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Thèse présentée devant l’INSA de Rennes en vue de l’obtention du doctorat d’Électronique

Iterative receivers for multi-antenna systems. Pierre-Jean BOUVET Le 13 décembre 2005. Thèse présentée devant l’INSA de Rennes en vue de l’obtention du doctorat d’Électronique. Foreword. Foreword. R&D Unit Broadband Wireless Acces / Innovative Radio Interface (RESA/BWA/IRI) Supervisor

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Thèse présentée devant l’INSA de Rennes en vue de l’obtention du doctorat d’Électronique

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  1. Iterative receivers for multi-antenna systems • Pierre-Jean BOUVET • Le 13 décembre 2005 Thèse présentée devant l’INSA de Rennes en vue de l’obtention du doctorat d’Électronique

  2. Foreword Foreword • R&D Unit • Broadband Wireless Acces / Innovative Radio Interface (RESA/BWA/IRI) • Supervisor • Maryline HELARD, R&D engineer HDR at France Telecom R&D division • Context • Internal project: SYCOMORE (research on digital communications) • European project: IST 4-MORE (4G demonstrator based on MIMO and MC-CDMA techniques)

  3. Outline Outline • Introduction • Multi-antenna techniques • Generic iterative receiver • Optimal space-time coding • Application to MC-CDMA • Conclusion

  4. MIMO techniques Generic iterative receiver Optimal space-time coding Application to MC-CDMA Introduction Conclusion Context MIMO transmission Objectives Part I: Introduction

  5. MIMO techniques Generic iterative receiver Optimal space-time coding Application to MC-CDMA Introduction Conclusion Context MIMO transmission Objectives Context • Digital wireless communications • High spectral efficiency • Robustness • Radio-mobile application • Multi-path propagation • Mobility • Multi-user access Time and frequency selective channel

  6. MIMO techniques Generic iterative receiver Optimal space-time coding Application to MC-CDMA Introduction Conclusion Context MIMO transmission Objectives Multi-antenna (MIMO) transmissions • Principle • Multi-antenna at transmitter and receiver • MIMO capacity [Telatar 95] : covariance of : rank of : singular values of SISO capacity

  7. MIMO techniques Generic iterative receiver Optimal space-time coding Application to MC-CDMA Introduction Conclusion Context MIMO transmission Objectives Multi-antenna (MIMO) transmissions • Motivations • Spectral efficiency gain • Performance gain • Spatial diversity gains • Antenna array gains • Limits • Interference terms • Co Antenna Interference (CAI) • Spatial correlation • Antennas must be sufficiently spaced • Rich scattering environment required • Optimal MIMO capacity exploitation • Complex algorithm not well suited for practical implementation • Lack of generic schemes Capacity gain linear in min(Nt, Nr)

  8. MIMO techniques Generic iterative receiver Optimal space-time coding Application to MC-CDMA Introduction Conclusion Context MIMO transmission Objectives Objectives • Multi-antenna transmission • Spectral efficiency gain • Arbitrary antenna configuration • Near-optimal reception • MIMO capacity exploitation • Iterative (turbo) principle • Low complexity algorithm • Multi-user access

  9. MIMO techniques Generic iterative receiver Optimal space-time coding Application to MC-CDMA Introduction Conclusion Transmitter MIMO Channel Classification LD code Equivalent representation CAI Part II: MIMO techniques

  10. MIMO techniques Generic iterative receiver Optimal space-time coding Application to MC-CDMA Introduction Conclusion Transmitter MIMO Channel Classification LD code Equivalent representation CAI Transmitter Information bits Modulation symbols Coded bits Convolutional code BICM scheme [Caire et al. 98]

  11. MIMO techniques Generic iterative receiver Optimal space-time coding Application to MC-CDMA Introduction Conclusion Transmitter MIMO Channel Classification LD code Equivalent representation CAI MIMO channel • Multi carrier approach (OFDM) Equivalent flat fading MIMO channels Reduced complexity MIMO equalization (no ISI treatment)

  12. MIMO techniques Generic iterative receiver Optimal space-time coding Application to MC-CDMA Introduction Conclusion Transmitter MIMO Channel Classification LD code Equivalent representation CAI MIMO channel • Equivalent flat fading MIMO channel • By assuming ideal symbol interleaving: • T-block Rayleigh fading model • Represents the optimal performance of a MIMO-OFDM system over a radio-mobile channel

  13. MIMO techniques Generic iterative receiver Optimal space-time coding Application to MC-CDMA Introduction Conclusion Transmitter MIMO Channel Classification LD code Equivalent representation CAI Classification of MIMO techniques Channel State Information (CSI) • CSI required at Tx and Rx • Eigen beam forming • Water-filling • Pre-equalization • CSI required only at Rx • Treillis based • Block based • No CSI required • Differential STC • USTM

