Quantized Precoding with Feedback 11n Partial Proposal Month 2002 doc.: IEEE 802.11-02/xxxr0 September 2004 Quantized Precoding with Feedback 11n 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 John Doe, His Company

Outline Main features Closed-loop MIMO with limited feedback September 2004 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

Main Features Closed-loop MIMO-OFDM with limited feedback (LF) Month 2002 doc.: IEEE 802.11-02/xxxr0 September 2004 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 John Doe, His Company

Proposal Overview Please refer to IEEE 802.11-04/962r0 September 2004 The University of Texas at Austin

Feedback Structure No physical feedback channels available in 802.11 Month 2002 doc.: IEEE 802.11-02/xxxr0 September 2004 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 John Doe, His Company

When and Why Use RTS/CTS? September 2004 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

MIMO without CSI at TX Space-time block code (STBC) September 2004 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 The University of Texas at Austin

MIMO with CSI at TX Transmit beamforming Precoded spatial multiplexing September 2004 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 Multi-mode precoding Adaptation between precoded BF and SM modes The University of Texas at Austin

How to Get CSI at TX? Open-loop: time division duplex (TDD) September 2004 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

Precoding H … … … Use more antennas than substreams Month 2002 doc.: IEEE 802.11-02/xxxr0 September 2004 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 Why do we propose precoding? Performance of spatial multiplexing is very sensitive to channel singular values. Precoding is a method of using additional antennas at the transmitter, beyond the number of substreams, to provide higher reliability and range through increased diversity. This advantage is achieved by customizing the transmitted signal to the channel; which requires the transmitter to be informed about the current channel state. The question is how to reduce the size of feedback.. … … Linear RX Detection Spatial Multiplexing F … Symbols and Decoding Feedback Channel Estimation & Precoding The University of Texas at Austin John Doe, His Company

Month 2002 doc.: IEEE 802.11-02/xxxr0 September 2004 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 John Doe, His Company

Selection of Codebook Use Grassmannian precoding [Love & Heath] September 2004 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

Quantized Precoding with OFDM Month 2002 doc.: IEEE 802.11-02/xxxr0 September 2004 Quantized Precoding with OFDM P/S & Add CP IDFT Feedback of quantized precoder Remove S/P DFT Vector Decoder … NOTE FOR JOAHNN: Explain the figure as a generic OFDM system using quantized precoding. Due to the number of tones, the feedback requirements increase. Hence we propose to quantize clusters of tones. Based on the design issues discussed before, we can show that Quantized precoding is a means of achieving the diversity gains of precoding with only a few bytes of feedback. It uses a predetermined codebook of (precoding) matrices known to both the transmitter and receiver. Based on an estimate of the channel state information, the receiver chooses a set of precoding matrices and sends the corresponding indices back to the transmitter. The transmitter reconstructs the precoding matrices from the indices and uses them for payload transmission. With a few bytes of feedback it is possible to realize near-ideal precoding gains with both beamforming and spatial multiplexing modes of operation. Per tone model The University of Texas at Austin John Doe, His Company

Correlation of Subcarriers Month 2002 doc.: IEEE 802.11-02/xxxr0 September 2004 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 John Doe, His Company

Clustering of Subcarriers Month 2002 doc.: IEEE 802.11-02/xxxr0 September 2004 Clustering of Subcarriers Subcarriers are grouped into clusters Single codeword is chosen for each cluster Clustering reduces feedback The University of Texas at Austin John Doe, His Company

Precoder Interpolation Month 2002 doc.: IEEE 802.11-02/xxxr0 September 2004 Precoder Interpolation Use codeword of the center subcarrier for cluster Spherical interpolation with phase optimization subcarriers cluster 1 cluster 2 cluster 3 cluster 4 feedback subcarriers feedback … Interpolate at the TX The University of Texas at Austin John Doe, His Company

System Diagram CTS Data RTS TX RX Feedback of codeword index September 2004 System Diagram TX Feedback of codeword index Precoding at TX Transmission of payload When RTS/CTS is on CTS Data RTS Linear receiver and detection Channel estimation at RX RX selects precoder from codebook RX The University of Texas at Austin

Multi-Mode Precoding Allow variable number of substreams September 2004 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

Multi-Mode Implementation Month 2002 doc.: IEEE 802.11-02/xxxr0 September 2004 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 John Doe, His Company

Multi-Mode Clustering September 2004 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

MAC Extension Extension to legacy RTS frame September 2004 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

Bit Field Format CTS Feedback format Mode selection field format September 2004 Bit Field Format CTS Feedback format Mode selection field format Mode Antenna (APxSTA) # Substreams Data Rate (Mbps) 000 4x2 1 6/9/12/18/24/36/48/54 001 2 12/18/24/48/72/96/108 010 4x3 011 100 3 18/27/36/54/72/108/144/162 101 4x4 110 111 The University of Texas at Austin

Spectral Efficiency of SM September 2004 Spectral Efficiency of SM 4x2/4x3/4x4 precoded SM with 2 substreams, 64QAM R3/4 (108Mbps) Frame Size 1000B 2000B 3000B 4000B Payload 5.2632 5.3571 5.3333 +Preamble 3.5714 4.2553 4.6154 4.7619 +ACK 2.6991 3.5682 4.0513 4.2988 +RTS/CTS 1.8128 2.6968 3.2551 3.5983 +Feedback 1.7193 2.5919 3.1525 3.5038 Overhead 36.30% 27.36% 22.19% 18.49% The University of Texas at Austin

Spectral Efficiency of BF September 2004 Spectral Efficiency of BF 4x2/4x3/4x4 TX BF w/ LF, 64QAM R3/4 (54Mbps) Frame Size 1000B 2000B 3000B 4000B Payload 2.6316 2.6667 2.6786 2.6846 +Preamble 2.1277 2.3810 2.4793 2.5316 +ACK 1.7841 2.1494 2.3068 2.3945 +RTS/CTS 1.3484 1.7992 2.0248 2.1603 +Feedback 1.2960 1.7519 1.9846 2.1258 Overhead 27.36% 18.49% 13.97% 11.22% The University of Texas at Austin

Comparison Assumption September 2004 Comparison Assumption Assume no collision All retransmission 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

Simulation Setup Substream number: 2 (SM) September 2004 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

SM with 2 Substreams in Ch B September 2004 SM with 2 Substreams in Ch B 5 dB The University of Texas at Austin

SM with 2 Substreams in Ch D September 2004 SM with 2 Substreams in Ch D 5 dB The University of Texas at Austin

SM with 2 Substreams in Ch E September 2004 SM with 2 Substreams in Ch E 6 dB The University of Texas at Austin

September 2004 Conclusions Quantized precoding with feedback is practical to improve goodput 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

References Webpage: http://www.ece.utexas.edu/~rheath/lf/ September 2004 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

September 2004 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