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Turbo-equalization for 802.11n/ac

Turbo-equalization for 802.11n/ac. Date: 2012-07-18. Authors:. Context. 802.11n/ac are heavily using non-orthogonal MIMO schemes which require co-antenna interference processing in the receiver. Context. The quality of the MIMO receivers have a very strong impact on the performance

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Turbo-equalization for 802.11n/ac

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  1. Turbo-equalization for 802.11n/ac Date: 2012-07-18 Authors: Laurent Cariou, Orange

  2. Context • 802.11n/ac are heavily using non-orthogonal MIMO schemes which require co-antenna interference processing in the receiver Laurent Cariou, Orange

  3. Context The quality of the MIMO receivers have a very strong impact on the performance • Optimum solution : joint (MIMO and channel) decoding • Maximum Likelyhood based on a « super treillis » • Sub-optimal solutions: • Disjoint decoding : MIMO detectionfollowed by channel decoding • ML detection (hard output, soft output) • Detection with interference cancellation (SIC, …) • Equalization with linear filters (MMSE, ZF, MRC) • Iterative decoding : MIMO detection  channel decoding • ML with a priori information • Equalization with interferance cancellation (MMSE-IC) Laurent Cariou, Orange

  4. Context • Turbo-equalization principle allows a very efficient processing of the interference • whatever interferers nature • Including MIMO co-antenna interferences Laurent Cariou, Orange

  5. Iterative reception allows interference cancellation through turbo-equalization • MIMO non-orthogonal scheme with estimation of transmitted symbols • Estimation of transmitted symbols in order to substract generated interferences • Turbo-equalization principle with interferences cancellation Laurent Cariou, Orange

  6. Introduction of feedback loop between channel decoding and MIMO detection functions • Turbo-equalization principle with interferences cancellation Laurent Cariou, Orange

  7. Performance modeling of iterative reception • SISO transmission on AWGN channel (limit) depends on channel coding and mapping • Genie receiver – ideal knowledge of transmitted symbols • Offset with AWGN limit depends on diversity order • Iterative receiver • Trigger point mainly depends on the amount of interference, channel coding and MIMO detector type • Offset with genie receiver depends on MIMO detector type Laurent Cariou, Orange

  8. MMSE-IC (Minimum Mean Square Error – Interference Cancellation) principle Glavieux et al. 97, Wang et al. 99, Tüchler et al. 02, Laot et al. 05 Laurent Cariou, Orange

  9. Classical MMSE at the first iteration • As no a priori information is available at the first iteration, equalization is done via a classical MMSE filtering with and the noise and signal variances • The equalized signal expressed in function of the transmitted signal s • Note that the equalized signal is biased compared to s Laurent Cariou, Orange

  10. For next iterations, the exact solution is associated to a prohibitive complexity • Exact solution for MMSE-IC for iterations > 1 • Such a treatment requires that for each iteration, and for each data symbol in a space-time bloc code: • 1 matrix inversion of size NRxNR is performed Laurent Cariou, Orange

  11. Approximations have been found to reach suitable complexity • 1st approximation for MMSE-IC: per block power invariance of the estimated symbols (average estimation of symbols) • Iteration 1 • Iteration i (> 1) • This allows to reduce the complexity by reusing the same filter for each data symbol within a space-time block code • With this approximation, only one channel inversion is required for each iteration Laurent Cariou, Orange

  12. Approximations have been found to reach suitable complexity • 2nd approximation for MMSE-IC: matched filter (MF-IC) which considers that the a priori information is perfect) • Iteration i (> 1): we apply a match filter instead of a MMSE filter • Equalized symbols after iteration l can now be obtained by: with ddiag(A) corresponding to A matrix without diagonal elements • No channel inversions are required for iterations > 1 • The filters are the same for all iterations > 1 and can be stored after their calculation in the first iteration Laurent Cariou, Orange

  13. Some theoretical results on a MIMO Rayleigh channel • Parameters SDM 4x4 QPSK CC K=7 r=1/2 5 iterations Random interleaving 2000 bits • Genie bound is reached • Gains at 10-4 • 4dB on MMSE • 2.2dB on perfect ML disjoint itérative Laurent Cariou, Orange

  14. Some theoretical results on a MIMO Rayleigh channel disjoint • Parameters SDM 4x4 16QAM CC K=7 r=1/2 5 itérations Random interleaving 2000 bits • More sensitivities to interference in case of 16QAM • The gains of iterative receivers are preserved itérative Laurent Cariou, Orange

  15. 4x4 MIMO transmission test bench for 802.11n iterative receiver hardware evaluation Laurent Cariou, Orange

  16. Parameters Laurent Cariou, Orange

  17. Performance results over Channel B • MIMO 4x4, Chan B • Simulation parameters: QPSK, 1/2, perfect CSI • Results for: • 1 iteration = MMSE only • more than 1 itertion = turbo-equalization • High gain with iterative interference cancellation scheme Laurent Cariou, Orange

  18. Performance results over Channel E • MIMO 4x4, Chan E • Simulation parameters: QPSK, 1/2, perfect CSI • Results for: • 1 iteration = MMSE only • more than 1 itertion = turbo-equalization • High gain with iterative interference cancellation scheme Laurent Cariou, Orange

  19. Performance results • General comments • iterative receiver for interference cancellation is bringing significant gain (about 5dB gain at 10-4 BER for 3 iterations). • the gains are bigger when the amount of interference increases, better with 4x4 than with 2x2 or 2x3. • helps you to reduce the required number of receive antennas (no need for 4x5 or 4x6), even for 4 SS • as 11n and 11ac go toward an increase of SS, iterative receivers become more and more interesting • 3 iterations is already getting the most part of the iterative gain. This is important as it lowers the constraints regarding the latency. Laurent Cariou, Orange

  20. Performance results • A complementary solution to beamforming technique • beamforming is strongly improving the transmission performance but its benefits are very different depending on the MCSs. Beamforming doesn’t improve significantly the performance of 4 spatial streams MCSs in case of a 4x4 system, while iterative receivers do. Iterative receivers clearly increases the range of very high throughputs. • beamforming works well if the transmitter and the receiver are beamforming-capable, and currently if they are from the same vendor. In all other cases, iterative receivers will complement beamforming. Laurent Cariou, Orange

  21. Conclusion Iterative receivers are a very competitive solution for MIMO-OFDM, • It outperforms ML receivers • It has reached maturity regarding implementation complexity and respect of latency constraints Laurent Cariou, Orange

  22. Combination with LDPC: Two kind of iterations are now combined: LDPC and turbo-equalization • Iterative channel decoding • Iterative equalization and iterative channel decoding • Combination of channel decoding loops and MIMO interference cancellation loops is flexible Laurent Cariou, Orange

  23. Combination with LDPC: Two kind of iterations are now combined: turbo decoding and turbo-equalization • We have optimized the number of inner (LDPC) and outer iterations (turbo-equalization) in order to improve the performance and reduce the number of total iterations • We have evaluated different static and dynamic stopping criteria for the inner iterations of the LDPC • We concluded that the total number of iterations of LDPC with or without turbo-equalization is kept unchanged. With turbo-equalization, this total number of iterations corresponds to the summation of the LDPC iterations of each outer turbo-equalization iteration. • Iterative receivers are therefore very well suited to a combination with LDPC Laurent Cariou, Orange

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