power allocation for the network

7/28/2019 POWER ALLOCATION FOR THE NETWORK

1/49

POWER ALLOCATION FOR THE NETWORKCODED COGNITIVE COOPERATIVE

NETWORK

by

Major Awal Uddin Ahmed (ID: 1003)Major Md Shariful Islam(ID: 1004)

Major K M Hasnut Zamil (ID: 1006)

A Project Report submitted to the department ofElectrical Electronic and Communication Engineering

in partial fulfillment of the requirements for the degree ofBachelor of Engineering

inElectrical Electronic and Communication Engineering

Advisor: M. Shamim Kaiser

Military Institute of Science and TechnologyMirpur Cantonment, Dhaka

December 2010


2/49

To Our Beloved Parents

ii


3/49

DECLARATION

This thesis is a presentation of my original research work. Wherever contributions

of others are involved, every effort is made to indicate this clearly, with due reference

to the literature, and acknowledgement of collaborative research and discussions. The

work was done under the guidance of Dr. M. Shamim Kaiser, at the Mililary Institute

of Science and Technology (MIST), Mirpur Cantonment, Dhaka.

(Major Awal Uddin Ahmed (ID: 1003))

(Major Md Shariful Islam(ID: 1004))

(Major K M Hasnut Zamil (ID: 1006))

iii


4/49

CERTIFICATE

This is to certify that the thesis entitled POWER ALLOCATION FOR THE

NETWORK CODED COGNITIVE COOPERATIVE NETWORK and sub-

mitted by Major Awal Uddin Ahmed (ID: 1003), Major Md Shariful Islam(ID: 1004),

Major K M Hasnut Zamil (ID: 1006) for the degree of Bachelor of Engineering in

Electrical Electronics and Communication Engineering. They embody original work

under my supervision to the best of my knowledge.

(Signature in full of The Supervisor)

Dr. M. Shamim Kaiser

Assistant Professor (Visiting Faculty),EECE, MIST

iv


5/49

ACKNOWLEDGEMENTS

First, we would like to express our deepest gratitude to our advisor Professor

Dr. M. Shamim Kaiser for his excellent guidance and continual support during the

course of our degree and the project. Second, we would like to thank Professor S

P Majumder for his valuable teaching during the course of our studies. For their

valuable teaching in different levels of the course which has helped us a lot in this

project work, we would like to thank all the teachers of Electrical, Electronic and

Communication Engineering (EECE) department of Military Institute of Science and

Technology (MIST).

The financial support provided by the MIST for the thesis entitled Cognitive

Radio Network and the technical assistance of EECE department are duly acknowl-

edged as well. We owe our thanks to faculty and staff of the EECE department for

their all out assistance.

We are thankful to Bangladesh Army for allowing us to undergo the engineering

course in MIST to enhance our professional and personal knowledge.

Finally, we are deeply indebted to our family for their love and support throughout

this degree and our life.

v


6/49

ABSTRACT

Demand for higher data rate wireless applications has led to scarcity in radio fre-

quency spectrum. This article focuses the power allocation for the network coded

cognitive cooperative network (NCCCN). Analog network coded (ANC) Orthogonal-

Frequency-Division-Multiplexing improves the capacity of the cognitive cooperative

network (CCN). Moreover, CCN enhances the spectrum utilization efficiency. A

power allocation optimization problem have been formed that maximizes the data

transmission rate of the NCCCN under the total transmit and peak-interference pow-

ers or the total transmit and average-interference powers. The spectral efficiency of

the proposed network is compared with the spectral efficiency of CCN without ANC.

Simulation results show that the proposed NCCCN enhances spectral efficiency in

compared to the CCN without ANC.

vi


7/49

TABLE OF CONTENTS

DEDICATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii

DECLARATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

CERTIFICATE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv

ACKNOWLEDGEMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . v

ABSTRACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi

LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix

CHAPTER

I. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1.1 Spectrum Sensing . . . . . . . . . . . . . . . . . . . 11.1.2 Spectrum Management . . . . . . . . . . . . . . . . 21.1.3 Spectrum Mobility . . . . . . . . . . . . . . . . . . 31.1.4 Spectrum Sharing . . . . . . . . . . . . . . . . . . . 31.1.5 Network Coding . . . . . . . . . . . . . . . . . . . . 4

1.2 Assumption and Limitation . . . . . . . . . . . . . . . . . . . 51.3 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61.4 Organization of Thesis . . . . . . . . . . . . . . . . . . . . . . 6

II. Literature Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.1 Wireless Communication . . . . . . . . . . . . . . . . . . . . 82.2 Cognitive Radio Network . . . . . . . . . . . . . . . . . . . . 92.3 OFDM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102.4 Advantages of OFDM . . . . . . . . . . . . . . . . . . . . . . 112.5 Disadvantages of OFDM . . . . . . . . . . . . . . . . . . . . . 122.6 Characteristics and Principles of Operation . . . . . . . . . . 132.7 OFDM in CRN . . . . . . . . . . . . . . . . . . . . . . . . . . 14

vii


8/49

2.8 Cooperative Cognitive Networks . . . . . . . . . . . . . . . . 142.9 Analysis of Network Coding . . . . . . . . . . . . . . . . . . . 14

2.9.1 Broadcasting in Single Channel Wireless Networks . 182.9.2 Reactive Network Coding . . . . . . . . . . . . . . . 182.9.3 Proactive Network Coding . . . . . . . . . . . . . . 20

2.10 Broadcasting in Multi Channel Wireless Networks . . . . . . 212.11 Advantages of Adaptive Power Allocation . . . . . . . . . . . 23

III. System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

3.1 Cognitive cooperative network (CCN) . . . . . . . . . . . . . 253.2 Power Allocation Algorithm . . . . . . . . . . . . . . . . . . . 29

3.2.1 One Primary and One Secondary Links . . . . . . . 29

IV. Simulation Results and Discussion . . . . . . . . . . . . . . . . 32

4.1 Simulation Parameters . . . . . . . . . . . . . . . . . . . . . . 324.2 Discussion of the Results . . . . . . . . . . . . . . . . . . . . 32

V. Conclusion and Future Work . . . . . . . . . . . . . . . . . . . . 36

5.1 Conclustion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 365.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

viii


9/49

LIST OF FIGURES

Figure

1.1 Cognitive radios are using the licensed band of licensed users as wellas unlicensed user. . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.2 Utilization of spectrum using cognitive radio network. . . . . . . . . 3

