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MIMO Broadcast Scheduling with Limited Feedback

MIMO Broadcast Scheduling with Limited Feedback. Student: 林鼎雄 (96325501) Director: 李彥文. Outline. Introduction System model MIMO broadcast scheduling algorithms MIMO Broadcast Scheduling with SINR Feedback MIMO Broadcast Scheduling with Selected Feedback

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MIMO Broadcast Scheduling with Limited Feedback

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  1. MIMO Broadcast Scheduling with Limited Feedback Student: 林鼎雄 (96325501) Director: 李彥文 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 Communication Signal Processing Lab

  3. Introduction • Multiuser diversity • Channel-aware scheduling • System capacity • The PDF of Communication Signal Processing Lab

  4. Introduction Communication Signal Processing Lab

  5. Introduction Communication Signal Processing Lab

  6. System model • BS (Mantennas) allocates independent information streams from all M Tx antennas to the M most favorable user (Nantennas) 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. ( ) 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 Communication Signal Processing Lab

  8. MIMO Broadcast Scheduling with SINR Feedback Communication Signal Processing Lab

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

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

  11. MIMO Broadcast Scheduling with SINR Feedback • Throughput analysis Communication Signal Processing Lab

  12. MIMO Broadcast Scheduling with SINR Feedback Communication Signal Processing Lab

  13. MIMO Broadcast Scheduling with SINR Feedback Communication Signal Processing Lab

  14. MIMO Broadcast Scheduling with SINR Feedback Communication Signal Processing Lab

  15. MIMO Broadcast Scheduling with Selected Feedback • Scheduling Algorithm Communication Signal Processing Lab

  16. MIMO Broadcast Scheduling with Selected Feedback 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) Communication Signal Processing Lab

  18. MIMO Broadcast Scheduling with Selected Feedback 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 ) • FB(t)is the CDF of Bk • The probability of l • Average feedback load of the selected scheduling 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. Communication Signal Processing Lab

  21. MIMO Broadcast Scheduling with SINR Feedback 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. Communication Signal Processing Lab

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

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

  25. MIMO Broadcast Scheduling with SINR Feedback Communication Signal Processing Lab

  26. MIMO Broadcast Scheduling with Quantized Feedback • Scheduling algorithm Communication Signal Processing Lab

  27. MIMO Broadcast Scheduling with Quantized Feedback Communication Signal Processing Lab

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

  29. MIMO Broadcast Scheduling with Quantized Feedback • Throughput analysis Communication Signal Processing Lab

  30. MIMO Broadcast Scheduling with Quantized Feedback • CDF of V • When • When • PDF of V 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 λ1. • If the quantization threshold λ1 is fixed, the total rate will be a constant. Communication Signal Processing Lab

  32. MIMO Broadcast Scheduling with Quantized Feedback Communication Signal Processing Lab

  33. MIMO Broadcast Scheduling with Quantized Feedback • Optimal threshold λ1 • The throughput is a function of λ1 and K, simply denote by E(R) = f(K, λ1 ). • It is not optimal to fix λ1 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 Communication Signal Processing Lab

  34. MIMO Broadcast Scheduling with Quantized Feedback Communication Signal Processing Lab

  35. MIMO Broadcast Scheduling with Quantized Feedback Communication Signal Processing Lab

  36. Conclusion 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 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. Communication Signal Processing Lab

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