DCA in CRN by Nazmi Aziz

7/28/2019 DCA in CRN by Nazmi Aziz

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Muhammad Nazmi Multimedia University July 2013

1.1 Introduction

//PROBLEM

With the rapid growth of smartphone, laptop, and tablet users, wireless networks all around the

world became congested and highly demanded. The impact of this crisis is much broader as mostof the applications used by users required internet connection [1]. This explosion of wireless

applications creates such a demand for more radio spectrum. However, most easily usable

spectrum bands have been allocated, although studies by [2] [3] shown that these bands are

significantly underutilized. These considerations have motivated the search for breakthrough

radio technologies that can scale to meet future demands both in terms of spectrum efficiency

and application performance.

//INTRO TO COGNITIVE RADIO

Imagine a four-lane highway that already assigned the usage of the each lane. The first one is for

ambulance, second lane is for police, third lane is for normal car and last lane is for heavy

vehicle. The lane for normal car will be congested while the other three lanes will run smooth.

Its because, the amount of normal car is much higher as compared to the other three types of

vehicles.

This scenario is happening today in our wireless network where its already congested. Cognitive

Radio Network is one of the options to encounter this problem. CRN is network with a cognitive

(plan, decide and reasoning) process [4]. It can perceive current network conditions, plan, decide,

act on those conditions, learn from the consequences of its actions, all while following end-to-

end goals.

//INTRO TO CHANNEL ASSIGNMENT

With the solution of this crisis has been proposed, the spectrum available need to be distributed

equally. The available spectrum will be divided into set disjointed channels that can be used

simultaneously [5]. The available channel then will be assigned based on the schemes used with

minimum interference and using maximum system capacity.


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There are three types of channel assignment schemes which are Fixed Channel Assignment

(FCA), Dynamic Channel Assignment (DCA) and Hybrid Channel Assignment (HCA) which

combining the first two schemes. For FCA, channels are pre-allocated to the cells during

planning phase. As an example, when a call attempt in mobile network can only be served if

unused channel in that particular cell is available. On the other hand, DCA will not pre-allocate

the channels available to the cell. It will dynamically choose any free cell that is available. For

example, when a call attempt at the cell, the available channel is selected based on the algorithm

which will choose the minimum interference and maximum capacity.

Comparison between FCA and DCA

As stated in the table above [6], DCA is more suitable to be the channel assignment scheme for

Cognitive Radio Network. Its because of its high flexibility of channel assignment is really

important to integrate with CRN which needed to operate dynamically so that it can maximize

the use of available channels [7].

Furthermore, the stability of DCA is higher as compare to FCA. Stability of channel assignment

is crucial. Its because when the stability is low, the interference will occur which will reduce the

efficiency of CRN [8].

FCA DCA

Performs better underheavy traffic

Low flexibility in channelassignment

Maximum channelreusability

Sensitive to time and spatialchanges

Not stable grade of serviceper cell in an interference

cell group

High forced calltermination probability

Suitable for large cellenvironment

Low flexibility

Performs better underlight/moderate traffic

Flexible channel allocation Not always maximum channel

reusability Insensitive to time and time

spatial changes

Stable grade of service per cellin an interference cell group

Low to moderate forced calltermination probability

Suitable in microcellularenvironment

High flexibility


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1.2 Objective

The main objective of this project is to propose a new or improved Dynamic Channel

Assignment for Cognitive Radio Network with minimum interference, high capacity and high

efficiency. To be more specific, the aims of this project are shown in the following:

To get the high efficiency of the channel assignment, the interference between cells mustbe at the minimum point and the capacity must be at the maximum point.

To get the minimum interference between cells, the channel assignment scheme mustobey the constraint there must be no interruption happen during the assignment of the

channel.

To get the maximum capacity, maximum packet delivery ratio must be achieve byremoving overall overhead message.

1.3 Problem Statement

As the spectrum becomes congested day by day, researchers come out with the idea to use the

idle or available license channel to secondary user. The crucial part is to assign the available

channels to the priority user.

Many approaches had been made by the researchers and most of them were using Dynamic

Channel Assignment [9]. Its because of the high flexibility of the scheme which will be suitable

to integrate with Cognitive Radio Network

Most considerations of newly created algorithm for this scheme had been proposed but, very less

of them consider to analyzed the power usage when the hand-over took place in their proposed

algorithms [10] [11].

If the usage of power during this process can be save, the telecommunication provider can save a

lot of money which results the end user can pay less for the service provided.

//ENOUGH? OR NEED MORE DETAILS?


