Capacity Enhancement with Relay Station Placement in Wireless Cooperative Networks

1. Capacity Enhancement with Relay Station Placement in Wireless Cooperative Networks Bin Lin1, Mehri Mehrjoo, Pin-Han Ho, Liang-Liang Xie and Xuemin (Sherman) Shen Department of Electrical and Computer Engineering,University of Waterloo,Canada IEEE WCNC 2009

2. Outline • Introduction • Goal • Problem Formulation • A GA-Based Heuristic Algorithm • Simulation • Conclusions

3. High rate RS Low rate MS Introduction • IEEE 802.16j Transparent Relay Station • Capacity Enhancement MR-BS Transparent Relay

4. relay(RS) source(BS) destination(SS) Introduction • Transparent Relay Station Placement • Exploiting the utmost performance benefits base on OFDMA Capacity Maximization Cooperative relaying on 3-node relay model using a different code

5. relay(RS) source(BS) destination(SS) SNR of “source-relay” path SNR of “source-destination” and “relay-destination”path r = r = Introduction [10] A. Host-Madsen and J. Zhang, “Capacity bounds and power allocation for wireless relay channels,” IEEE Trans. on Inf. Theory, vol. 51, no. 6, pp. 2020-2040, Jun. 2005. • The achievable rates for the destination node [10] Shannon function C(SNR) = Blog2(1+SNR) dsr drd drd Case2: Buffer in “source-relay” Case1: Waiting for “source-relay” αis the path loss exponent Θis the transmit power allocation ratio of the source node between the “source-relay” path and “source-destination” path Psis the transmit power of BS Pris the transmit power of RS

6. Introduction • Capacity Maximization RS Placement (CMRP) problem • TheCMRP problemis to maximize the system capacity • Given the locations and the minimum traffic demands of N SSs • The finite locations of M CPs for deploying RSs • Total bandwidth allocated to the cell • Transmit power of BS and RS

7. Goal • Based on 802.16j Relay Station • Cooperative relaying on 3-node relay model using a different code on OFDMA • Develop an optimization framework for the Capacity Maximization RS Placement (CMRP) problem • Using Genetic Algorithm based heuristic to solve CMRP problem

8. Problem Formulation • Network Model • One BS, multiple RSs, and fixed SSs • CPs is candidate positions to deploy RSs

9. CPm BS SSn =1 =0 Problem Formulation • Capacity Maximization RS Placement (CMRP) formulation rmn is the achievable rate for the destination node using cooperative relay N : The number of SSs M : The number of CPs : indicates the index of the chosen CP serving a specific SS : Bandwidth-allocation of SSn CPm SSn CPm SSn

10. CP1 CP2 CPm SSn =1 =0 Problem Formulation • Constraint for CMRP problem • (1) States that each entry in the location-allocation matrix is binary • (2) Ensures exclusively allocation of each SS to an RS(CP) CPm SSn CPm SSn : indicates the index of the chosen CP serving a specific SS : The set of SSs，|| = N. : The set of CPs, || = M. …

11. Problem Formulation • Constraint for CMRP problem • (3) Ensures the throughput of each SS is larger than its minimum traffic demand : The minimum traffic demand of SSn : The set of SSs，|| = N.

12. CP1 CP2 CP3 SS1 SS2 SS3 Problem Formulation • Constraint for CMRP problem • (4) Satisfies locating K RSs among the M CPs RS M=1 => max(0,1-2(2)) = 0 RS 1 = 3-2 M=2 => max(0,1-2(1)) = 0 M=3 => max(0,1-0) = 1 : indicates the index of the chosen CP serving a specific SS :The number of RSs to be deployed within the cell

13. Problem Formulation • Constraint for CMRP problem • (5) The bandwidth constraint of the cell : Bandwidth-allocation of SSn B: The total radio bandwidth allocated to the cell.