  14. MIMO techniques Generic iterative receiver Optimal space-time coding Application to MC-CDMA Introduction Conclusion Transmitter MIMO Channel Classification LD code Equivalent representation CAI Classification of MIMO techniques Channel State Information (CSI) • CSI required at Tx and Rx • Eigen beam forming • Water-filling • Pre-equalization • CSI required only at Rx • Treillis based • Block based • No CSI required • Differential STC • USTM

  15. MIMO techniques Generic iterative receiver Optimal space-time coding Application to MC-CDMA Introduction Conclusion Transmitter MIMO Channel Classification LD code Equivalent representation CAI • Linear Dispersion (LD) Code • [Hassibi et al. 02] Classification of MIMO techniques • Spatial Data Multiplexing (SDM) • [Foschini et al. 96, Wolniansky et al. 98] • CSI required at Tx and Rx • Eigen beam forming • Water-filling • Pre-equalization • CSI required only at Rx • Treillis based • Block based • No CSI required • Differential STC • USTM • Space Time Block Coding (STBC) • [Alamouti 98, Tarokh et al. 99] • Linear Precoded STBC • [Da Silva et al. 98] • Algebraical STBC • [Damen et al. 03, El Gamal et al. 03]

  16. MIMO techniques Generic iterative receiver Optimal space-time coding Application to MC-CDMA Introduction Conclusion Transmitter MIMO Channel Classification LD code Equivalent representation CAI LD Code STC latency: Input block length: STC rate:

  17. MIMO techniques Generic iterative receiver Optimal space-time coding Application to MC-CDMA Introduction Conclusion Transmitter MIMO Channel Classification LD code Equivalent representation CAI Equivalent representation Joint space-time coding and channel representation

  18. MIMO techniques Generic iterative receiver Optimal space-time coding Application to MC-CDMA Introduction Conclusion Transmitter MIMO Channel Classification LD code Equivalent representation CAI Special LD Code Examples

  19. MIMO techniques Generic iterative receiver Optimal space-time coding Application to MC-CDMA Introduction Conclusion Transmitter MIMO Channel Classification LD code Equivalent representation CAI Solution • Transmission matrices • Reception matrices • Equivalent channel matrix

  20. MIMO techniques Generic iterative receiver Optimal space-time coding Application to MC-CDMA Introduction Conclusion Transmitter MIMO Channel Classification LD code Equivalent representation CAI Example: Alamouti Code over channel • Transmission matrices • Equivalent model

  21. MIMO techniques Generic iterative receiver Optimal space-time coding Application to MC-CDMA Introduction Conclusion Transmitter MIMO Channel Classification LD code Equivalent representation CAI Co-antenna interference Desired signal Noise CAI terms Multi-antenna transmission provides CAI terms CAI terms can be treated like ISI terms (which were due to the frequency selectivity in SISO transmission)

  22. MIMO techniques Generic iterative receiver Optimal space-time coding Application to MC-CDMA Introduction Conclusion Principle MIMO equalizer Complexity analysis Asymptotical analysis Reception strategies Performance results Part III: Generic iterative receiver

  23. MIMO techniques Generic iterative receiver Optimal space-time coding Application to MC-CDMA Introduction Conclusion Principle MIMO equalizer Complexity analysis Asymptotical analysis Reception strategies Performance results Reception state of the art • Optimal solution: joint detection • ML detection based on a “super trellis” • Sub-optimal solution • Disjoint decoding: MIMO detection  channel decoding • MAP MIMO detection • SIC, OSIC, PIC detection • MRC, MMSE, ZF equalization • Iterative decoding: MIMO detection  channel decoding [Berrou et al. 93] • MAP MIMO detection • [Tonello 00, Boutros et al. 00, Vikalo et al. 02] • Filtered based MIMO equalization • [Sellathurai et al. 00, Gueguen 03, Witzke et al. 03] Optimal performance Very high complexity Relative low complexity Optimal performance for orthogonal STC (Alamouti) Sub-optimal performance for non-orthogonal STC Near optimal performance High complexity Near optimal performance reduced complexity

  24. MIMO techniques Generic iterative receiver Optimal space-time coding Application to MC-CDMA Introduction Conclusion Principle MIMO equalizer Complexity analysis Asymptotical analysis Reception strategies Performance results Reception state of the art • Optimal solution: joint detection • ML detection based on a “super trellis” • Sub-optimal solution • Disjoint decoding: MIMO detection  channel decoding • MAP MIMO detection • SIC, OSIC, PIC detection • MRC, MMSE, ZF equalization • Iterative decoding: MIMO detection  channel decoding [Berrou et al. 93] • MAP MIMO detection • [Tonello 00, Boutros et al. 00, Vikalo et al. 02] • Filtered based MIMO equalization • [Sellathurai et al. 00, Gueguen 03, Witzke et al. 03] Near optimal performance reduced complexity