1.3 Organization of the report. . . . . . . . . . . . . . . . . . . . . . . . 7

2.1 Communication link is interrupted due to the deep fading. . . . . . 15

2.2 Cooptative communication uses one relay to establish communicationbetween source and destination. . . . . . . . . . . . . . . . . . . . . 16

2.3 Example of data dissemination in a wireless ad hoc network: tradi-tional store and forward vs. network coding. . . . . . . . . . . . . . 17

3.1 A simplified cognitive cooperative network (CCN). . . . . . . . . . . 26

3.2 A Network coded CCN. Left node transmits information to relay andright node in the 1st-time slot, Right node transmits informationto relay and left node in the 2nd-time slot. Relay broadcast theinformation in the 3rd-time slot. . . . . . . . . . . . . . . . . . . . . 27

3.3 Water-filling Power Allocation . . . . . . . . . . . . . . . . . . . . . 31

4.1 Effect of Pmax on spectral efficiency considering Imax. . . . . . . . . 34

4.2 Effect of Pmax on spectral efficiency considering Iave. . . . . . . . . . 34

4.3 Effect of Imax on spectral efficiency considering Pmax. . . . . . . . . 35

4.4 Effect of Imax on spectral efficiency considering Pave. . . . . . . . . . 35

ix


10/49

CHAPTER I

Introduction

1.1 Background

A Cognitive Radio (CR) allocates licenced spectrum to unlicenced user, as shown

in Figure 1.1 and changes transmission parameter to avoid interference with primary

users (PU). These parameters are changed basing on the active monitoring of several

factors in the external and internal radio environment, such as radio frequency spec-

trum, user behavior and network state. The CRs can enhanced the overall spectrum

utilization efficiency as shown in Figure 1.3. The main functions of CRs are:

1.1.1 Spectrum Sensing

Spectrum sensing is used to detect the unused spectrum and share it without

harmful interference with other users. It is an important requirement of the CR

network to sense spectrum holes (Unused Licensed Spectrum). The most efficient way

to detect spectrum holes is by detecting primary users. Spectrum sensing techniques

can be classified into three categories:

1. Transmitter Detection: CRs must be able to determine whether a signal from

a primary transmitter is locally present in a certain spectrum or not.

1


11/49

Figure 1.1: Cognitive radios are using the licensed band of licensed users as well asunlicensed user.

2. Cooperative Detection: Cooperative detection are the spectrum sensing meth-

ods where information from multiple CR users are incorporated for primary

user detection.

3. Interference based detection.

1.1.2 Spectrum Management

It is the process where the best available spectrum is captured to meet user com-

munication requirements without creating undue interference to other users (PU).

CRs should decide on the best spectrum band to meet the Quality of service require-

ments over all available spectrum bands. So spectrum management functions are

required for CRs. These management functions can be classified as:

1. Spectrum analysis

2


12/49

Sense Usevoid

p

Figure 1.2: Utilization of spectrum using cognitive radio network.

2. Spectrum decision

1.1.3 Spectrum Mobility

It is the process when a CR user exchanges its frequency of operation. CRN

emphasizes on using the spectrum in a dynamic manner by allowing the radio termi-

nals to operate in the best available frequency band. During this it also emphasizes

on maintaining seamless communication requirements during the transition to better

spectrum.

1.1.4 Spectrum Sharing

Spectrum sharing provides the fair spectrum scheduling method. It is one of the

major challenges in open spectrum usage.

3


13/49

1.1.5 Network Coding

During its operation in CR environment the licensed spectrum is required to be

access in such a way that, the PU of a particular licensed spectrum is not being

interfered. Again at the same time the communication between the secondary users

need to be on. To accomplish this, CRs usually depend on few mechanisms such as

cooperation, learning and negotiation. Following certain activities like observing the

wireless environment, exchanging information, and evaluating different actions com-

munication of the secondary users and maintain the integrity of PU activity is ensured

by CRs. CRN uses a technique in order to achieve this standard of performance which

is called Network Coding. Its main features are:

1. Fast and reliable network deployment through neighbor discovery algorithm.

2. Very fast exchange of control information is done by a dedicated control channel.

3. Efficient cooperative detection of PU activity.

4. Distributed allocation of the spectrum resources to CRs for both single hop and

multi hop CRN.

5. A spectrum aware cluster formation protocol that allows spectrum reuse.

Considering the broadcasting in single channel wireless networks the network cod-

ing can be divided into two main types having their sub types as well. The two main

types of network coding are:

1.1.5.1 Reactive Network Coding

In reactive protocols, nodes participate in the dissemination of data only when

they receive innovative information. This type of network coding has got total three

different types of schemes, all are basing on the forwarding factor which is the ratio of

4


14/49

average number of packets transmitted to the average number of innovative packets

received per node.

Probabilistic network coding.

Semi-deterministic network coding.

Timed network coding.

1.1.5.2 Proactive Network Coding

Proactive Network Coding does not require the reception of innovative information

to continue data dissemination. So it is more robust to interference and collisions and

its performance does not depend on the forwarding factor . It is based on two

important components:

A set of conditions to stop transmissions when all source packets have been

delivered to all nodes, i.e., Stopping Conditions (SCs).

A strategy to set the frequency at which new random packet combinations are

to be sent so as to avoid network congestion. In the rest of the section we refer

to this strategy is known as Rate Adaptation mechanism.

1.2 Assumption and Limitation

The following assumptions have been made:

The downlink transmission is considered.

The channel state information (CSI) is available at the receiver with no delay.

The channel gain co-efficient is considered to be independent-and-identically-

distributed (iid) random variable.

Selfish and Malicious relay nodes are ignored.

5


15/49

1.3 Objectives

To design a system for the optimization of power allocation problem in order

to maximize the data transmission rate of the network coded cognitive cooperative

network (NCCCN) under two specific conditions:

1. The total transmits power and peak-interference powers.

2. The total transmits power and average-interference powers.

1.4 Organization of Thesis

In Figure ?? we show the structural organization of this work. Our discussion

begins in Chapter 1 which introduces the background under few main headings as

spectrum sensing, spectrum management, spectrum mobility and network coding.

Under network coding main two types of network coding i.e. reactive network coding

and proactive network codings are being discussed with their limitations as well as

advantages of one over another.

In Chapter 2 we have discussed few topics required to understand the cognitive

radio concept more clearly. These are wireless communication, cognitive radio net-

work, OFDM, OFDM in CRN, Cooperative Cognitive Networks, Analysis of Network

Coding, Broadcasting in Single Channel Wireless networks, Broadcasting in Multi

Channel Wireless Networks, Advantages of Adaptive Power Allocation.

