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

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

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

Introduction 2008/10/2 4 Communication Signal Processing Lab

Introduction 2008/10/2 5 Communication Signal Processing Lab

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Conclusion 2008/10/2 36 Communication Signal Processing Lab

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

Reference Z. Wei and K. B. Letaief, MIMO Broadcast Scheduling with Limited Feedback, IEEE J. Select. Areas Commun., vol. 25, pp , Sep 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 /10/2 38 Communication Signal Processing Lab