1. Multicast Recipient Maximization in IEEE 802.16j WiMAX Relay Networks Wen-Hsing Kuo † ( 郭文興 ) & Jeng-Farn Lee ‡ ( 李正帆 ) † Department of Electrical Engineering, Yuan Ze University, Taiwan ‡ Department of CSIE, National Chung Cheng University, Taiwan IEEE Transactions on Vehicular Technology, vol. 59, no. 1, Jan. 2010

2. Outline Introduction Problem & Goal System model Challenge Proposed Resource –Greedy Approach (GD) –Dynamic Station Selection (DSS) Performance Conclusion

3. Introduction WiMAX 802.16 networks –better coverage –higher throughput Wireless resources available for each wireless service is inevitably limited. As the capacity of wireless devices improves, the multicast applications, including Video conferencing, have been developed.

4. Introduction Resource-management policy –limit the maximum time slots of a single multicast, e.g., 10% of a TDD super-frame –to maintain the quality of different services With the given resource budget, a BS should serve as many recipients, i.e., SSs, as possible –to maximize user satisfaction –to maximize resource utilization

5. Problem & Goal How to address the multicast recipient maximization (MRM) problem in the WiMAX 802.16j network ? To propose a resource-allocation scheme for multicast service in downlink transmission –To maximize the total number of recipients –with the given budget (maximal usable resource) To the best of authors’ knowledge, this is the first work to study the problem.

6. System model Resource can be distributed to different transmissions –time slots This budget is to be distributed among the BS and RSs –since they are in the same interference range –only one of them can transmit at the same time Routing of each SS is assumed to be decided beforehand –SS accesses the BS either directly or through an RS –it is impractical that the whole multicast tree can dynamically be formed and adjusted as the channel condition of any recipient changes.

7. System model M RSs & N SSs Let not only the SSs but also RSs directly served by the BS be classified as group 0. The SSs that receive data via the mth RS be placed in group m, where m > 0. Group 0 Group 1 Group 2 Group m

8. System model N m : number of nodes in group m S (m,n) : the nth node in group m r (m,n) : the resource requirement of S (m,n) Since SSs have different bit error rates due to heterogeneous channel conditions, they may require different amounts of resource for receiving the same data from the BS. Group 2

9. System model i m : RS’s order in group 0 –RS m = S (0,im) = S (m,0) r (0,im) =0 = RS’s resource requirement Group 2 Group 0 RS 2 = S (0,2) = S (2,0)

10. System model Nodes in each group are placed in increasing order of r (m,n), i.e., r (m,1) ≤ r (m,2) ≤ · · · ≤ r (m, Nm)

11. System model Δr m (n) represents the additionally required resource of S (m,n) when the last node S (m, n−1) is served. –Δr m (n) = r (m,n) – r (m, n – 1)

12. System model Binary function D m (n) –RS m can receive from the BS when n nodes are served in group 0. D m (n) is equal to 1 if i m ≤ n and 0 otherwise. U m (n) is the number of served SSs when serving S (m,n), starting from the BS.

13. Challenge MRM Problem is NP-Complete The goal of MRM is to maximize the total number of served SSs; however, the total resource consumed by the RSs and the BS should not be greater than r budget. Likes the integral knapsack problem (NP-hard) –(1) Object’s price and its weight, (2) the weight limitation –(1) Group’s nodal amount and the resource requirements, (2) the budget limitation

14. Challenge MRM Problem is NP-Complete MRM is also NP, because the a solution (i.e., {n 0, n 1,..., n M }) can be validated by calculating MRM problem is NP-hard and NP, so that MRM problem is NP-Complete

15. Proposed Algorithm Greedy approach (GD) –u m (n): allocation utility of including S (m,n) U m (n), ( u m (n) )

16. Proposed Algorithm Greedy approach (GD) –u m (n): allocation utility of including S (m,n) U m (n), ( u m (n) )

17. Proposed Algorithm Greedy approach (GD) –u m (n): allocation utility of including S (m,n) U m (n), ( u m (n) )

18. Proposed Algorithm Greedy approach (GD) –u m (n): allocation utility of including S (m,n) If r budget = 2

19. Proposed Algorithm Dynamic Station Selection (DSS) –U m * (n) : the envelope function of U m (n) –u m * (n) : the optimal allocation utility of including S (m,n) –U 0 * (0) = U 0 (0) = 0

20. Proposed Algorithm Dynamic Station Selection (DSS) U m * (n), ( u m * (n) ) If r budget = 2

21. Performance BS at (0,0), RS uniformly distributed, SS random deployed Required resource for each node = (1/d a ) –d : distance between sender and receiver –a : channel attenuation factor, 2  a  4

22. Simulation I DSS: Dynamic station selection OP: Optimal solution GD: Greedy algorithm a = 2a = 3 5 RSs and 100 SSs

23. Simulation II DSS: Dynamic station selection OP: Optimal solution GD: Greedy algorithm Resource budget = 20000 a = 2

24. Conclusion & Future Work This paper have considered a resource-allocation problem called MRM for multicast over WiMAX relay networks. It proposes a dynamic station selection (DSS) to solve the problem based on the proposed envelope function. The future research can be extended in (1) relay networks with more than two hops (2) the distributed approach to solve MRM problem TheEND Thanks for your attention !