Chapter 3 introduces the architecture of the proposed system deign aspect and

the power allocation algorithm for such a system. For the power allocation algorithm

two different conditions are being considered. These are firstly the total transmit

power and the peak interference power and secondly the total transmit power and

the average interference power.

In Chapter 4 the numerical result and the simulation results are being shown.

6


16/49

Figure 1.3: Organization of the report.

Finally Chapter 5 deals with the concluding remarks and highlighting the probable

future works.

7


17/49

CHAPTER II

Literature Survey

2.1 Wireless Communication

A lot of development works have been done in the last two decades to enhance

wireless communication technologies. Rapidly the wireless communications are be-

coming conventional source of connectivity for everyone. There is a possibility that

within a short period one third of the worlds total population will use wireless devices

for communication purposes. Now a days wireless voice-centric communications is be-

coming the substitute of the well established wired communication system in many

developed countries. This development is quickly thinning out into all countries of

the world.

One of the major reasons for the development of wireless communication system

is the increasing user acceptance of wireless Internet. The wireless communication

networks ranges from large to small networks, starting with very large distribution

networks of up to hundreds of kilometers wide down to few meters short-range net-

works. Several other reasons for the development of increasingly smaller wireless

networks includes the pressure to move towards unused frequency bands of the spec-

trum and the need to support higher data throughput.

8


18/49

2.2 Cognitive Radio Network

Usually fixed spectrum assignment policies are used for wireless networks. Accord-

ing to these policies, licensees are granted the rights for exclusive use of frequency

bands on a long term basis over vast geographical areas. Several portions of the li-

censed bands are unused or under-used at many times and/or locations because of

this static allocation of the available spectrum resources OFCOM (2009). Besides

this, several recent technologies - such as IEEE 802.11, Bluetooth, and to some ex-

tent WiMAX - that operate in the Industrial, Scientific and Medical (ISM) unlicensed

bands, have experienced a huge success and proliferation. As a result, the wireless

spectrum accessed by them - especially the 2.4 GHz ISM band - has become over-

crowded. In order to provide further spectrum resources for these technologies, and

to allow potential development of alternative ones, it has recently been proposed to

allow unlicensed devices, called Secondary Users (SU), to access those licensed spec-

trum resources that are unused or sporadically used by their legitimate owners (PU).

This is called Dynamic Spectrum Access. This technology enables SU to find and

opportunistically exploit unused or underused spectrum bands is called CR Haykin

(2005).

Due to the recent achievements in the field of Software De?ned Radios (SDR), the

concepts of Dynamic Spectrum Access and CR have attracted significant attention

by the research community III (2000). The required technological background for the

realization of low-power CR transceivers were developed from the knowledge of SDR

and Dynamic Spectrum Access. These low-power CR transceivers are able to change

their transmitter parameters (operating frequency, modulation, transmission power

and communication technology) as a response to changes in the wireless environment.

Spectrum shortage problem faced by traditional wireless network is being alleviate

by viable architectural solutions provided by CRN Yoon et al. (2009) ref (1990) . It

is accomplished by exploiting the existing wireless spectrum opportunistically. How-

9


19/49

ever, it must be kept in mind while designing such solutions that, besides the strict

requirements imposed by the opportunistic coexistence with PU, CRs may also have

to deal with other malicious/selfish (adversary) CRs that aim at denying/gaining ac-

cess to the available spectrum resources with no regard to fairness or other behavioral

etiquettes. This is possible because the same SDR technology can enable adversary

CRs to significantly modify the perception that legacy CRs have of the surrounding

environment. This results in interruption of operation for CRN.

Hence, CRs must rely on mechanisms such as cooperation, learning and negotia-

tion in order to opportunistically access the licensed spectrum in a manner that there

is no interference and, at the same time, guarantee their own communications in the

face of malicious attacks. CRs can take the appropriate countermeasures to ensure

the continuity of their communications and the integrity of PU activity by observing

the wireless environment, exchanging information, and evaluating different actions,

2.3 OFDM

The fundamental principle of the OFDM system is to decompose the high rate

data stream (Bandwidth) into N lower rate data streams and then to transmit them

simultaneously over a large number of subcarriers. A sufficiently high value of N

makes the individual bandwidth (W/N) of sub-carriers narrower than the coherence

bandwidth (Bc) of the channel. The individual sub-carriers as such experience at

fading only and this can be compensated for using a trivial frequency domain single

tap equalizer. The choice of individual subcarrier is such that they are orthogonal to

each other, which allows for the overlapping of sub-carriers because the orthogonality

ensures the separation of subcarriers at the receiver end. This approach results in a

better spectral efficiency compared to FDMA systems, where no spectral overlap of

carriers is allowed.

10


20/49

2.4 Advantages of OFDM

Following are the advantages of OFDM transmission:

Allows simultaneous low-data-rate transmission from several users.

Lower maximum transmission power for low data rate users.

Shorter delay and constant delay.

Contention-based multiple access (collision avoidance) is simplified.

Further improves OFDM robustness to fading and interference.

Flexibility of deployment across various frequency bands with little needed mod-

ification to the air interface.

Flexibility of deployment across various frequency bands with little needed mod-

ification to the air interface.

Averaging interferences from neighboring cells, by using different basic carrier

permutations between users in different cells.

Interferences within the cell are averaged by using allocation with cyclic per-

mutations.

Offers Frequency diversity by spreading the carriers all over the used spectrum.

Can easily adapt to severe channel conditions without complex time-domain

equalization.

Robust against narrow-band co-channel interference.

Robust against inter symbol interference (ISI) and fading caused by multipath

propagation.

11


21/49

High spectral efficiency as compared to conventional modulation schemes, spread

spectrum, etc.

Efficient implementation using Fast Fourier Transform(FFT).

Low sensitivity to time synchronization errors.

Tuned sub-channel receiver filters are not required (unlike conventional FDM).

2.5 Disadvantages of OFDM

Following are the disadvantages:

Higher sensitivity to frequency offsets and phase noise.

Asynchronous data communication services such as web access are char-

acterized by short communication bursts at high data rate. Few users in a

base station cell are transferring data simultaneously at low constant data

rate.

The complex OFDM electronics, including the FFT algorithm and forward

error correction, is constantly active independent of the data rate, which is

inefficient from power consumption point of view, while OFDM combined

with data packet scheduling may allow FFT algorithm to hibernate during

certain time intervals.

The OFDM diversity gain, and resistance to frequency-selective fading,

may partly be lost if very few sub-carriers are assigned to each user, and

if the same carrier is used in every OFDM symbol. Adaptive sub-carrier

assignment based on fast feedback information about the channel, or sub-

carrier frequency hopping, is therefore desirable.

