Ryerson University The School of Computer Science

1. Ryerson UniversityThe School of Computer Science CP8207: Selected Topics in Computational Intelligence & Computer Networks Professor: IssacWoungang A TWO-PHASE CHANNELAND POWER ALLOCATION SCHEME FOR COGNITIVE RADIO NETWORKS Presented by: RaedKarim RouzbehBehrouz SamEerLAlji October 30,2009

2. 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

3. Brief Overview • Cognitive Radio Networks (CRNs) • Power allocation in relation to distance • Channel allocation with respect to already assigned spectrums • Minimize interference and maximize CPEs serviced • Two-phase channel and power allocation

4. Introduction to the Problem

5. 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

6. Specific Problem of Focus • PHASE 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.

7. 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.

8. Considerations for our problem • System throughput –  number of active CPEs served simultaneously • Number of connected PUs • Total transmission power for given channels • Same 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 CPEs • Mock formula on the board for Transition Function

9. Solutions and Algorithms • Step 1: Select an action a(t)=ak according to the probability vector • Step 2: Receive the feedback bk(t) of action ak from the environment • Step 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

10. Visualization of the Result

11. Implementation The following steps will be taken to complete the implementation of the proposed solution: • A java program will be written implementing the improved algorithm • Program will be tested using the data set as mentioned in the paper • Graphs of the throughput of the system will be drawn using java chart API • The performance of original and improved algorithm will be compared

12. Possible Future Work • Focus on the power allocation of the proposed problem by considering more factors than just the distance

13. Q&A Any Questions? Thank you!