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

20/04/23France Telecom Division Recherche et Développement

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

2

Foreword

s R&D Unit

QBroadband Wireless Acces / Innovative Radio Interface

(RESA/BWA/IRI)

s Supervisor

QMaryline HELARD, R&D engineer HDR at France Telecom R&D

division

s ContextQInternal project: SYCOMORE (research on digital communications)

QEuropean project: IST 4-MORE (4G demonstrator based on MIMO and MC-CDMA techniques)

Foreword

3

Outline

I. Introduction

II. Multi-antenna techniques

III. Generic iterative receiver

IV. Optimal space-time coding

V. Application to MC-CDMA

VI. Conclusion

Outline

4

Part I: Introduction

Context MIMO transmission

Objectives

Introduction

MIMO techniques

Generic iterative receiver

Optimal space-time coding

Application to MC-CDMA

Conclusion

5

Context

s Digital wireless communicationsQHigh spectral efficiency

QRobustness

s Radio-mobile applicationQMulti-path propagation

QMobility

QMulti-user access

Introduction

MIMO techniques




Conclusion

Time and frequency selective channel


Objectives

6

Multi-antenna (MIMO) transmissions

s PrincipleQMulti-antenna at transmitter and receiver

s MIMO capacity [Telatar 95]

Introduction

MIMO techniques




Conclusion

: covariance of

: rank of

: singular values

of SISO capacity


Objectives

7

Multi-antenna (MIMO) transmissions

s MotivationsQSpectral efficiency gain

QPerformance gain–Spatial diversity gains–Antenna array gains

s LimitsQInterference terms

–Co Antenna Interference (CAI)

QSpatial correlation–Antennas must be sufficiently spaced–Rich scattering environment required

QOptimal MIMO capacity exploitation–Complex algorithm not well suited for practical implementation–Lack of generic schemes

Introduction

MIMO techniques




Conclusion


Objectives

Capacity gain linear in min(Nt, Nr)

8

Objectives

s Multi-antenna transmissionQSpectral efficiency gain

QArbitrary antenna configuration

s Near-optimal receptionQMIMO capacity exploitation

QIterative (turbo) principle

QLow complexity algorithm

QMulti-user access

Introduction

MIMO techniques




Conclusion


Objectives

9

Part II: MIMO techniques

Introduction

MIMO techniques




Conclusion

Classification LD code Equivalent representation CAITransmitter

MIMO Channel

10

Transmitter

BICM scheme [Caire et al. 98]

Information bits Coded bits

Modulation symbols

Convolutional code

Introduction

MIMO techniques




Conclusion


MIMO Channel

11

MIMO channel

s Multi carrier approach (OFDM)

Equivalent flat fading MIMO channels

Reduced complexity MIMO equalization (no ISI treatment)

Introduction

MIMO techniques




Conclusion


MIMO Channel

12

Q By assuming ideal symbol interleaving:

Q T-block Rayleigh fading model

Q Represents the optimal performance of a MIMO-OFDM system over a radio-mobile channel

MIMO channel

s Equivalent flat fading MIMO channel

Introduction

MIMO techniques




Conclusion


MIMO Channel

13

Classification of MIMO techniques

s CSI required at Tx and RxQEigen beam forming

QWater-filling

QPre-equalization

s CSI required only at RxQTreillis based

QBlock based

s No CSI requiredQDifferential STC

QUSTM

Channel State Information (CSI)

Introduction

MIMO techniques




Conclusion


MIMO Channel

14



QWater-filling

QPre-equalization


QBlock based


QUSTM

Channel State Information (CSI)

Introduction

MIMO techniques




Conclusion


MIMO Channel

15



QWater-filling

QPre-equalization


QBlock based


QUSTM

Linear Precoded STBC [Da Silva et al. 98]

Spatial Data Multiplexing (SDM) [Foschini et al. 96, Wolniansky et al. 98]

Space Time Block Coding (STBC) [Alamouti 98, Tarokh et al. 99]

Algebraical STBC [Damen et al. 03, El Gamal et al. 03]

Linear Dispersion (LD) Code [Hassibi et al. 02]

Introduction

MIMO techniques




Conclusion


MIMO Channel

16

LD Code

STC latency:

Input block length:

STC rate:

