Ryerson UniversityThe School of Computer Science

CP8207: Selected Topics in Computational Intelligence & Computer Networks

Professor: Issac Woungang

A TWO-PHASE CHANNEL AND POWER ALLOCATION SCHEME FOR COGNITIVE RADIO NETWORKS

Presented by:

Raed Karim Rouzbeh Behrouz

SamEer LAlji October 30,2009

Agenda• Brief Overview of Cognitive Networks• Introduction to the problem• Specific Problem of Focus• How does LA apply (SELA)• Considerations for our problem• Solutions and Algorithms• Implementation• Visualization of the Result• Implementation• Possible Future Work• Questions and Answers

Brief Overview

Cognitive Radio Networks (CRNs)Power allocation in relation to distanceChannel allocation with respect to already

assigned spectrumsMinimize interference and maximize CPEs

servicedTwo-phase channel and power allocation

Introduction to the Problem

Introduction to the Problem (cont)PHASE I – Global Allocation

Sort the base stations in order of the maximum channel gain by base station to any primary user( ) where is channel gain from base station b to primary user p.

Select the CPE’s. where is the

set of CPE’s.

Transmit Power Based on , determine N x K

coverage matrix C. C(i,c) = 1

Specific Problem of FocusPHASE II – Local Allocation

Determine All active CPE’s. Form a bipartite graph that represents the

coverage of the cell.

Use Berge’s algorithm to find maximum disjoint edges in the resulting bipartite graph.

How Does LA Apply (SELA)An LA is a finite-state machine that interacts with

a stochastic environment, trying to learn the optimal action the environment offers through a learning process

At any iteration the automaton chooses an action, according to a probability vector, using an output function. This function triggers the environment, which responds with an answer (reward or penalty)

The automaton takes into account this answer and jumps, if necessary, to a new state using a transition function.

Considerations for our problemSystem throughput –  number of active CPEs

served simultaneously Number of connected PUs Total transmission power for given channelsSame channels serving different CPEs SINR- total interference does not exceed the

predefined threshold, for each PU Distance to the PUs (power consideration) Active and Idle CPEsMock formula on the board for Transition

Function

Solutions and AlgorithmsStep 1: Select an action a(t)=ak according to the probability

vector Step 2: Receive the feedback bk(t) of action ak from the

environmentStep 3: Compute the new True Estimate dk(t) of the selected

action ak according to new consideration parameters

Step 4: Update the Oldness Vector by setting mk(t)=0 and mi(t)=mi(t−1)+1 i≠k

Step 5: Compute the new Stochastic Estimate ui(t) i

Step 6: Select the action am that has the highest Stochastic Estimate um(t)=max{ui(t)}

Step 7: Update the probability vector using considerations above again

Visualization of the Result

ImplementationThe following steps will be taken to complete

the implementation of the proposed solution:A java program will be written implementing

the improved algorithmProgram will be tested using the data set as

mentioned in the paperGraphs of the throughput of the system will

be drawn using java chart APIThe performance of original and improved

algorithm will be compared

Possible Future WorkFocus on the power allocation of the

proposed problem by considering more factors than just the distance

Q&A

Any Questions?

Thank you!