Quantized Precoding with Feedback 11n Partial Proposal

1. Quantized Precoding with Feedback11n Partial Proposal Robert W. Heath Jr., Johann Chiang, Bishwarup Mondal and Roopsha Samanta The University of Texas at Austin Department of Electrical and Computer Engineering Wireless Networking and Communications Group 1 University Station C0803, Austin, TX 78712-0240 Phone: +1-512-425-1305 Fax: +1-512-471-6512 e-Mail: {rheath, jchiang, mondal, roopsha}@ece.utexas.edu The University of Texas at Austin

2. Outline • Main features • Closed-loop MIMO with limited feedback • Quantized precoding • Subcarrier clustering • Multi-mode precoding • MAC extension • Simulation results • Conclusion The University of Texas at Austin

3. Main Features • Closed-loop MIMO-OFDM with limited feedback (LF) • Robust optional mode when RTS/CTS is on • Proposed PHY features • Quantized precoding with feedback • TX Beamforming (BF) • Spatial multiplexing (SM) • Multi-mode adaptation • Proposed MAC features • Extended RTS/CTS frames for feedback • Backward compatible with 802.11a The University of Texas at Austin

4. Proposal Overview • Please refer to IEEE 802.11-04/962r1 The University of Texas at Austin

5. Feedback Structure • No physical feedback channels available in 802.11 • Exploit control frames in existing or emerging standards as logical feedback channels • Propose extension to existing 802.11 MAC • Use RTS for estimation and CTS for feedback The University of Texas at Austin

6. When and Why Use RTS/CTS? • Efficient when # of active STAs is large • Reduce collision overhead in hotspots • Useful in DCF (no AP, peer-to-peer) • Alleviate hidden terminals issue • Low overhead when frame size is large • Benefit from frame aggregation • Required for backward compatibility • Should maximize the worth of legacy mechanism • Capable of Improving MAC throughput • Use dynamic RTS/CTS threshold (IEEE 802.11-04/312r0) The University of Texas at Austin

7. MIMO without CSI at TX • Space-time block code (STBC) • Easily obtained spatial diversity • No array gain • Inflexible code design • Spatial multiplexing • Sensitive to channel invertibility • Limited spatial diversity • Hybrid mode (e.g. DSTTD) • Extra transmit antennas • No array gain The University of Texas at Austin

8. MIMO with CSI at TX • Transmit beamforming • Significant spatial diversity • Additional array gain • Flexible implementation w/ various antenna configurations • Precoded spatial multiplexing • Improved channel invertibility w/ extra transmit antennas • Additional spatial diversity • Additional array gain • Multi-mode precoding • Adaptation between precoded BF and SM modes The University of Texas at Austin

9. How to Get CSI at TX? • Open-loop: time division duplex (TDD) • Exploit channel reciprocity from control frames • Require RF calibration • Closed-loop: feedback • Inform transmitter about channel state • Require overhead for feedback Can reduce overhead with efficient feedback The University of Texas at Austin

10. Precoding • Use more antennas than substreams • Apply channel dependent linear transform at the TX • Create effective channel with better invertibility • Use instantaneous channel state information (CSI) • Transmitter must be informed about H (or F) H Detection … … Linear RX Spatial Multiplexing … F Symbols and Decoding Estimation & Precoding Feedback Channel The University of Texas at Austin

11. Quantized Precoding • Quantize precoder using instantaneous CSI (versus channel statistics) • Use fixed codebook of precoding matrices known to TX and RX • Select codeword from codebook and feedback index • Smart codebook designs based on Grassmannian subspace packing The University of Texas at Austin

12. Selection of Codebook • Use Grassmannian precoding [Love & Heath] • Codebooks are optimal packings in the Grassmann manifold, G(Mt, M), where M is the number of data streams • Distance measure depends on precoding criteria Set of subspaces Codebooks available at http://www.ece.utexas.edu/~rheath/research/mimo/lf/ The University of Texas at Austin

13. Remove CP & S/P DFT Vector Decoder IDFT P/S & Add CP Remove CP & S/P DFT IDFT P/S & Add CP … … … … … IDFT P/S & Add CP Remove CP & S/P DFT Feedback of quantized precoder Quantized Precoding with OFDM Per tone model The University of Texas at Austin

14. Correlation of Subcarriers • Precoding in MIMO-OFDM requires • Feedback requirements  Number of subcarriers • How can we reduce the number of matrices fed back? • Exploit correlation between precoding matrices • Send back fraction of matrices and use smart interpolation The University of Texas at Austin

15. Clustering of Subcarriers • Subcarriers are grouped into clusters • Single codeword is chosen for each cluster Clustering reduces feedback The University of Texas at Austin

16. cluster 1 cluster 2 cluster 3 cluster 4 subcarriers feedback … … … … subcarriers … feedback Interpolate at the TX Precoder Interpolation • Use codeword of the center subcarrier for cluster • Spherical interpolation with phase optimization The University of Texas at Austin