Introduction

MIMO techniques




Conclusion


MIMO Channel

17

Equivalent representation

Joint space-time coding and channel representation

Introduction

MIMO techniques




Conclusion


MIMO Channel

18

Special LD Code

Examples

Introduction

MIMO techniques




Conclusion


MIMO Channel

19

Solution

s Transmission matrices

s Reception matrices

s Equivalent channel matrix

Introduction

MIMO techniques




Conclusion


MIMO Channel

20

Example: Alamouti Code over channel

s Transmission matrices

s Equivalent model

Introduction

MIMO techniques




Conclusion


MIMO Channel

21

Co-antenna interference

Desired signal CAI terms Nois

e

CAI terms can be treated like ISI terms (which were due to the frequency selectivity in SISO transmission)

Multi-antenna transmission provides CAI terms

Introduction

MIMO techniques




Conclusion


MIMO Channel

22

Part III: Generic iterative receiver

Reception strategies

Principle

MIMO equalizer

Asymptotical analysis

Complexity analysis

Performance results

Introduction

MIMO techniques




Conclusion

23

s Optimal solution: joint detectionQ ML detection based on a “super trellis”

s Sub-optimal solution1. Disjoint decoding: MIMO detection channel decoding

a.MAP MIMO detection

b.SIC, OSIC, PIC detection

c.MRC, MMSE, ZF equalization

2. Iterative decoding: MIMO detection channel decoding [Berrou et al. 93]


•[Tonello 00, Boutros et al. 00, Vikalo et al. 02]

b.Filtered based MIMO equalization

•[Sellathurai et al. 00, Gueguen 03, Witzke et al. 03]

Reception state of the art

Relative low complexity

Sub-optimal performance for non-orthogonal STC

High complexity

Near optimal performance

reduced complexity


Very high complexity

Optimal performance


Principle

MIMO equalizer


Complexity analysis

Performance results

Introduction

MIMO techniques




Conclusion

Optimal performance for orthogonal STC (Alamouti)

24

s Optimal solution: joint detectionQ ML detection based on a “super trellis”

s Sub-optimal solution1. Disjoint decoding: MIMO detection channel decoding


b.SIC, OSIC, PIC detection

c.MRC, MMSE, ZF equalization

2. Iterative decoding: MIMO detection channel decoding [Berrou et al. 93]


•[Tonello 00, Boutros et al. 00, Vikalo et al. 02]

b.Filtered based MIMO equalization

•[Sellathurai et al. 00, Gueguen 03, Witzke et al. 03]

Reception state of the art


Principle

MIMO equalizer


Complexity analysis

Performance results

Introduction

MIMO techniques




Conclusion

reduced complexity


25

Principle

s Application of the turbo-equalization concept to MIMO

Channel decoding stageMIMO equalization stage


Principle

MIMO equalizer


Complexity analysis

Performance results

Introduction

MIMO techniques




Conclusion

26

MIMO equalizer (1)

s MMSE based soft interference cancellation (MMSE-IC)Q [Glavieux et al. 97, Wang et al. 99, Reynolds et al. 01, Tüchler et al. 02,

Laot et al. 05]

s MMSE optimization of both filters


Principle

MIMO equalizer


Complexity analysis

Performance results

Introduction

MIMO techniques




Conclusion

27

MIMO equalizer (2)

s Optimal solution: MMSE-IC

s Time invariant approximation: MMSE-IC(1)


Principle

MIMO equalizer


Complexity analysis

Performance results

TNr x TNr matrix inversion

Introduction

MIMO techniques




Conclusion

28

MIMO equalizer (3)

s Matched filter approximation: MMSE-IC(2)

s Zero-Forcing solution: ZF-IC

Iteration 1 Iteration p

Iteration 1 Iteration p


Principle

MIMO equalizer


Complexity analysis

Performance results

Introduction

MIMO techniques




Conclusion

29

Complexity analysis (MIMO equalizer)

Proposed iterative receivers provide complexity gain


Principle

MIMO equalizer


Complexity analysis

Performance results

Introduction

MIMO techniques




Conclusion

30


s Asymptotical performances = Genie aided receiver

s Asymptotical equivalent channel


Principle

MIMO equalizer


Complexity analysis

Performance results

Introduction

MIMO techniques




Conclusion

31

s Pair-wise error probability

s Chi-square approximation and Chernoff bound

Asymptotical diversity


Principle

MIMO equalizer


Complexity analysis

Performance results

Introduction

MIMO techniques




Conclusion

32

Asymptotical diversity

s Proposed definition of the space-time diversity

s Total diversity exploited by both channel and space-time

codingQModified Singleton Bound [Gresset et al. 04]

Full channel diversity can only be achieved by using jointly channel coding and space-time coding


