Cheng- Hsien Lin, Jeng-Farn Lee, Jia-Hui Wan

1. A Utility-based Mechanism for Broadcast Recipient Maximization in WiMAX Multi-level Relay Networks Cheng-Hsien Lin, Jeng-Farn Lee, Jia-Hui Wan Department of Computer Science and Information Engineering,National Chung Cheng University, Taiwan IEEE Transactions on Vehicular Technology (IEEE TVT 2012)

2. Outline • Introduction • Goal • Network Model and Assumption • Problem specification • Multi-Level Utility-based Resource Allocation (ML-URA) • Simulations • Conclusions

3. Introduction • The emergence of IEEE 802.16 WiMAX and advances in video coding technologies have made real-time applications possible. • The granted applications (e.g., real-time IPTV Broadcast) • Allocated limited time-slots (Resource Budget).

4. Problem • This paper studies the resource allocation problem • Broadcast receipt maximization in IEEE 802.16j • IEEE 802.16j • Multihop Relay Base Station(MR-BS) • multiple Relay Stations(RSs) • Mobile Stations(MSs) • Broadcast data is sent by the MR-BS to a set of receivers • How to allocate the given resource budget to maximize the number of MSsis a challenging issue.

5. Problem • The broadcast receipt maximization problem

6. Problem • The broadcast receipt maximization problem

7. Problem • The broadcast receipt maximization problem

8. Problem • The broadcast receipt maximization problem

9. Related works • Existing researches • heuristic resource allocation strategies • single-levelrelay networks (two-hop relay networks) • This paper models the resource allocation problem in IEEE 802.16j WiMAX multi-level relay networks (multi-hop) • Multi-Level Broadcast Receipt Maximization (ML-BRM) problem

10. Goal • To propose multi-level resource allocation mechanism • Consider the multi-level relay paths and the required resource • Maximize resource utilization in WiMAX multi-level relay networks

11. Network Model and Assumption • In a WiMAX relay network, • one MR-BS • Y RSs • N MSs that subscribe to a certain real-time program • This paper assumes that the real-time program, whose streaming data size is M • Resource budget: rbudget • total time slots in a TDD super frame RS0 Each RS y (1 ≤ y ≤ Y) is denoted by RSy Each MS n(1 ≤ n≤ N) is denoted by MSn

12. Network Model and Assumption • The number of time slots required to transmit a broadcast stream varies • MSs and RSs have different channel conditions • MSs and RSs have different modulation schemes • the transmission rates required for RSsto successfully send data also vary

13. Network Model and Assumption • The transmission rate bx,ybetween sender x and receiver y • based on one of the channel conditions, such as the SNR value • sender x: MR-BS or RS • receiver y: RS or MS • The resource required by the receiver y: M/bx,y

14. Network Model and Assumption • RAx: a node x with the allocated resource RAx • all nodes whose required resource is not larger than RAx can receive the downlink data successfully through one downlink transmission from node x. MS x RAx MS MS

15. Network Model and Assumption • For all RSs, the channel conditions are represented by where records the resource required by RSy to receive streaming data from other RSs. • RResy,y= 0: RSy doesn’t demand any resource from itself. RS0 RS2 RS4 RS8 RS5 RS3 RS1 RS6 RS7 ...

16. Network Model and Assumption • Similarly, the matrixportrays the resource requirement of all MSs, where records the resource that MSn requires to receive data from all RSs. MS1 MS2 RS0 RS2 RS4 RS8 RS5 RS3 RS1 RS6 RS7

17. Network Model and Assumption • Finally, the resource allocation vector is denoted by RA= [RA0, RA1, RA2, …, RAY], where RAy represents the amount of the resource allocated to RSy. MS1 MS2 RS0 RS2 RS4 RS8 RS5 RS3 RS1 RS6 RS7

18. Network Model and Assumption • U(): whether the MSn can receive data from RSy successfully. MS1 MS2  U(RA1-MRes1,1) = U(5-3) = 1  U(RA1-MRes2,1) = U(5-7) = 0 RA1 = 5 RS0 MRes2,1 = 7 RS1 MRes1,1 = 3

19. Network Model and Assumption • D(): whether RSyis eligible to receive real-time streamingdata from the MR-BS when the current resource allocation RA is given. • D0(RA) = 1:MR-BS is the source node of the real-time stream. RS0 RS1 RS3 RS2  D2(RA) = D2(5-3) = 1  D3(RA) = D3(5-7) = 0 RA1 = 5 RRes3,1 = 7 RRes2,1 = 3

20. Problem specification • We now define the Multi-Level Broadcast RecipientMaximization (ML-BRM) problem. • resource budget (rbudget) • channel conditions ofthe wireless relay network (RMS and RRS ) • ML-BRM searches for an allocation RA vector that will maximize the number of MSs receiving the real-time program. The ML-BRM problem is NP-complete

21. ML-URA • Multi-Level Utility-based Resource Allocation • Definition of Utility • ui,y:the number of additional MSs divided by the extra resource that the network mustallocate to the RSs on the relay path

22. ML-URA • Construct single-source shortest path tree that is rooted at the MR-BS and connects all RSs. (SPy) • ѱ(SPy) counts the number of RSs on SPy • Γ(SPy, k) obtains the ID of the kth RS on SPy, 1 ≤ k ≤ ѱ(SPy) SP6 Γ(SP6, 1) = 1 Γ(SP6, 2) = 6 ѱ(SP6) = 2 SP1 MR-BS RS2 RS4 RS8 RS5 RS3 RS1 RS6 RS7

23. ML-URA • To derive the utility of a relay path ui,y • count the number of additional MSs • calculate the amount of extra resource required check if MSj can be served by SPy MSj ……... RS0 ……... RSk RSk+1 RSy • Because of the broadcast natureof the wireless medium, MSj can receive data of the real-time program

24. ML-URA • To derive the utility of a relay path ui,y • count the number of additional MSs • calculate the amount of extra resource required RSy is allocated MResi,y to serve MSi check ifMSj can be served by RSy MSj MSi RSy • Because of the broadcast natureof the wireless medium, MSj can receive data of the real-time program

25. ML-URA • the union operation whether MSj has been served in previous rounds of the resource allocation process the additional number of MSs that can be served

26. ML-URA • To derive the utility of a relay path ui,y • count the number of additional MSs • calculate the amount of extra resource required MSi RS0 ……... ……... RSk RSk+1 RSy

27. ML-URA • To derive the utility of a relay path ui,y • count the number of additional MSs • calculate the amount of extra resource required MSi RSk Rsk+1

28. ML-URA • The expression of the utility of a relay path ui,y is defined as follows:

29. ML-URA • The ML-URA Mechanism • Greedy procedure • Find-Most-MS-Path procedure (ui,y) (number of MSs)

30. ML-URA_Greedyprocedure Greedy procedure stop conditions exists: (i) the entire resource budget has been allocated (ii) all MSs have been served.

31. ML-URA_Greedyprocedure • Resource-Recycle procedure

32. ML-URA_Greedyprocedure • Two distinct paths that have the same utility value 2/2 5/5

33. ML-URA_Find-Most-MS-Pathprocedure Find-Most-MS-Pathprocedure

34. ML-URA_Find-Most-MS-Pathprocedure

35. Simulations

36. Simulations => computes the optimal solution in a brute-force manner

37. Simulations

38. Simulations

39. Conclusions • The proposed ML-URA mechanism improve • Resource utilization • Performance

40. Thanks for your attention !!!