14. Problem Formulation • The achievable rate for the destination node Shannon function C(SNR) = Blog2(1+SNR) CMRP : The distance between node i and node j. : The transmit power of BS : The transmit power of RS : Path loss exponent : Source power allocation ratio between the relay path and direct transmission path,

15. Selection Replacement Crossover Mutation A G-A Based Heuristic Algorithm [12] D. E. Goldberg, Genetic algorithms in search, optimization, and machine learning. Reading, MA, Addison-Wesley, 1989. • CMRP problem is NP-hard individual natural selection better individuals (for the environment) population natural selection cycle :

16. fitness value : amn = 1 bnrepresent the associated CP to SSn rbn,nrepresents the achievable rateof SSnrelayed with the RS located at CPbn bn = m A G-A Based Heuristic Algorithm • Overview randomly select K among M CPs ﹛ CP1 , CP3 ,CP4 ﹜ If number of RSs is 3 CP5 CP5 BS BS CP1 CP1 CP2 CP2 SS1 SS1 CP3 CP4 CP3 CP4 SS3 SS3 SS2 SS2

17. Selection Replacement Crossover Mutation A G-A Based Heuristic Algorithm • Overview Larger fitness value : CP5 CP5 BS BS CP1 CP1 CP2 CP2 SS1 SS1 CP3 CP4 CP3 CP4 SS3 SS3 SS2 SS2 ﹛ CP1 , CP3 ,CP4 ﹜ If number of RSs is 3

18. Solve CMRP problem Selection Replacement Crossover Mutation A G-A Based Heuristic Algorithm • Overview CP5 CP5 BS BS CP1 CP1 CP2 CP2 SS1 SS1 CP3 CP4 CP3 CP4 SS3 SS3 SS2 SS2 ﹛ CP1 , CP3 ,CP4 ﹜ If number of RSs is 3

19. Selection Replacement Crossover Mutation • Overview CP5 CP5 BS BS CP1 CP1 CP2 CP2 SS1 SS1 CP3 CP4 CP3 CP4 SS3 SS3 SS2 SS2 Crossover ﹛ CP1 , CP3 ,CP4 ﹜ If number of RSs is 3 CP1 Check constraints : BS CP1 CP2 SS1 CP3 CP4 SS3 SS2

20. Selection Replacement Crossover Mutation • Overview CP5 BS CP1 CP2 SS1 CP3 CP4 SS3 SS2 Mutation If number of RSs is 3 Check constraints : CP5 BS CP1 CP2 SS1 CP3 CP4 SS3 SS2

21. A G-A Based Heuristic Algorithm Select Replacement Crossover Mutation (a) a predefined number of iterations NGare completed (b) the resultant capacity of the whole cell is close enough to the upper bound CUB (c) The improvement of capacity is negligible after a certain number of iterations NT

22. Improved GA for the CMRP Problem • Search space reduction • Fast convergence of bandwidth allocation

23. Improved GA for the CMRP Problem • Search space reduction • To exclude the CPs that can not provide a significant performance gain in terms of achievable rate. G = rrelay − r0 = 0.8bit/sec ,When PBS = 1W, PRS= 0.5W, α = 2

24. Improved GA for the CMRP Problem • Fast convergence of bandwidth allocation • Random allocation of both amn’s and ωn’s in each iteration of GA • Linear Programming (LP) problem can be solved to obtain the optimal values of ωnto maximize C

25. Simulation • Simulation parameters IEEE 802.16j parameter GA PARAMETER SETTING Number of individual mutation Iteration times crossover Error rate

26. Simulation • The cell layout, geographical traffic demand distribution and best CPs (RSs) for SSs.

27. Simulation • Convergence comparison of GA and improved GA

28. Simulation • Capacity vs. number of RSs given the same GA termination

29. Simulation • Achievable rate comparison for each SS in case of with best CPs, 6 RSs and direct transmission. (6RSs)

30. Conclusions • In this paper, we have explored the joint RS placement and bandwidth allocation problem in wireless cooperative relay networks. • Develop an optimization framework for the Capacity Maximization RS Placement (CMRP) problem • And using Genetic Algorithm based heuristic to solve CMRP problem