  25. MIMO techniques Generic iterative receiver Optimal space-time coding Application to MC-CDMA Introduction Conclusion Principle MIMO equalizer Complexity analysis Asymptotical analysis Reception strategies Performance results Principle • Application of the turbo-equalization concept to MIMO Channel decoding stage MIMO equalization stage

  26. MIMO techniques Generic iterative receiver Optimal space-time coding Application to MC-CDMA Introduction Conclusion Principle MIMO equalizer Complexity analysis Asymptotical analysis Reception strategies Performance results MIMO equalizer (1) • MMSE based soft interference cancellation (MMSE-IC) • [Glavieux et al. 97, Wang et al. 99, Reynolds et al. 01, Tüchler et al. 02, Laot et al. 05] • MMSE optimization of both filters

  27. MIMO techniques Generic iterative receiver Optimal space-time coding Application to MC-CDMA Introduction Conclusion Principle MIMO equalizer Complexity analysis Asymptotical analysis Reception strategies Performance results MIMO equalizer (2) • Optimal solution: MMSE-IC • Time invariant approximation: MMSE-IC(1) TNr x TNr matrix inversion

  28. MIMO techniques Generic iterative receiver Optimal space-time coding Application to MC-CDMA Introduction Conclusion Principle MIMO equalizer Complexity analysis Asymptotical analysis Reception strategies Performance results MIMO equalizer (3) • Matched filter approximation: MMSE-IC(2) • Zero-Forcing solution: ZF-IC Iteration 1 Iteration p Iteration 1 Iteration p

  29. MIMO techniques Generic iterative receiver Optimal space-time coding Application to MC-CDMA Introduction Conclusion Principle MIMO equalizer Complexity analysis Asymptotical analysis Reception strategies Performance results Complexity analysis (MIMO equalizer) Proposed iterative receivers provide complexity gain

  30. MIMO techniques Generic iterative receiver Optimal space-time coding Application to MC-CDMA Introduction Conclusion Principle MIMO equalizer Complexity analysis Asymptotical analysis Reception strategies Performance results Asymptotical analysis • Asymptotical performances = Genie aided receiver • Asymptotical equivalent channel

  31. MIMO techniques Generic iterative receiver Optimal space-time coding Application to MC-CDMA Introduction Conclusion Principle MIMO equalizer Complexity analysis Asymptotical analysis Reception strategies Performance results Asymptotical diversity • Pair-wise error probability • Chi-square approximation and Chernoff bound

  32. MIMO techniques Generic iterative receiver Optimal space-time coding Application to MC-CDMA Introduction Conclusion Principle MIMO equalizer Complexity analysis Asymptotical analysis Reception strategies Performance results Asymptotical diversity • Proposed definition of the space-time diversity • Total diversity exploited by both channel and space-time coding • Modified Singleton Bound [Gresset et al. 04] Full channel diversity can only be achieved by using jointly channel coding and space-time coding

  33. MIMO techniques Generic iterative receiver Optimal space-time coding Application to MC-CDMA Introduction Conclusion Principle MIMO equalizer Complexity analysis Asymptotical analysis Reception strategies Performance results Performance results: simulation conditions • Theoretical independent T-Block Rayleigh flat fading MIMO channel • Non recursive non systematic convolutional code (133,171)o, K=7 • SOVA algorithm for channel decoding • No spatial correlation • Normalized BER • Asymptotical curve: Matched filter Bound (MFB) • Optimal curve: AWGN decoupled Receive array gain not taken into account Genie aided receiver Min(Nt,Nr) parallel AWGN channels

  34. MIMO techniques Generic iterative receiver Optimal space-time coding Application to MC-CDMA Introduction Conclusion Principle MIMO equalizer Complexity analysis Asymptotical analysis Reception strategies Performance results Performance results: Jafarkhani code Iterative decoding Disjoint decoding MFB is reached whichever iterative algorithm is used 5 iterations are sufficient 0.8 dB gain at 10-4 versus disjoint MAP receiver (state of the art)

  35. MIMO techniques Generic iterative receiver Optimal space-time coding Application to MC-CDMA Introduction Conclusion Principle MIMO equalizer Complexity analysis Asymptotical analysis Reception strategies Performance results Performance results: SDM Disjoint decoding Iterative decoding MFB is reached only with the MMSE-IC(1) receiver 7 dB gain at 10-4 versus disjoint MMSE receiver