12


22/49

Dealing with co-channel interference from nearby cells is more complex in

OFDM than in CDMA. It would require dynamic channel allocation with

advanced coordination among adjacent base stations.

The fast channel feedback information and adaptive sub-carrier assignment

is more complex than CDMA fast power control.

2.6 Characteristics and Principles of Operation

In OFDM, the sub-carrier frequencies are chosen so that the sub-carriers are or-

thogonal to each other, meaning that cross-talk between the sub-channels is elim-

inated and inter-carrier guard bands are not required. This greatly simplifies the

design of both the transmitter and the receiver; unlike conventional FDM, a separate

filter for each sub-channel is not required. The orthogonality also allows high spectral

efficiency, with a total symbol rate near the Nyquist rate for the equivalent baseband

signal (i.e. near half the Nyquist rate for the double-side band physical pass band

signal). Almost the whole available frequency band can be utilized. OFDM generally

has a nearly white spectrum, giving it benign electromagnetic interference properties

with respect to other co-channel users.

OFDM requires very accurate frequency synchronization between the receiver and

the transmitter; with frequency deviation the sub-carriers will no longer be orthogo-

nal, causing inter-carrier interference (ICI) (i.e., cross-talk between the sub-carriers).

Frequency offsets are typically caused by mismatched transmitter and receiver os-

cillators, or by Doppler shift due to movement. While Doppler shift alone may be

compensated for by the receiver, the situation is worsened when combined with mul-

tipath, as reflections will appear at various frequency offsets, which is much harder to

correct. This effect typically worsens as speed increases, and is an important factor

limiting the use of OFDM in high-speed vehicles.

13


23/49

2.7 OFDM in CRN

The spectral efficiency of an OFDM system is more than that of conventional multi

carrier system like FDMA. This is due to the overlapping multi carrier modulation

technique that is followed in OFDM. In CRN using OFDM technique, bandwidth of

the channel can be utilized more efficiently. In FDMA, guard bands are introduced

between the different carriers in the frequency domain, which results in a waste of

the spectrum efficiency. However, it is possible to arrange the carriers in an OFDM

system such that the sidebands of the individual sub-carriers overlap and the signals

are still received without adjacent carrier interference.

2.8 Cooperative Cognitive Networks

The main challenge in cognitive radio ad hoc networks is maximizing the through-

put. In cognitive network the availability of local spectrum resources may change from

time to time and hop-by-hop. To achieve this objective, cooperative transmission is a

technique to increase the capacity of relay links by exploiting spatial diversity without

multiple antennas at each node. Unlike conventional point-to-point communications,

cooperative communication is a new form of diversity that allows users or nodes to

share resources to create collaboration via distributed transmission and processing of

messages as shown in Figure 2.2.

2.9 Analysis of Network Coding

In order to increase throughput, reduce delay, and enhance robustness, network

coding is a recently introduced standards for data transmission in wireless networks.

Network coding provides a technique of a store, code and forward in contrast to tra-

ditional store and forward approaches. In this technique each node stores all the

incoming packets in an internal buffer and transmits their linear combinations. Com-

14


24/49

bining is performed over some finite Galois Field. This technique provides increased

throughput efficiency, scalability and robustness due to its inherent capability of ap-

proaching the network capacity in practical settings.

Figure 2.1: Communication link is interrupted due to the deep fading.

Network coding was first introduced in their seminal work by Ahlswede et al.

Cannons et al. (2006). Network coding can be defined as a particular in-network

data processing technique that exploits the characteristics of the wireless medium.

It exploits the broadcast communication channel, in order to increase the achievable

throughput of wireless networks.

15


25/49

Figure 2.2: Cooptative communication uses one relay to establish communication be-tween source and destination.

Network coding implements a more complex store, encode, and forward approach

in contrast to the store and forward pattern. Here each node stores the incoming

packets in its own buffer, and successively sends a combination of the stored data.

While successfully decoding, e.g., n packets, at least n independent combinations

of the original packets must be collected by a node. This is how it can provide

high throughput gains in multicast or broadcast networks. Using network coding

we can achieve higher transmission rates than separate unicast transmissions when

information sources transmit to multiple destinations or to all nodes in the network.

16


26/49

A R B A R B

(a) Store and Forward (b) Store, Encode and Forward

Slot2

PAPB PA PBSlot1

PA

PBPB

PASlot3

Slot4

Slot2

PA PB PA PB

Slot1

PA

PA PB

PB

PA PBSlot3

Slot4

Figure 2.3: Example of data dissemination in a wireless ad hoc network: traditionalstore and forward vs. network coding.

In Figure 2.3 we show an example of network coding in a simple two hop wireless

network and compare it to the traditional store and forward approach. The network

consists of two nodes A and B and a relay node R. In this example, nodes A and B are

interested in exchanging with each other the data packets PA and PB, respectively.

The distance between A and B is such that it is not possible for them to directly

exchange their packets, i.e., they are not within each others transmission range. So,

in order to exchange information they have to relay their transmissions through node

R. In Figure 2.3 we see that for both the traditional (store and forward) approach andnetwork coding (store, encode and forward), during the first two time slots nodes A

and B forward their packets to the relay node R. Once the relay node R has received

both packets PA and PB it can use the traditional approach (see Figure 2.3 (a)).

According to it, the relay node R forwards the packets in subsequent time slots (PA is

17


27/49

transmitted in time slot 3 and PB in time slot 4) allowing to deliver the information

to both destination nodes A and B in 4 time slots. On the other side, if network

coding is used (see Figure 2.3 (b)), the relay node R transmits a XOR-ed version of

packets PA and PB to both nodes A and B. At this point, node A (B) can decode

packet PB (PA) by simply subtracting its own packet from the received one. This

way, it is possible for the nodes to receive the packets in 3 time slots instead of 4

time slots, as required by the traditional approach. This example shows how network

coding is particularly effective whenever there are overlapping data flows as it can

exploit both the broadcast nature of the wireless channel and the coding process to

simultaneously deliver different packets to multiple users.

2.9.1 Broadcasting in Single Channel Wireless Networks

Reliable data broadcasting in single channel wireless ad hoc networks can be

done using practical dissemination algorithm by exploiting network coding. The use

of network coding in realistic wireless environments must be included in efficient

design of such algorithms. Impact on the network performance must be identified to

achieve substantial benefits by designing heuristic and proactive mechanisms. This

mechanism optimizes the network operation.

Network coding based algorithms Yang and Wu (2010) are suitable for data broad-

casting in single channel wireless ad hoc networks. According to these algorithms,

whenever an innovative packet is received at a given node, it generates with a new

packet through network coding and broadcasts it over the wireless channel.