17. System Diagram TX Feedback of codeword index Precoding at TX Transmission of payload When RTS/CTS is on CTS Data RTS Channel estimation at RX RX selects precoder from codebook Linear receiver and detection RX The University of Texas at Austin

18. Multi-Mode Precoding • Allow variable number of substreams • Select optimal # of substreams and rate jointly • Use a heterogeneous codebook • Provide flexible diversity-multiplexing tradeoff The University of Texas at Austin

19. Multi-Mode Implementation • Number of STA antennas • Mandatory mode (2 antennas) • Optional modes (3 or 4 antennas) • Mode / number of substreams • Precoded beamforming (1 substream) • Precoded spatial multiplexing (>1 substreams) • Multi-mode MAC adaptation (frame-based) • Feedback the mode selection bit field • Multi-mode PHY adaptation (cluster-based) • Embed information of number of substreams in codebook The University of Texas at Austin

20. Multi-Mode Clustering • Enable fast PHY adaptation • Adaptive loading (Modulation/Coding/Substream) • Substream adaptation based on channel condition • Rate adaptation based on SNR • Embed both adaptation mode information in codebook The University of Texas at Austin

21. MAC Extension • Extension to legacy RTS frame • Append training sequences for multiple antennas • Extension to legacy CTS frame • Exploit free 10 bit available, required to fill OFDM symbol • Mode selection • Use higher order modulation (QPSK) for limited feedback • 48 bits => 1 OFDM symbol The University of Texas at Austin

22. Bit Field Format • CTS Feedback format • Mode selection field format The University of Texas at Austin

23. Spectral Efficiency of SM 4x2/4x3/4x4 precoded SM with 2 substreams, 64QAM R3/4 The University of Texas at Austin

24. Spectral Efficiency of BF 4x2/4x3/4x4 TX BF w/ LF, 64QAM R3/4 The University of Texas at Austin

25. Comparison Assumption • Assume no collision • All retransmission overhead results from packet error • In fact, RTS/CTS can reduce collision overhead • Open-loop • Retransmission overhead depending on PER • Closed-loop • Fixed overhead from RTS/CTS and feedback • Equivalent points for both overhead • SM: PER=0.3630 • BF: PER=0.2736 The University of Texas at Austin

26. Simulation Setup • Substream number: 2 (SM) • Precoding quantization: 6 bits • Subcarrier clustering size: 6 • Feedback amount: 48 bit • Modulation: 64QAM • Coding rate: 3/4 • Data Rate: 108 Mbps • Frame size: 1000 Byte • Channel Model: Ch B, Ch D, Ch E The University of Texas at Austin

27. SM with 2 Substreams in Ch B 5 dB The University of Texas at Austin

28. SM with 2 Substreams in Ch D 5 dB The University of Texas at Austin

29. SM with 2 Substreams in Ch E 6 dB The University of Texas at Austin

30. Conclusions • Quantized precoding with feedback is practical to improve throughput even if RTS/CTS is on • Subcarrier clustering reduces feedback for OFDM • Multi-mode precoding provides for diversity- multiplexing tradeoff and compatible with rate adaptation Q&A The University of Texas at Austin

31. References • Webpage: http://www.ece.utexas.edu/~rheath/lf/ • D. J. Love, R. W. Heath, Jr., W. Santipach, and M. L. Honig, "What is the Value of Limited Feedback for MIMO Channels?," to appear in IEEE Communications Magazine • J. Choi and R. W. Heath, Jr., "Interpolation Based Transmit Beamforming for MIMO-OFDM with Limited Feedback,'' Proc. of IEEE Int. Conf. on Communications, Paris, France, June 2004. • R. W. Heath Jr., "Precoding and Interpolation for Spatial Multiplexing MIMO-OFDM with Limited Feedback,'' The 2004 Workshop on Smart Antennas in Communications, Stanford, California, July 2004 http://www.stanford.edu/group/sarg/program.htm The University of Texas at Austin

32. References • D. J. Love, R. W. Heath Jr., and T. Strohmer, “Grassmannian Beamforming for Multiple-Input Multiple-Output Wireless Systems,” IEEE Trans. Inf. Th., vol. 49, pp. 2735-2747, Oct. 2003. • D. J. Love and R. W. Heath Jr., “Necessary and Sufficient Conditions for Full Diversity Order in Correlated Rayleigh Fading Beamforming and Combining Systems,” to appear in IEEE Trans. Wireless Comm. • D. J. Love and R. W. Heath Jr., “Limited feedback unitary precoding for spatial multiplexing systems,” submitted to IEEE Trans. Inf. Th., July 2003 (see also Globecom 2003) • R. W. Heath Jr. and D. J. Love, “Multi-mode precoding for spatial multiplexing systems,” submitted to IEEE Trans. Sig. Proc., December 2003 (see also Allerton 2003) The University of Texas at Austin