Principle

MIMO equalizer


Complexity analysis

Performance results

Introduction

MIMO techniques




Conclusion

33

Performance results: simulation conditions

s Theoretical independent T-Block Rayleigh flat fading MIMO channel

s Non recursive non systematic convolutional code (133,171)o, K=7

s SOVA algorithm for channel decoding

s No spatial correlation

s Normalized BER

s Asymptotical curve: Matched filter Bound (MFB)

s Optimal curve: AWGN decoupled

Receive array gain not taken into account

Genie aided receiver

Min(Nt,Nr) parallel AWGN channels


Principle

MIMO equalizer


Complexity analysis

Performance results

Introduction

MIMO techniques




Conclusion

34

Performance results: Jafarkhani code

MFB is reached whichever iterative algorithm is used

0.8 dB gain at 10-4 versus disjoint MAP receiver (state of the art)


Principle

MIMO equalizer


Complexity analysis

Performance results

Disjoint decodin

g

Iterative decodin

g

Introduction

MIMO techniques




Conclusion

5 iterations are sufficient

35

Performance results: SDM

MFB is reached only with the MMSE-IC(1) receiver

7 dB gain at 10-4 versus disjoint MMSE receiver


Principle

MIMO equalizer


Complexity analysis

Performance results

Disjoint decodin

g

Iterative decodin

g

Introduction

MIMO techniques




Conclusion

36

Performance results: SDM overloaded

MFB is reached only with the MMSE-IC(1) receiver

The Iterative receiver still converges although the rank of is degenerated


Principle

MIMO equalizer


Complexity analysis

Performance results

Disjoint decodin

g

Iterative decodin

g

Introduction

MIMO techniques




Conclusion

37

Synthesis

s Derivation of a MMSE iterative receiver for generic MIMO

transmissionQReduced complexity versus MAP based iterative algorithm

s Asymptotical analysisQProposition of an estimation of the space-time coding diversity

s Simulation resultsQMMSE-IC(1) tends towards the MFB curve whichever space-time coding scheme is used

QMMSE-IC(1) still works in case of rank degenerated channel matrix

QMMSE-IC(2) and ZF-IC converge when CAI terms are quite low and/or for small order modulation

Introduction

MIMO techniques




Conclusion

38

Part IV: Optimal space-time coding

Optimality conditions DTST coding Performance results

Introduction

MIMO techniques




Conclusion

39

Optimality conditions

1. Maximizing data rate

2. Maximizing space-time coding diversity

3. Minimizing and

4. Minimizing the non orthogonal terms of


Introduction

MIMO techniques




Conclusion

40

Optimality conditions

1. Maximizing data rate

2. Maximizing space-time coding diversity

3. Minimizing and

4. Minimizing the non orthogonal terms of


Introduction

MIMO techniques




Conclusion

41

Maximizing data rate

s Ergodic Capacity

s High SNR approximation (Foschini et al. 96)


Introduction

MIMO techniques




Conclusion

42

Maximizing the diversity

s Assuming ML detectionQPairwise error probability analysis

QDiversity gain maximization

QTAST [El Gamal et al. 03], FDFR [Ma et al. 03]

s Assuming MMSE-IC receptionQAsymptotical analysis

QSpace-time coding diversity maximization

QSufficient condition: “Along a space-time coded block, each data symbol must be transmitted uniquely by each antenna”


Introduction

MIMO techniques




Conclusion

43

Summary

s Conditions:

s STC construction rule:Q “During Nt symbol durations, min(Nt,Nr) data symbols have to be uniquely transmitted by the Nt antennas”

1

2

3


Introduction

MIMO techniques




Conclusion

44

Diagonal Threaded Space Time (DTST) coding


Introduction

MIMO techniques




Conclusion

45

Example over a channel

Optimal with iterative decoding


Introduction

MIMO techniques




Conclusion

46

Performance results

s 4 transmit antennas and 2 receive antennas

s Channel model: T-block Rayleigh flat fading

s No spatial correlation

s ReceptionQIf S is orthogonal: MRC

QIf S is non orthogonal: MMSE-IC with 5 iterations

s Optimal performance: AWGN decoupledQCorresponds to virtual parallel AWGN channels


Introduction

MIMO techniques




Conclusion

47

System Parameters

Alamouti AS Double Alamouti (DA) Jafarkhani

DTST


Introduction

MIMO techniques




Conclusion

48

Ergodic capacity

Near optimal exploitation for DA and DTST schemes


Introduction

MIMO techniques




Conclusion

49

BER Performance

Best performance achieved with DTST (and DA)


