0.4000 0.4015
1 0.24 0.2000 0.1917 0.8000 0.8089 0.4000 0.4044
1 0.2 0.2000 0.1944 0.8000 0.8138 0.4000 0.4062
1 0.17143 0.2000 0.1967 0.8000 0.8064 0.4000 0.4032
1 0.15 0.2000 0.1784 0.8000 0.7997 0.4000 0.3999
Table B.6: Validation for Uncontrolled Scheme with 2 server for CognitiveUsers
AP AC Analytical Simulation Analytical Simulation Analytical Simulation Analytical Simulation
1 2.0000 0.4829 0.4883 0.5470 0.5506 0.2436 0.2450 0.2735 0.2753
1 1.2000 0.4243 0.4262 0.4486 0.4561 0.2019 0.2014 0.2243 0.2281
1 0.6000 0.3477 0.3493 0.3068 0.3138 0.1409 0.1321 0.1534 0.1572
1 0.4000 0.3107 0.3059 0.2324 0.2335 0.1083 0.1041 0.1162 0.1167
1 0.3000 0.2888 0.2846 0.1870 0.1839 0.0880 0.0883 0.0935 0.0920
1 0.2400 0.2744 0.2746 0.1564 0.1532 0.0742 0.0717 0.0782 0.0766
1 0.2000 0.2640 0.2693 0.1344 0.1311 0.0641 0.0640 0.0672 0.0656
1 0.1714 0.2563 0.2598 0.1178 0.1158 0.0565 0.0552 0.0589 0.0579
1 0.1500 0.2502 0.2549 0.1049 0.1030 0.0505 0.0505 0.0524 0.0515
Blocking Carried Traffic Force Termination Bandwidth Utilization
82
83
Figure B.8: Validation for Uncontrolled scheme
B.2.2.2 Controlled and Cognitive Hopping
The Markov chain model for both the controlled and with cognitive hopping is same and is
shown in Figure
0,0 λC
µC
Pri
ma
ry
Use
rs (
i)
Secondary Users (j)
1,1
0,20,1
2,0
1,0
λC
2µC
λC
µC
λP
µP
λP 2µP
µP
λC
λP
λP
λC
λC
λP
λP
i denotes no. of primary users
j denotes no. of secondary users
Arrival rate of Primary λP
µP Termination rate of Primary
λC Arrival rate of Secondary
µC Termination rate of Secondary
Figure B.9: Markov Chain Model Controlled Scheme for 2 server
From the general equation as describe in section 4.1, the node equations can be generated as
For Node 0,0
(B.22)
For Node 0,1
(B.23)
For Node 0,2
(B.24)
For Node 1,0
84
(B.25)
For Node 1,1
(B.26)
For Node 2,0
(B.27)
Then normalizing with
( B.28 )
Once the state probabilities are calculated then we can calculate the parameters like
Blocking
( B.29 )
( B.30 )
Force Termination Probability for Cognitive users
( B.31 )
Bandwidth Utilization
( B.32 )
( B.33 )
The values are calculated and shown in Table B.7 for primary users and Table B.8 for cognitive
users.
85
Table B.8: Validation for Controlled Scheme with 2 servers for Primary Users
AP AC Analytical Simulation Analytical Simulation Analytical Simulation
1 2 0.2000 0.1952 0.8000 0.8118 0.4000 0.4059
1 1.2 0.2000 0.2015 0.8000 0.7965 0.4000 0.3982
1 0.6 0.2000 0.2004 0.8000 0.7776 0.4000 0.3888
1 0.4 0.2000 0.2041 0.8000 0.8101 0.4000 0.4051
1 0.3 0.2000 0.2006 0.8000 0.7919 0.4000 0.3960
1 0.24 0.2000 0.2024 0.8000 0.8116 0.4000 0.4058
1 0.2 0.2000 0.1976 0.8000 0.8193 0.4000 0.4097
1 0.1714 0.2000 0.1949 0.8000 0.7851 0.4000 0.3926
1 0.15 0.2000 0.1945 0.8000 0.7854 0.4000 0.3927
Blocking Carried Traffic Bandwidth Utilization
Table B.9: Validation for Controlled Scheme with 2 servers for Cognitive Users
AP AC Analytical Simulation Analytical Simulation Analytical Simulation Analytical Simulation
1 2 0.5067 0.5051 0.5778 0.5618 0.2044 0.2074 0.2889 0.2809
1 1.2 0.4435 0.4397 0.4730 0.4659 0.1623 0.1589 0.2365 0.2329
1 0.6 0.3592 0.3539 0.3208 0.3330 0.1062 0.1057 0.1604 0.1665
1 0.4 0.3182 0.3339 0.2412 0.2371 0.0788 0.0815 0.1206 0.1185
1 0.3 0.2941 0.2839 0.1930 0.1958 0.0627 0.0605 0.0965 0.0979
1 0.24 0.2782 0.2810 0.1607 0.1646 0.0522 0.0542 0.0804 0.0823
1 0.2 0.2670 0.2664 0.1377 0.1352 0.0447 0.0500 0.0688 0.0676
1 0.1714286 0.2586 0.2527 0.1204 0.1241 0.0391 0.0376 0.0602 0.0621
1 0.15 0.2522 0.2404 0.1070 0.1074 0.0348 0.0385 0.0535 0.0537
Force Termination Bandwidth Blocking Carried Traffic
86
Figure B.11: Validation for Controlled scheme