High-Throughput Enhancements for 802.11: PHY Supplement

1. High-Throughput Enhancements for 802.11:PHY Supplement John Ketchum, Sanjiv Nanda, Rod Walton, Steve Howard, Mark Wallace QUALCOMM, Incorporated9 Damonmill Square, Suite 2AConcord, MA 01742Phone: 781-276-0915Fax: 781-276-0901johnk@qualcomm.com John Ketchum et al, Qualcomm

2. Agenda • Calibration • Eigenvalue decomposition complexity • Eigenvector steering modes of operation • PHY Q&A John Doe, His Company

3. Over-the-Air Calibration • ES approach requires over-the-air calibration procedure • Compensates for amplitude and phase differences in Tx and Rx chains • Calibration required infrequently– typically on association only • Simple exchange of calibration symbols and measurement information requires little overhead and background processing • Total of ~1000 bytes of calibration data exchanged for 2x2 link • ~2800 bytes for 4x4 link John Doe, His Company

4. Over-the-Air Calibration Procedure • Calibration typically is initiated by client STA on association or when STA determines that it needs calibration due to performance degradation • AP uses repeated calibrations with many clients to refine its calibration • Both STAs involved in calibration procedure compute channel estimate • Only one STA computes calibration vectors—keeps one set and sends the other to the other STA John Doe, His Company

5. Measured Calibration Errors • Calibration based on a single channel measurement in each directions • Measured calibration error is a strong function of SNR • Resulting calibration used in operating modem • Residual errors are compensated by receive processing John Doe, His Company

6. Achieved Average Calibration Error • Shows average of repeated calibration procedures • Demonstrates that calibration error is primarily limited by receiver noise John Doe, His Company

7. Degradation Due to Calibration Error • Shows cumulative distribution function of capacity in 4×4 on a random flat fading channel • Average receive SNR is 20 dB; average calibration error is -10 dB • Less than 10% degradation in median capacity John Doe, His Company

8. Degradation Due to Calibration Error • Shows cumulative distribution function of capacity in 4×4 on a random flat fading channel • Average receive SNR is 20 dB; average calibration error is -20 dB • Minimal degradation in median capacity John Doe, His Company

9. Degradation Due to Calibration Error • Shows cumulative distribution function of capacity in 4×4 on a random flat fading channel • Average receive SNR is 10 dB; average calibration error is -10 dB • Minimal degradation in median capacity John Doe, His Company

10. TDD Reciprocal Channel • In a TDD MIMO system, the over-the-air portion of the channel is reciprocal • The up-link channel, , (entity A to entity B) is the transpose of the down-link channel, , ( is the OFDM sub-carrier index): • Due to gain differences in Tx and Rx chains at both ends of the link, the baseband-to-baseband channel is not reciprocal. • The observed channel is weighted by two diagonal matrices with the complex gains of the transmit and receive chains: • These gain differences can be removed with a simple over-the-air calibration procedure that learns the gain matrices • Result is a very stable calibrated reciprocal channel John Doe, His Company

11. Calibration Procedure • Find diagonal calibration matrices that can be applied to transmit vectors to compensate for amplitude/phase variations between Rx and Tx chains in device • Calibration required once per session; i.e., upon association • Procedure as follows: • Entity A (typically a STA) observes MIMO pilot from entity B (typically an AP) • Entity A forms an estimate of channel, • Entity A transmits MIMO pilot, which entity B observes • Entity B forms channel estimate, • Entity B transmits to A • Requires transmitting 12-bit values • 624 B for 2x2; 2496 B for 4x4 John Doe, His Company

12. Calibration Procedure • Entity A now has both • Entity A now solves for diagonal calibration matrices such that • Entity A sends to entity B, then both ends of link have calibration matrix • Requires transmitting 12-bit values • 624 B for 4 antennas; 312 B for 2 antennas • Calibration matrices are incorporated into Tx steering vectors. John Doe, His Company

13. Calibration Summary • Calibration required infrequently • Low overhead message exchange • Non-time-critical channel estimates and calibration vector calculations can be performed as background tasks • Calibration errors have minimal impact on performance • Receiver processing required to clean up other misalignments takes care of calibration errors as well John Doe, His Company

