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Ryerson University The School of Computer Science. CP8207: Selected Topics in Computational Intelligence & Computer Networks Professor: Issac Woungang A TWO-PHA SE CHANNE L A ND POWER ALLOCATION SCHEME FOR COGNITIVE RADIO NETWORKS Presented by: Raed Karim - PowerPoint PPT Presentation
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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!