1. MIMO Broadcast Scheduling with Limited Feedback Student: (96325501) Director: 2008/10/2 1 Communication Signal Processing Lab

2. Outline Introduction System model MIMO broadcast scheduling algorithms – MIMO Broadcast Scheduling with SINR Feedback – MIMO Broadcast Scheduling with Selected Feedback – MIMO Broadcast Scheduling with Quantized Feedback Conclusion 2008/10/2 2 Communication Signal Processing Lab

3. Introduction Multiuser diversity – Channel-aware scheduling – System capacity – The PDF of 2008/10/2 3 Communication Signal Processing Lab

4. Introduction 2008/10/2 4 Communication Signal Processing Lab

5. Introduction 2008/10/2 5 Communication Signal Processing Lab

6. System model BS (M antennas) allocates independent information streams from all M Tx antennas to the M most favorable user (N antennas) with the highest SINR. Downlink of a single-cell wireless system – Tx: M antennas, Rx: N antennas ( ) – A total of K users ( ) Only J out of K users are allowed to communicate with BS simultaneously. ( ) 2008/10/2 6 Communication Signal Processing Lab

7. System model The SINR-based scheduling algorithm requires the feedback of KN SINR values and the feedback load increases with the increase of the number of receiver antennas 2008/10/2 7 Communication Signal Processing Lab

8. MIMO Broadcast Scheduling with SINR Feedback 2008/10/2 8 Communication Signal Processing Lab

9. MIMO Broadcast Scheduling with SINR Feedback This algorithm only requires a feedback of total K SINR values. Scheduling Algorithm 2008/10/2 9 Communication Signal Processing Lab

10. MIMO Broadcast Scheduling with SINR Feedback 2008/10/2 10 Communication Signal Processing Lab

11. MIMO Broadcast Scheduling with SINR Feedback Throughput analysis 2008/10/2 11 Communication Signal Processing Lab

12. MIMO Broadcast Scheduling with SINR Feedback 2008/10/2 12 Communication Signal Processing Lab

13. MIMO Broadcast Scheduling with SINR Feedback 2008/10/2 13 Communication Signal Processing Lab

14. MIMO Broadcast Scheduling with SINR Feedback 2008/10/2 14 Communication Signal Processing Lab

15. MIMO Broadcast Scheduling with Selected Feedback Scheduling Algorithm 2008/10/2 15 Communication Signal Processing Lab

16. MIMO Broadcast Scheduling with Selected Feedback 2008/10/2 16 Communication Signal Processing Lab

17. MIMO Broadcast Scheduling with Selected Feedback Throughput analysis – It can be observed that when λ 0, (22) is equivalent to (16) 2008/10/2 17 Communication Signal Processing Lab

18. MIMO Broadcast Scheduling with Selected Feedback 2008/10/2 18 Communication Signal Processing Lab

19. MIMO Broadcast Scheduling with Selected Feedback Feedback load analysis – Assume that l users are selected for feedback in one time slot ( l users satisfying ) – F B (t) is the CDF of B k – The probability of l – Average feedback load of the selected scheduling 2008/10/2 19 Communication Signal Processing Lab

20. MIMO Broadcast Scheduling with SINR Feedback Average feedback ratio (FLR) ζ – FLR is not dependent on the number of user K – When the threshold (λ) is increased, FLR (ζ) decreases. 2008/10/2 20 Communication Signal Processing Lab

21. MIMO Broadcast Scheduling with SINR Feedback 2008/10/2 21 Communication Signal Processing Lab

22. MIMO Broadcast Scheduling with SINR Feedback Throughput-FLR tradeoff – The throughput and FLR both depend on the threshold λ and decrease when λ increase. – Throughput-oriented: the scheme is to minimize FLR while guaranteeing a target throughput. – FLR-oriented: the scheme is to maximize the throughput while attaining a target FLR. – FLR can be greatly reduced without sacrificing the throughput. 2008/10/2 22 Communication Signal Processing Lab

23. MIMO Broadcast Scheduling with SINR Feedback (1) Target throughput =6.3 bps (2) λ=10 dB(2) λ=5 dB (3) Throughput =7.7 bps 2008/10/2 23 Communication Signal Processing Lab

24. MIMO Broadcast Scheduling with SINR Feedback (3) FLR=0.05 (2) λ=10 dB (2) λ=5 dB (1) Target FLR=0.4 2008/10/2 24 Communication Signal Processing Lab

25. MIMO Broadcast Scheduling with SINR Feedback 2008/10/2 25 Communication Signal Processing Lab

26. MIMO Broadcast Scheduling with Quantized Feedback Scheduling algorithm 2008/10/2 26 Communication Signal Processing Lab

27. MIMO Broadcast Scheduling with Quantized Feedback 2008/10/2 27 Communication Signal Processing Lab

28. MIMO Broadcast Scheduling with Quantized Feedback Quantization – The full feedback scheduling where each user feeds a real value B k to BS. – The quantized feedback scheduling requires each user to send back a quantized value Q(B k ) – The number of levels L is determined by the number of bits required to represent a value B k and L=2 b 2008/10/2 28 Communication Signal Processing Lab

29. MIMO Broadcast Scheduling with Quantized Feedback Throughput analysis 2008/10/2 29 Communication Signal Processing Lab

30. MIMO Broadcast Scheduling with Quantized Feedback – CDF of V When – PDF of V 2008/10/2 30 Cmmunication Signal Processing Lab

31. MIMO Broadcast Scheduling with Quantized Feedback 1-bit feedback – Each user feeds 1 or 0 back to the BS according to the threshold λ 1. If the quantization threshold λ 1 is fixed, the total rate will be a constant. 2008/10/2 31 Communication Signal Processing Lab

32. MIMO Broadcast Scheduling with Quantized Feedback 2008/10/2 32 Communication Signal Processing Lab

33. MIMO Broadcast Scheduling with Quantized Feedback Optimal threshold λ 1 – The throughput is a function of λ 1 and K, simply denote by E(R) = f(K, λ 1 ). – It is not optimal to fix λ 1 for various K to enhance the throughout. – To search for the optimal quantization threshold, we need to solve which is not tractable. – The optimal threshold should be dependent on K for given M, N and SNR 2008/10/2 33 Communication Signal Processing Lab

34. MIMO Broadcast Scheduling with Quantized Feedback 2008/10/2 34 Communication Signal Processing Lab

35. MIMO Broadcast Scheduling with Quantized Feedback 2008/10/2 35 Communication Signal Processing Lab

36. Conclusion 2008/10/2 36 Communication Signal Processing Lab

37. Conclusion Combined with spatial multiplexing and receive antenna selection, the proposed scheduling algorithm can achieve high multiuser diversity The feedback load can be greatly reduced with a negligible throughput loss with user selection based on SINR 2008/10/2 37 Communication Signal Processing Lab

38. Reference Z. Wei and K. B. Letaief, MIMO Broadcast Scheduling with Limited Feedback, IEEE J. Select. Areas Commun., vol. 25, pp. 1457-1467, Sep. 2007. D. Gesbert and M. Alouini, How much feedback is multi-user diversity really worth?, in Proc. IEEE ICC2004, Int. Conf. Commun., June 20-24, 2004, vol 1, pp.234-238. 2008/10/2 38 Communication Signal Processing Lab