14. Eigenvalue Decomposition Complexity • Only one device in a corresponding pair needs to calculate eigenvalue decomposition (EVD) • Full singular value decomposition (SVD) is not required—only right singular vectors for transmit steering • Can be computed as eigenvectors of channel correlation matrix John Doe, His Company

15. Eigenvalue Decomposition Complexity • Very low complexity direct calculation for two-antenna device • Calculate eigenvalues & eigenvectors of 2×2 Hermitian matrix for each subcarrier • 21 real multiplies, 3 inverts, 3 square roots required for each subcarrier • Total of 1176 multiplies, 168 inverts, 168 square roots for 56 subcarrier system • 20-30 µs with minimal hardware resources John Doe, His Company

16. Eigenvalue Decomposition Complexity • Larger matrices (more than two antennas) require iterative calculation of EVD • Accuracy requirements are minimal compared to many other applications • Current hardware prototype completes 4×4 EVD for 52 subbands in ~800 µs on FPGA running at 80 MHz with modest hardware resources. John Doe, His Company

17. Wideband Eigenmodes on TDD Reciprocal Channel • Uplink channel SVD: • Tx steering matrix: • Rx matched filter: • Downlink SVD: • : downlink Tx steering matrix • Transmit steering vectors at one end of the link can be computed directly from the receive matched filter at the same end of the link • Normalize and conjugate • Since Tx steering vectors can be obtained directly from Rx matched filter, eigenvectors only need to be computed at one end of the link John Doe, His Company

18. Distributed Computation of SVDs • Client STAs compute SVD • Relieves AP of burden of SVD computation for many STAs • Under EDCA access rules, STAs send a training request (TRQ) to AP • AP responds with PPDU containing unsteered reference (TRSP) • STA can use any TRSP to initiate SVD calculation • STA with data to send listens for a TRSP, and if none in reasonable period, sends TRQ • AP with data to send can transmit unsolicited TRSP addressed to data destination • Busy AP can send frequent broadcast TRSPs (~2–4 ms intervals) • STA receiving TRSP calculates SVD, and when complete, sends data in ES mod • STA with backlogged data for several PPDUs can include TRQ with data PPDU John Doe, His Company

19. ES with Distributed SVD Calculation under EDCA John Doe, His Company

20. Distributed Computation of SVDs • Under ACF, TRSP is part of SCHED message • All client STAs can share the unsteered reference in the PLCP header to calculate a channel estimate and then SVD • Protocol overhead for supporting ES mode is minimized in this manner John Doe, His Company

21. ES Mode in Scheduled Access Period John Doe, His Company

22. AP-Centric ES Mode—single TXOP • AP does all SVD calculations • Substantial processing burden at AP • Tight time constraints for completion of SVD • Requires additional overhead of RTS/TRQ & CTS/TRSP John Doe, His Company

23. AP-Centric ES Mode—Relaxed Timing • AP “primes the pump” with TRQ, STA responds with TRSP • Result is relaxed timing compared to previous • Still requires extra overhead John Doe, His Company

24. Eigenvector Steering Operation Summary • Eigenvector steering operation can be supported within existing EDCA and HCCA access mechanisms • Lowest protocol overhead incurred within ACF construct • Protocol overhead due to MIMO training for SVD is minimal when AP sends common training PPDU, for SVD calculation at client • Distributing SVD calculation to clients also relieves intense processing burden at AP • AP-centric ES operation, including SVD calculation, can be supported with relaxed timing constraints. John Doe, His Company

25. Interdigital Questions • Have you looked at some additional coding on top of the Eigen-Beamforming? • There is no need for further coding (other than either legacy BCC or advanced coding such as turbo or ldpc) on top of ES mode. • ES is optimal in the sense that it is provides optimal Tx and Rx spatial processing for a fully informed transmitter. In that sense, further tx processing such as Space-Time Block Coding or Space Frequency block coding would not be productive. • What constraints are imposed on the MAC in order to use CSI feedback? • CSI feedback can be productively utilized within the constraints of EDCA, HCCA, or ACF. • CSI feedback does not constrain the MAC. CSI feedback is available as part of the MIMO training in the PLCP header, which is common to all proposals. • Why is throughput for 4x4 more than twice that of 2x2? • In general, in the simulation results that we report, throughput for 4x4 is less than or equal to twice the throughput for 2x2. There are a few cases where this does not hold, which is due to suboptimal coding and rate selection. John Doe, His Company