2.9.2 Reactive Network Coding

There are three different packet combination strategies based on network coding,

where the combination coefficients are scalars randomly picked in Galors Field (28).

All the presented schemes are characterized by the forwarding factor which is defined

18


28/49

as follows: the ratio between the average number of packets transmitted and the

average number of innovative packets received per node.

Probabilistic network coding:In Probabilistic coding a random linear combina-

tion of the packets are sent by each node in its buffer. When an innovative

packet is received, a new combination is transmitted with probability . On the

contrary, when nothing is transmitted the probability is 1 . For example if

the forwarding factor is = 0.5, it means that a node on average sends a new

packet every two innovative packets received. From Fragouli et al. (2008) we

know that = 0.5 would theoretically (circular topology, ideal scheduling, and

no collisions) assure a packet delivery ratio of 1 when the number of neighbors

is 2.

Semi-deterministic network coding: In this case, each node sends out a new

combination after having received exactly 1/ innovative packets for a given

forwarding factor , As an example, = 0.5 means that each node determinis-

tically transmits a new combination every two received innovative packets. The

forwarding factor, in this case, is not related to a probability but is rather used

as a threshold on the number of incoming packets.

Timed network coding: There are two major drawbacks in Probabilistic network

coding and Semi-deterministic network coding. They are particularly sensitive

to packet losses, e.g., due to collisions. In fact, if one of the transmitted pack-

ets is lost, the propagation of the information through the network could be

interrupted.

Both probabilistic and semi-deterministic network coding suffer from some in-

efficiencies. When there is a small number of packets to combine, new combi-

nations are created from a small set of packets. A timing strategy is introduced

into the first scheme to alleviate these problems. For each received innovative

19


29/49

packet a timer is activated. When the timer has expired, the node decides to

send out a new random combination with probability . The timer, is a uni-

form random variable in [0, max]. This approach has two advantages. With the

introduction of a waiting interval before transmission, nodes have the chance of

collecting other innovative packets and send out richer combinations. Moreover,

the collision probability at the MAC layer is decreased by reducing the number

of transmissions with the help of random characteristic of the timer.

2.9.3 Proactive Network Coding

In reactive protocols the nodes participate in the dissemination of data only when

they receive innovative information. The dissemination is interrupted even though

nodes may still have innovative information to send if this does not occur. This fact

is an inherent characteristic of the reactive approach. In this section we describe a

network coding data dissemination scheme based on a proactive approach (ProNC) to

address this problem. Though focused on the scenarios where data is to be exchanged

among all the users of a wireless ad hoc network, the rationale behind ProNC also

applies to different settings. This scheme is completely distributed and self-adaptable

and requires very limited network knowledge, which can be easily acquired by over-

hearing the exchanged data.

The reactive schemes are likely to suffer from the presence of interference and

collisions in realistic radio environments. The main problem of reactive schemes is

that new random combinations are generated and transmitted only when innovative

(i.e., linearly independent) information is received. Innovative packets may howeverbe lost due to packet collisions, thus interrupting data propagation. Even worse, the

insertion of innovative information into a given network area often causes all nodes in

the area to attempt their new transmissions simultaneously which further increases

the collision probability.

20


30/49

In case of reactive probabilistic network coding, nodes send out new combinations

based on a forwarding factor . The value of depends on their number of neighbors

[7]. If we set the value of inversely proportional to the number of neighbors, we get

the desirable effect that the number of innovative packets/area is independent of the

local node density. There are particular topologies where this strategy does not work.

As an example, if we consider a given node t has a large number of neighbors and one

of them, say node r, has only t as its neighbor. Due to its high number of neighbors,

t sends out a small number of packets and, in turn, r is unlikely to be able to decode

all the wanted information (as it did not receive enough independent combinations

from t). On the other hand, the reception of innovative information to continue data

dissemination is not required by ProNC. This is why it is more robust to interference

and collisions and its performance does not depend on the forwarding factor . The

performance is based on two important components:

A set of conditions to stop transmissions when all source packets have been

delivered to all nodes, i.e., Stopping Conditions (SCs),

A strategy to set the frequency at which new random packet combinations are

to be sent so as to avoid network congestion. This strategy is referred as Rate

Adaptation mechanism.

2.10 Broadcasting in Multi Channel Wireless Networks

Multiple parallel transmissions on orthogonal frequency bands are possible in multi

channel wireless networks. It leads to a more efficient utilization of spectrum resources

than their single channel broadcasting. Increased throughput and robustness to inter-

ference generated by other users is provided by using multiple channels. As a result,

significant benefits to wireless ad hoc, sensor and CRN are expected using multiple

parallel channels. Nodes need to coordinate in order to efficiently share the available

21


31/49

wireless resources in such a multichannel system, Hence, it is important to design

a robust dissemination protocol for broadcasting to enable nodes in exchanging the

required information for coordination.

Designing and operating multichannel wireless networks is one of the challenges in

the coordination of the nodes operating in the system. Some strategy or control rules

must be in place in order for nodes to exchange control information. One important

aspect in this exchange of information is the ability of nodes to broadcast informa-

tion to all other users in their neighborhood. Dissemination of routing information,

information about availability of spectrum, or neighbor discovery is included in this

type of broadcast.

At first we have to select a single channel for broadcasting purposes. The draw-

backs of this approach include:

This strategy eliminates the possibility that information broadcast benefits from

the use of multiple channels if the single chosen channel becomes congested;

This solution cannot be used in opportunistic cognitive networking scenarios

where the availability of a given channel cannot be guaranteed a priori Akyildiz

et al. (2006).

If the chosen channel experiences high levels of interference, the system perfor-

mance may degrade.

It is easy for an adversary to jam a single control channel.

A second approach we designate a fixed number of channels, c, to be used for

broadcasting purposes. In this case nodes in the system must move between the c

channels to disseminate their information to all other nodes over time. Though this

solution may be more robust than using a single control channel, it still suffers from

some drawbacks:

22


32/49

Ifc is chosen to be too large, random encounters of nodes on a common channel

may become too infrequent, thus requiring a long time for information to be

received by all nodes.

If c is chosen to be too small, the channels may become congested.

It may be possible for a small number of adversaries to effectively jam a small

number of control channels. In fact, as we show below, using simple mecha-

nisms for disseminating information over c channels can often lead to very high

dissemination delays.

