Turbo-equalization for 802.11n/ac May 2006 doc.: IEEE 802.11-06/0528r0 July 2012 Turbo-equalization for 802.11n/ac Date: 2012-07-18 Authors: Laurent Cariou, Orange Bruce Kraemer, Marvell

Context presentation title July 2012 Context 802.11n/ac are heavily using non-orthogonal MIMO schemes which require co-antenna interference processing in the receiver Laurent Cariou, Orange presentation title

July 2012 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 detection followed 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

July 2012 Context Turbo-equalization principle allows a very efficient processing of the interference whatever interferers nature Including MIMO co-antenna interferences Laurent Cariou, Orange presentation title

July 2012 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 presentation title

July 2012 Introduction of feedback loop between channel decoding and MIMO detection functions Turbo-equalization principle with interferences cancellation Laurent Cariou, Orange presentation title

Performance modeling of iterative reception July 2012 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 presentation title

July 2012 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 presentation title

Classical MMSE at the first iteration July 2012 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 presentation title

July 2012 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 presentation title

Approximations have been found to reach suitable complexity July 2012 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 presentation title

Approximations have been found to reach suitable complexity July 2012 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 presentation title

Some theoretical results on a MIMO Rayleigh channel July 2012 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

Some theoretical results on a MIMO Rayleigh channel July 2012 Some theoretical results on a MIMO Rayleigh channel 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 disjoint itérative Laurent Cariou, Orange

July 2012 4x4 MIMO transmission test bench for 802.11n iterative receiver hardware evaluation Laurent Cariou, Orange

Parameters July 2012 Parameters Values Number of subcarriers 128 Number of data subcarriers 108 Total symbol duration 4µs Useful symbol duration 3.2µs Cyclic prefix duration 0.8µs Channel coding Convolutional code (7,133,171) Coding rate 1/2 Modulation QPSK MIMO scheme Spatial Data Multiplexing NTX x NRX 4x4 Channel models TGn B/C/D/E/F Laurent Cariou, Orange

Performance results over Channel B July 2012 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 presentation title

Performance results over Channel E July 2012 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 presentation title

Performance results General comments July 2012 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

Performance results A complementary solution to beamforming technique July 2012 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

July 2012 Conclusion We believe that 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 We believe that it is a good time for its integration into products, especially for WIFI Laurent Cariou, Orange presentation title

July 2012 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 presentation title

July 2012 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 presentation title