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1.4 Scope

HAVENT DECIDED YET WHETHER TO ANALYZED ONE SPECIFIC PROPOSED

ALGORITHM OR CREATE A NEW ONE?

1.5 Gantt Chart


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2.0 Literature Review

In this part, we will further explain the theoretical background and literature review on Cognitive

Radio Network and Dynamic Channel Assignment. The approaches of the Dynamic Channel

Assignment schemes made by other researches will be further discussed and emphasized.

2.1 Spectrum-Aware Dynamic Channel Assignment (SA-DCA) in CRN

In this approach, Cognitive Radio (CR) nodes will check for Primary User (PU) activity on all

channels. It will determine the value for maximum connectivity and minimum interference of CR

nodes based on the calculated channel quality. To avoid interference between two CR nodes, SA-

DCA only considering the assigned channels of two-hop neighbors [12].

The quality of the channel will be calculated based on the equation below;

where: H1(i,n): No. of 1-hop neighbors of node n at channel i

H2(i,n): No. of 2-hop neighbors of node n at channel i

During initialization, when no neighbor of a node is available, the channel quality (CQ) for all

channels will be equal to 1. It indicates that the channel can be assigned when CQ is equal to 1.

So, the channel is unoccupied by PU when CQ is equal to 1.

//still need time and understanding to explain the process based on the figure above.


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2.2 Particle Swarm Optimization Algorithm

Particle Swarm Optimization (PSO) algorithm was introduced by Kennedy and Eberhart in 1995

which used to investigate the behavior of some social animals like collection of bees and flock of

birds [13]. The main objective of this approach is to maximize the channel assignment for CR

with minimum interference with PU.

To do this, PSO will gather the channels information and divide them to the based on the best

fitness value of the channel.

//flow chart explanation about 4 to 5 lines.

//System Structure explanation about 2 paragraph


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2.3 DCA Based on the Game Learning

2.4 DCA in CRN with Channel Heterogeneity

2.5 Auction-Based Spectrum Management of CRN

2.6 Hybrid Genetic Algorithm and Simulated Annealing

2.7 Game Theoretical Approach

2.8 DCA based on Stratification and Simulated Annealing Method

2.9 CRN using Fuzzy Logic System

2.10 Throughput Maximization in CRN

2.11 ZAP Approach

2.12 Spectrum Oppurtunity-Based Control Channel Assignment in CRN


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Dynamic

ChannelAllocation Based

on the Game

Learning in

Cognitive RadioNetworks

Introduce EWA

(Experience-Weight

Attraction) game

learning model, and

propose a channel

selection learning

algorithm based on

the channel priority

and the interference

between cognitive

radio users

Increase thethroughput ofsystem and have a

better equity in the

resource allocation

MATLAB EWA can

allocate channelsmore reasonably

and has a better

equity in the

resourceallocation

Solving Channel

Allocation

Problem in a

Cognitive RadioNetwork Using

Particle Swarm

OptimizationAlgorithm

Channel

allocation using

Particle Swarm

Optimization(PSO) algorithm

Maximize the

allocation of

channels of

active unlicensedusers

MATLAB PSO provide

channel for

active unlicensed

user without anyinterference with

primary user

Auction-Based

Spectrum

Management of

Cognitive RadioNetworks

Assign available

Primary User

Channel to the

higher bidder ofSecondary User

with constraint of

interference andconnectivity

Service Provider

can earn more

profit by using

this approach

MATLAB By auction-based

approach,

resource

available can bemanage

efficiently with

win-win situationbetween PU and

SU

Dynamic

ChannelAllocation using

Hybrid Genetic

Algorithm and

SimulatedAnnealing

(HGASA)

HGASA search

for optimumsolution to

distribute the

resources

(availablechannels) and

minimize the call

blocking

Minimize the

probability ofcall-dropping

which later

increase the

network capacity

MATLAB HGASA gather

all theinformation of

the available

channel and

select theoptimum

solution for SU

Analysis ofDecision Making

Operation in

Cognitive Radio

using FuzzyLogic System

Using FuzzyLogic System to

assigned

available channel

to SU

Maximize theutilization of the

available channel

(SU resource)

MATLAB By using FuzzyLogic System,

high Quality of

Service (Qos)

can be achieve


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Channel

Assignment forThroughput

Maximization in

Cognitive Radio

Networks

Introduce

overlapping andnon-overlaping

channel

assignment

algorithm

Maximize

throughput byassuming there is

no sensing error

MATLAB When these two

algorithms runtogether,

maximum

throughput can

be achieve

DCA in CRN by Nazmi Aziz

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