2.11 Advantages of Adaptive Power Allocation

The major advantage of cognitive radio technology is its ability to search for

available spectrums in its surrounding environment and adjust its transmit parame-

ters accordingly to increase the system performance. The transmit power is one of

the most important parameter. The method which is used by unlicensed users (or

secondary users) access the licensed spectrum is called spectrum sharing. In general

there are two different ways to share the spectrum. One scheme works by looking for

spectrum holes for the use of secondary users (SUs), and the other allows both the

PUs and SUs to operate simultaneously. For sharing the spectrum in second way it is

important to maintain the tolerable interference level at the PUs. In wireless network,

it is obvious that the interference will be introduced by multi-users. The PUs should

always get the priority for using the spectrum. For a CRN it is important to design

a power control policy which can maximize system output of the SUs. For designing

a successful power control policy, one of the most important issues is to minimize

the interference to PUs generated by SUs. The average and peak transmit power

constraints should also be considered. Adaptive power allocation is an effective way

to reduce interference by means of updating transmit powers according to the target

23


33/49

SINR. Besides, by adaptive power allocation it is also possible to conserve energy for

longer battery life.[10,11] Thus optimal adaptive power allocation is widely used for

wireless communication.

24


34/49

CHAPTER III

System Model

3.1 Cognitive cooperative network (CCN)

Figure 3.1 shows a simplified cognitive cooperative network (CCN). It consists of

3 P U s and 3 SU s. Two ofP U s (i.e., A and B) and Two of the SU s (i.e., a and b)

are communicating with each other through the remaining P U (i.e., R) and SU (i.e.,

r) nodes respectively. In the first time slot, wireless node transmits and wireless

nodes and receive; in the second time shot, wireless node transmits and wireless

nodes and receive; In the third time slot forwards the transmission of and

(Fig.3.2). Here {A, a}, {B, b} and {R, r} The total number of OFDM

subcarriers is N. It is assumed that the channel fading model is multipath Rayleigh,

amplify-and-forward (AF) relaying is employed at the relay node, channel coefficients

are symmetric, complex additive white gaussian noise with mean zero and variance 2

and channel state information (CSI) are available to receiver. Using similar deduction

of Ma et al. (2009)Kaiser and Ahmed (2010), the SNR of the received signal at the

node b and a of the NCCCN, i.e., nab and nba, in the three time slot is given by Li

et al. (2009)

25


35/49

PrimaryRelaystation Prims

Se

aryBaseation

condaryUsers

Figure 3.1: A simplified cognitive cooperative network (CCN).

nab =nPnc |h

na,b|

2

2 + nPnp |gnA,b|

2+

nPnc |hna,c|

2

2+nPnp |gnA,c

|2(1n)Pnc |h

nc,b|2

2+(1n)Pnp |gnC,b|2

2nPnc |h

na,c|

2

2+nPnp |gnA,c|2

+(1n)Pnc |h

nc,b|2

2+(1n)Pnp |gnC,b|2

, (3.1)

nba =(1 n)Pnc |h

nb,a|

2

2 + (1 n)Pnp |gnB,a|

2

+

(1n)Pnc |hnb,c|2

2+(1n)Pnp |gnB,c

|2nPnc |h

nc,a|

2

2+nPnp |gnC,a

|2

(1n)Pnc |hnb,c|2

2+(1n)Pnp |gnB,c

|2+ 2

nPnc |hnc,a|

2

2+nPnp |gnC,a

|2

, (3.2)

where and n are power allocation proportional factors of the P U and SU

respectively, Pnp and Pnc are the total link power of the P U and SU respectively, h

nu,v

is the channel gain of the u v link at n-th subcarrier,

where (u, v) {(a, c), (b, c), (a, b), (b, a), (c, a), (c, b)}, gnU,v is the channel gain of the

U v interference link at n-th subcarrier, where (U, v) {(A, c), (B, c), (A, b), (B, a), (C, a),

The optimal value of and n can be determined using Eqn. (10) of [12].

26


36/49

A

C

B

a b

c

ACh

ABh

abh

ach

Abg

Acg aBf

A

C

B

a b

c

BCh

BAh

bah

bch

bAg

bCg

Baf

Bcf

A

C

B

a b

c

CBhCAh

ca

h cbh

Cag

Cbg

cAf

cBf

First time slot

Third time slot

Second time slot

Figure 3.2: A Network coded CCN. Left node transmits information to relay andright node in the 1st-time slot, Right node transmits information to relayand left node in the 2nd-time slot. Relay broadcast the information inthe 3rd-time slot.

Equation (3.1) can be simplified as

27


37/49

nab =nPnc |h

na,b|

2

2 + nPnp |gnA,b|

2

+Pnc (1

n

)|h

n

c,b|

2

n

|h

n

a,c|

2

(1n)|hnc,b|2+2n|hna,c|

2

2 + Pnp |gn2 |

2, (3.3)

where |gn2 |2 =

2(1n)|gnC,b|2n|hna,c|

2+(1n)Pnc |hnc,b|2n|gnA,c|

2


2 .

In the worst case, the maximum equivalent channel gain of the interference link

from P U to SU, i.e., |gn|2, is given by |gn|2 = max(n|gnA,b|2, |gn2 |

2) and the equivalent

channel gain ofacb link, i.e., |hn|2, is given by |hn|2 = n|hna,b|2 +

(1n)|hnc,b|2n|hna,c|

2


2 .

Equation (3.3) can be simplified as

nab =Pnc |h

n|2

2 + Pnp |gn|2

, (3.4)

The channel capacity of a b link using nth subcarrier, i.e., nab, can be

written as

nab =1

3log2 (1 +

nab) . (3.5)

In Kaiser et al. (2010), the maximum interference temperature, i.e., Qnmax, is given

as

Qmax =PmaxkBc

, (3.6)

where fc is the center frequency, Pmax is the maximum allocated transmit power

over all subcarriers, B is the bandwidth and k is the Boltzmanns constant.

28


38/49

3.2 Power Allocation Algorithm

3.2.1 One Primary and One Secondary Links

The sum capacity maximization problem for SU s can be written as

maxNn=0

B

3Nlog2

1 +

Pnc |hn|2

2 + Pnp |gn|2

. (3.7)

The constraints are

N

n=0Pnc Pmax, (3.8)

Pnc fn2 Qnmax, (3.9)

1

N

Nn=0

Pnc fn2 Qave, (3.10)

where

|fn|2 = max

n(|fnaB|2, |fnaC|

2), (1 n)(|fncA|2, |fncB|

2)

. Qnmax and Qave are the peak

and average interference temperature levels respectively. Equation (3.8) ensures the

sum of the allocated power over subcarriers is less than Pmax; Equation (3.9) is the

maximum amount of interference temperature at n-th subcarrier is less than Qnmax,

where Qnmax = Pnmax/(kN Bc) and P

nmax is the maximum allocated transmit power over

n-th subcarrier; Equation (3.10) is the average interference level over all subcarriers

which is less than Qave.