  36. MIMO techniques Generic iterative receiver Optimal space-time coding Application to MC-CDMA Introduction Conclusion Principle MIMO equalizer Complexity analysis Asymptotical analysis Reception strategies Performance results Performance results: SDM overloaded Disjoint decoding Iterative decoding MFB is reached only with the MMSE-IC(1) receiver The Iterative receiver still converges although the rank of is degenerated

  37. MIMO techniques Generic iterative receiver Optimal space-time coding Application to MC-CDMA Introduction Conclusion Synthesis • Derivation of a MMSE iterative receiver for generic MIMO transmission • Reduced complexity versus MAP based iterative algorithm • Asymptotical analysis • Proposition of an estimation of the space-time coding diversity • Simulation results • MMSE-IC(1) tends towards the MFB curve whichever space-time coding scheme is used • MMSE-IC(1) still works in case of rank degenerated channel matrix • MMSE-IC(2) and ZF-IC converge when CAI terms are quite low and/or for small order modulation

  38. MIMO techniques Generic iterative receiver Optimal space-time coding Application to MC-CDMA Introduction Conclusion Performance results DTST coding Optimality conditions Part IV: Optimal space-time coding

  39. MIMO techniques Generic iterative receiver Optimal space-time coding Application to MC-CDMA Introduction Conclusion Performance results DTST coding Optimality conditions Optimality conditions • Maximizing data rate • Maximizing space-time coding diversity • Minimizing and • Minimizing the non orthogonal terms of

  40. MIMO techniques Generic iterative receiver Optimal space-time coding Application to MC-CDMA Introduction Conclusion Performance results DTST coding Optimality conditions Optimality conditions • Maximizing data rate • Maximizing space-time coding diversity • Minimizing and • Minimizing the non orthogonal terms of

  41. MIMO techniques Generic iterative receiver Optimal space-time coding Application to MC-CDMA Introduction Conclusion Performance results DTST coding Optimality conditions Maximizing data rate • Ergodic Capacity • High SNR approximation (Foschini et al. 96)

  42. MIMO techniques Generic iterative receiver Optimal space-time coding Application to MC-CDMA Introduction Conclusion Performance results DTST coding Optimality conditions Maximizing the diversity • Assuming ML detection • Pairwise error probability analysis • Diversity gain maximization • TAST [El Gamal et al. 03], FDFR [Ma et al. 03] • Assuming MMSE-IC reception • Asymptotical analysis • Space-time coding diversity maximization • Sufficient condition: “Along a space-time coded block, each data symbol must be transmitted uniquely by each antenna”

  43. MIMO techniques Generic iterative receiver Optimal space-time coding Application to MC-CDMA Introduction Conclusion Performance results DTST coding Optimality conditions Summary • Conditions: • STC construction rule: • “During Nt symbol durations, min(Nt,Nr) data symbols have to be uniquely transmitted by the Nt antennas” 1 2 3

  44. MIMO techniques Generic iterative receiver Optimal space-time coding Application to MC-CDMA Introduction Conclusion Performance results DTST coding Optimality conditions Diagonal Threaded Space Time (DTST) coding

  45. MIMO techniques Generic iterative receiver Optimal space-time coding Application to MC-CDMA Introduction Conclusion Performance results DTST coding Optimality conditions Example over a channel Optimal with iterative decoding

  46. MIMO techniques Generic iterative receiver Optimal space-time coding Application to MC-CDMA Introduction Conclusion Performance results DTST coding Optimality conditions Performance results • 4 transmit antennas and 2 receive antennas • Channel model: T-block Rayleigh flat fading • No spatial correlation • Reception • If S is orthogonal: MRC • If S is non orthogonal: MMSE-IC with 5 iterations • Optimal performance: AWGN decoupled • Corresponds to virtual parallel AWGN channels

  47. MIMO techniques Generic iterative receiver Optimal space-time coding Application to MC-CDMA Introduction Conclusion Performance results DTST coding Optimality conditions System Parameters Alamouti AS Jafarkhani Double Alamouti (DA) DTST

  48. MIMO techniques Generic iterative receiver Optimal space-time coding Application to MC-CDMA Introduction Conclusion Performance results DTST coding Optimality conditions Ergodic capacity Near optimal exploitation for DA and DTST schemes

  49. MIMO techniques Generic iterative receiver Optimal space-time coding Application to MC-CDMA Introduction Conclusion Performance results DTST coding Optimality conditions BER Performance 2 bps/Hz Best performance achieved with DTST (and DA)

  50. MIMO techniques Generic iterative receiver Optimal space-time coding Application to MC-CDMA Introduction Conclusion Performance results DTST coding Optimality conditions Capacity at BER=10-4 When increasing the spectral efficiency, only the iterative system is able to exploit the MIMO capacity

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