1. Liping WANG 1, Yusheng JI 1,2, and Fuqiang Liu 3 1 The Graduate University for Advanced Studies, Tokyo, Japan 2 National Institute of Informatics, Tokyo, Japan 3 Dept. of Information and Communication Engineering, Tongji University, Shanghai, China WCNC 2009

2. Outline  Introduction  System model  Joint Path Selection and Subchannel Allocation  A. Problem Formulation  B. Linearization  C. Joint Path Selection and Subchannel Allocation Algorithm  Performance Evaluation  Conclusions

3. Introduction  Shadow effect BS RS SS

4. Introduction BSSSRS Transparent RS Amplify Forward BSSSRS Non-transparent RS Decode Forward Encode

5. Introduction

6.  Motivation  In this paper, based on the two-zone frame structure proposed in [10], which supports both relaying and cooperative selection diversity (CSD), we find that the effective data rate of a path on a subchannel used in [5][10] as the path selection rule can only be achieved when the proportion of the two zones equals a optimal value

7. System model  Each downlink data subframe has a length of Td with the proportion of the DL-access zone and the optimal transparent zone equal to u : (1 − u)  Assumption  Channel states are invariant during one frame  in [10] that one subchannel can be allocated to only one user

8. A. Problem Formulation B. Linearization C. Joint Path Selection and Subchannel Allocation Algorithm Joint Path Selection and Subchannel Allocation

9. A. Problem Formulation  f( ・ ) is a nonlinear mapping function that depends on the type of constellation used [11] Achievable data rate Index of a user Index of subchannel Transmission power Channel gain Link

10. A. Problem Formulation BSSSRS BS RS Direct link First-hop link Second-hop link

11. A. Problem Formulation  If k receives data directly from the BS on the nth subchannel, its end-to-end achievable data rate on that subchannel is BSSSRS

12. A. Problem Formulation  if k receives data through the jth RS on n, its end-to-end achievable data rate on that subchannel is BSSSRS FrameFirstSecond BS → RSRS → SS 1/32/3 9bps 3bps 1/3*9=32/3*3=2 FirstSecond BS → RSRS → SS Free

13. A. Problem Formulation  optimal u that maximizes r j,k,n is equal to BSSSRS FirstSecond BS → RSRS → SS 1/43/4 9bps 3bps u = 3/(9+3) = 3/12 = 1/4 3/4 * 3 = 9/41/4 * 9 = 9/4

14. A. Problem Formulation  If the subchannel in the first zone or in the second zone cannot be fully occupied FirstSecond BS → RSRS → SS Free FirstSecond BS → RSRS → SS Free

15. A. Problem Formulation FirstSecond BS → RSRS → SS Free BSSSRS 9bps 3bps f j,k,n = 1/2 – 1/2 * 3/9 = 1/2 – 1/6 =2/6 = 1/3 1/21/31/6 1/6*9=3/21/2*3=3/2 > <

16. A. Problem Formulation  Void filling algorithm FirstSecond BS → RSRS → SS Free BSSSRS 9bps 3bps 1/21/31/6 1/6*9=3/21/2*3=3/2 BS → SS 1bps

17. A. Problem Formulation  In many literatures such as [5] and [10], they defined the effective data rate of a two-hop user as 1/(1/9 + 1/3) = 1/(4/9) = 9/4 FirstSecond BS → RSRS → SS 1/43/4 3/4 * 3 = 9/41/4 * 9 = 9/4 BSSSRS 9bps 3bps 1bps

18. A. Problem Formulation  it is impossible that every relaying path has the same optimal u on every subchannel

19. A. Problem Formulation  In such a multiuser OFDMA relay system with cooperative selection diversity (CSD), we get the throughput of the kth user as path selection and subchannel allocation indicator (0 or 1)

20. A. Problem Formulation Minimum number of subchannels required by k r j,k,n is a nonlinear function of p n

21. B. Linearization  When rate adaptation is adopted, the overall throughput can hardly be reduced even if power allocation is not adaptive [11]

22. B. Linearization  With fixed power allocation, BS precalculates all r j,k,n values, without the constraint c3, the optimal solution could be easily obtained with the following algorithm

23. B. Linearization  In this case, the complexity to make the optimal path selection and subchannel allocation is O((J+1)KN). However, if we take c3 into account, the problem has K + N constraints and becomes more complicated. A general linear integer programming has been proven to be NP-complete and can be solved by an exhaustive search, which has a computational complexity of O(((J +1)K) N ) [9]. Suppose there are 6 RSs, 20 users and 128 subchannels in the system, the element in the big O notation is about 10 274

24. C. Joint Path Selection and Subchannel Allocation Algorithm (J+1)K comparisons are needed O(KN log KN) O(3KN ) If we have 6 RSs, 20 users and 128 subchannels, the element in the big O notation equals 27910. Compared with 10 274, our algorithm reduce the computational complexity considerably.

25. Performance Evaluation  we assume a widely studied network topology with a BS located in the cell center and uniformly surrounded by certain number of RSs as in [3-6] and [10]  There are totally 128 subchannels

26. Performance Evaluation  6 RSs are in the cell

27. Performance Evaluation  u = 0.5

28. Performance Evaluation  6 RSs are in the cell

29. Performance Evaluation  u = 0.5

30. Performance Evaluation  10 RSs are in the cell

31. Conclusions  In this paper, we formulate the problem on resource allocation for OFDMA relay-enhanced systems.  a heuristic joint path selection and subchannel allocation algorithm is designed and a void filling algorithm is proposed to allocate the remaining resources to users’ direct links.  Simulation results show our path selection rule based on the end-to-end achievable data rate and the void filling algorithm improve the overall throughput especially when the proportion of the two zones is far from the optimal value

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