3.2.1.1 Total-transmit and Peak-interference-powers

In this case, the sum capacity maximization problem is optimized subject to the

total transmit-power, given in Equation (3.8), and peak interference-power, given in

Eq. (3.9). The average interference level over all sub-carriers is relaxed. The solution

of this optimization problem can be written as [13].

29


39/49

Pnc =

2 + Pnp |gn|2

|hn|2

Qnmaxfn2

, (3.11)

where = B3Nln 2 , is the non-negative Lagrange multiplier. It is chosen such

that

Nn=0

Pnc () = min

Pmax,

Nn=0

Qnmax|fn|2

. (3.12)

3.2.1.2 Total-transmit and Average-interference-powers

In this case, the sum capacity maximization problem is optimized subject to the

total transmit-power, given in Equation (3.8), and average interference-power, given

in Eq. (3.10). The peak interference power condition is relaxed. The solution of this

optimization problem can be written as [13].

Pic =

W + |fn|2

2 + Pnp |gn|2

|hn|2+

. (3.13)

where and are non-negative Lagrange multipliers. Figure 3.3 shows power al-

location over different sub-carriers. Multi-level water filling algorithm is employed for

the power allocation on different sub-carriers based on the interference temperature

limit.

30


40/49

1

cP

3

cP

4

cP 5

cP

6

cP

n

cP

2

1

max

1

Q

f

2

2

max

2

Q

f2

3

max

3

Q

f2

4

max

4

Q

f2

5

max

5

Q

f

2

6

max

6

Q

f2

max

n

n

Q

f

2

1

1

2

1

2|

|

|

|P

P

g

h

+ 2

2

2

2

2

2|

|

|

|

P

P

g

h

+

2

3

3

2

3

2

|

|

|

|P

P

g

h

+

2

4

4

2

4

2|

|

|

|

P

P

g

h

+

2

5

5

2

5

2|

|

|

|

P

P

g

h

+

2

6

6

2

6

2

|

|

|

|P

P

g

h

+ 2

2

2|

|

|

|

n

n

P n

P

g

h

+

1 2 3 4 5 6 n

Power

Figure 3.3: Water-filling Power Allocation

31


41/49

CHAPTER IV

Simulation Results and Discussion

4.1 Simulation Parameters

This section includes the simulation results for the proposed NCCCN by means of

Monte-Carlo simulations. We consider all the wireless nodes use OFDM based trans-

mission, the channel fading model is 3-Rayleigh-multipath, the number of subcarriers,

i.e., N = 16, Qimax = Qmax and the distribution of noise power over all the subcarrier

is same. The simulation is run for 5000-times. Optimal power allocation (OPA) al-

gorithm follows multilevel water-filling whereas sub-optimal power allocation (SUB)

follows even power allocation. The power allocation of primary network can be either

OPA or SUB whereas the power allocation in a NCCCN can also be either OPA or

SUB.Thus the spectral efficiency of a SU with respect to P U depends on OPT/OPT;

SUB/OPT; OPT/SUB and SUB/SUB. The simulation parameters for both NCCCN

and CCN without NC are considered same for the fair comparison.

4.2 Discussion of the Results

Figure 4.1 shows the effect of Pmax on spectral efficiency for different constraint

of Imax. When no maximum-interference constraint is imposed, i. e., Imax = , the

spectral efficiency of SU is highest and follows logarithmic trend. On the other

32


42/49

hand, when the maximum-interference temperature constraint is considered, i.e.,

Imax = 1/N, the spectral efficiency of SU follows the logarithmic trend for smaller

value ofPmax but becomes flat for higher value ofPmax for all OPT/OPT; SUB/OPT;

OPT/SUB and SUB/SUB cases. The maximum-interference temperature constraint

imposes limit on maximum power allocation to avoid interference to the primary net-

work. The dash-sign curves represent the spectral efficiency of the proposed network

whereas the solid-sign curves represents the spectral efficiency of CCN without ANC.

The spectral efficiency [bit-per-second/Hz] of the proposed NCCCN is better than

that of CCN without ANC [14]. The ANC requires less radio resources compared

to traditional CCN to finish a bi-directional communication. It is also found that

the spectral efficiency of OPT/OPT is better than that of SUB/OPT; OPT/SUB;

SUB/SUB for the interference limited case.

Figure 4.2 shows the effect ofPmax on spectral efficiency for different constraint of

Iave. When no average-interference constraint is imposed, i. e., Iave = , the spectral

efficiency of SU is highest and follows logarithmic trend. On the other hand, when

the average-interference temperature constraint is considered, i.e., Iave = 1/N, the

spectral efficiency of SU follows the logarithmic trend for smaller value of Pmax but

becomes flat for higher value of Pmax for all OPT/OPT; SUB/OPT; OPT/SUB and

SUB/SUB cases. The average-interference temperature constraint imposes limit on

power allocation to avoid interference to the primary network. It is also found that

the spectral efficiency of OPT/OPT is better than that of SUB/OPT; OPT/SUB;

SUB/SUB for the interference limited case. The proposed NCCCN also performs

better than that of CCN without ANC [13] in this case.Figures 4.3 and 4.4 show the effect ofImax on spectral efficiency considering Pmax

and Pave constraints respectively. In terms of spectral efficiency, the proposed NCCCN

outperforms the CCN considered in [13].

33


43/49

0 0.5 1 1.5 2 2.5 3 3.5 4

0

0.5

1

1.5

2

2.5

Pmax

Spectralefficiency[bps/Hz]

NCCCN; OPT/OP T; Imax=inf

CCN; OPT/OPT; Imax=inf

NCCCN; OPT/OP T; Imax=1/N

CCN; OPT/OPT; Imax=1/N

NCCCN; SUB/OPT; Imax=1/N

CCN; SUB/OPT; Imax=1/N

NCCCN; OPT/SUB; Imax=1/N

CCN; OPT/SUB; Imax=1/N

NCCCN; SUB/SUB; Imax=1/N

CCN; SUB/SUB; Imax=1/N

Figure 4.1: Effect of Pmax on spectral efficiency considering Imax.

0 0.5 1 1.5 2 2.5 3 3.5 4

0

0.5

1

1.5

2

2.5

Pmax


NCCRN;OPT/OPT; Iave=inf

CRN;OPT/OP T; Iave=inf

NCCRN;OPT/OPT; Iave=1/NCRN;OPT/OPT; Iave=1/N

NCCRN;SUB/OPT; Iave=1/N

CRN;SUB/OPT; Iave=1/N

NCCRN;OPT/SUB; Iave=1/N

CRN;OPT/SUB; Iave=1/N

NCCRN;SUB/SUB; Iave=1/N

CRN;SUB/SUB; Iave=1/N

Figure 4.2: Effect of Pmax on spectral efficiency considering Iave.

34


44/49

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10

0.5

1

1.5

2

2.5

Imax

Spectralefficiency[bps/H

z]

NCCCN; OPT/OPT; Pmax=inf

CCN; OPT/OPT; Pmax=inf

NCCCN; OPT/OPT; Pmax=1/N

CCN; OPT/OPT; Pmax=1/N

NCCCN; SUB/OPT; Pmax=1/N

CCN; SUB/OPT; Pmax=1/N

NCCCN; OPT/SUB; Pmax=1/N

CCN; OPT/SUB; Pmax=1/N

NCCCN; SUB/SUB; Pmax=1/N

CCN; SUB/SUB; Pmax=1/N

Figure 4.3: Effect of Imax on spectral efficiency considering Pmax.

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1

0

0.5

1

1.5

2

2.5

3

3.5

Imax


NCCCN, OPT/OPT; Pave=inf

CCN, OPT/OPT; Pave=inf

NCCCN, OPT/OPT; Pave=1/NCCN, OPT/OPT; Pave=1/N

NCCCN, SUB/OPT; Pave=1/N

CCN, SUB/OPT; Pave=1/N

NCCCN,OPT/SUB; Pave=1/N

CCN,OPT/SUB; Pave=1/N

NCCCN, SUB/SUB; Pave=1/N

CCN, SUB/SUB; Pave=1/N

Figure 4.4: Effect of Imax on spectral efficiency considering Pave.

35


45/49

CHAPTER V

Conclusion and Future Work

5.1 Conclustion

CR can sense its environment and without the intervention of the user can adapt

to the users communication needs while conforming to FCC rules. Conceptually, the

amount of spectrum is infinite, practically for propagation and other reasons it is finite

because of the desirability of certain portions of the band. Even the spectrum which

is assigned is far from being 100% utilized, hence efficient use of the spectrum is a

growing concern. CR offers a solution to this problem. A CR can intelligently detectwhether any portion of the spectrum is in use or not, and can temporarily latch into

or out of it without interfering with the transmissions of other users thereby efficiently

utilizing spectrum. Some of the radios other cognitive abilities include determining

its location, sensing spectrum use by neighboring devices, changing frequency, ad-

justing output power or even altering transmission parameters and characteristics.

All of these capabilities, and others yet to be realized, will provide wireless spectrum

users with the ability to adapt to real-time spectrum conditions, offering regulators,

licenses and the general public flexible, efficient and comprehensive use of the spec-

trum. The phenomenal success of the unlicensed band in accommodating a range

of wireless devices and services has led the FCC to consider opening further bands

for unlicensed use. In contrast, the licensed bands are underutilized due to static

36


46/49

frequency allocation. Realizing that CR technology has the potential to exploit the

inefficiently utilized licensed bands without causing interference to incumbent users;

the FCC released the Notice of Proposed Rule Making to allow unlicensed radios to

operate in the TV broadcast bands.

In this thesis we showed that network coding techniques can be used for reli-

able and robust dissemination of control information among Cognitive Radios. This

control information allows CR to cooperate with each other in a timely manner, guar-

anteeing the stability of their communications and the integrity of the Primary Users

communications. We discussed neighbor discovery algorithms which exploit network

coding for fast and reliable control packet dissemination. We have proposed power

allocation for the NCCCN under peak interference constraints. The proposed system

performs better than the CCN without network coding. Simulation results show that

the higher data rate can be achieved using optimal power allocation.

5.2 Future Work

This work can be extended by considering the channel coding together with the

source coding and optimal power allocation based on outage probability analysis. We

will also analyze the complexity of the proposed algorithm.

37


47/49

REFERENCES

38


48/49

REFERENCES

(1990), Regulatory Framework for Radio Spectrum Policy in the European Commu-nity, EUROPA, http://europa.eu.

Akyildiz, I., W. Lee, M. Vuran, and S. Mohanty (2006), Next generation/dynamicspectrum access/cognitive radio wireless networks: A survey, Computer Networks,50(13), 21272159.

Cannons, J., R. Dougherty, C. F. Freiling, and K. Zeger (2006), Network routingcapacity, IEEE Transactions on Information Theory, 52(3), 777788.

Fragouli, C., J. Widmer, and J. Y. Le Boudec (2008), Efficient broadcasting usingnetwork coding, IEEE/ACM Transactions on Networking, 16(2), 450463.

Haykin, S. (2005), Cr: Brain-empowered wireless communications, IEEE Journal onSelected Areas In Communications, 23, 201220.

III, J. M. (2000), Cognitive radio an integrated agent architecture for software definedradio dissertation, Scientific American, 294 (3), 6673.

Kaiser, M. S., and K. M. Ahmed (2010), Radio resource allocation for heterogeneousservices in relay enhanced ofdma systems, JCM, 5(6), 447454.

Kaiser, M. S., K. M. Ahmed, and R. A. Shah (2010), Power allocation in ofdm-basedcognitive relay networks, in WCNIS, pp. 202206.

Li, D., X. Dai, and H. Zhang (2009), Game theoretic analysis of joint rate and powerallocation in cognitive radio networks, IJCNS, 2(1), 17.

Ma, Y., H. Zhang, D. Yuan, and H.-H. Chen (2009), Adaptive power allocation withquality-of-service guarantee in cognitive radio networks, Comput. Commun., 32,19751982, doi:10.1016/j.comcom.2009.06.012.

OFCOM (2009), Digital dividend: Cognitive access consultationon licence-exempting cognitive devices using interleaved spectrum,http://www.ofcom.org.uk/consult/condocs/cognitive/cognitive.pdf.

Yang, S., and J. Wu (2010), Efficient broadcasting using network coding and direc-tional antennas in manets, IEEE Trans. Parallel Distrib. Syst., 21 (2), 148161.

39


49/49

Yoon, M. K., K. H. Lee, and J. B. Song (2009), Performance analysis of distributedcooperative spectrum sensing for underlay cognitive radio, in Proceedings of the11th international conference on Advanced Communication Technology - Volume1, ICACT09, pp. 338343, IEEE Press, Piscataway, NJ, USA.

power allocation for the network

Documents