2 bps/Hz

Introduction

MIMO techniques




Conclusion

50

Capacity at BER=10-4

When increasing the spectral efficiency, only the iterative system is able to exploit the MIMO capacity


Introduction

MIMO techniques




Conclusion

51

Synthesis

s Construction criteria of optimal LD code

s DTST codeQ Check the optimality criteria

Q Subset of special linear dispersion code family

Q Generic construction scheme

s Simulation resultsQ DTST codes lead to near optimal exploitation of MIMO capacity

and spatial diversity

Introduction

MIMO techniques




Conclusion

52

Part V: Application to MC-CDMA

Introduction

MIMO techniques




Conclusion

MC-CDMA Transmitter Equivalent model

Iterative receiver

Performance results

53

MC-CDMA

s Introduced in 93 [Yee et al. 93, Fazel et al.

93]

s Aim

Qto spread multi-user information in the

frequency domain

s Principle

QCombination of CDMA and OFDM techniques

s Benefits

QRobustness against multi-path channels

QMulti-user flexibility

QLow multi-access interference (MAI) in

downlink scenario

Time

Fre

quen

cy

User 1User 2

MC-CDMA

Introduction

MIMO techniques




Conclusion


Iterative receiver

Performance results

54

MIMO MC-CDMA Transmitter

Introduction

MIMO techniques




Conclusion


Iterative receiver

Performance results

55

s Equivalent channel matrix

s Receive signal

s Receiver algorithmQ Since S’ is a special LD code, proposed MMSE-IC receiver

can be used

Equivalent model

Desired signal MAI + CAI

termsnoise

Introduction

MIMO techniques




Conclusion


Iterative receiver

Performance results

56

s MMSE-IC (1) solution

s Full load approximation

Multi-user iterative receiver

Nu x Nu matrix inversion

TNr x TNr matrix inversions

Introduction

MIMO techniques




Conclusion


Iterative receiver

Performance results

Complexity of the equalization stage equivalent to the OFDM case

multi-user complexity: each user must be channel decoded

57

Performance results: 4x2 Bran E channel

s Bran E models Transmission parameters specified by the IST 4 MORE project for DL

transmission

Bit Interleaving depth 512/user for QPSK

1024/user for 16-QAM

FFT size 1024

Nc 695

CP size 256 samples

W 41.7 MHz

Fo 5 GHz

Velocity 16.6 m/s

Number of taps 12

Fs 50 MHz

No spatial correlation

Introduction

MIMO techniques




Conclusion


Iterative receiver

Performance results

58

s DA code s Alamouti AS

CAI + MAI terms MAI terms

Multi user MMSE-IC receiver Single user MMSE

receiver

Introduction

MIMO techniques




Conclusion


Iterative receiver

Performance results


59

DA + iterative receiver

Alamouti AS + SU MMSE receiver

Rayleigh

Bran E

Rayleigh

Bran E

Small degradation compared to Rayleigh i.i.d. channel

DA code outperforms Alamouti code

Perfect channel estimation

Introduction

MIMO techniques




Conclusion


Iterative receiver

Performance results


60

~ same impact whichever receiver is used

DA code still outperforms Alamouti AS code

Imperfect channel estimation:

Basic pilots aided algorithm with 1D interpolation (16% of pilots)

2.1 dB 1.9 dB

Introduction

MIMO techniques




Conclusion


Iterative receiver

Performance results


61

Synthesis

s MIMO MC-CDMA systems with iterative decodingQ Exploitation of MC-CDMA advantages and MIMO capacity

Q Multi-user algorithm complexity (each user must be individually decoded)

Q Equalization stage based on linear filters

Q Near-optimal performance no matter what the load

s Application to realistic channelsQ Small degradation compared to theoretical channel

Q Impact of channel estimation is satisfactory

Introduction

MIMO techniques




Conclusion

62

Part VII: Conclusion

Introduction

MIMO techniques




Conclusion

Major contributions Future prospectsGeneral conclusion Publications and patents

63

General conclusion

s MIMO capacity can be efficiently exploited by iterative

processing

s MMSE-IC based solutions lead to low complexity

algorithm (especially comparing to MAP based

solution)QHigh order modulations are suitable

QHigh number of antennas can be considered

s MMSE-IC receiver can be derived for MC-CDMA

transmission

s The behavior of MMSE-IC receiver over realistic

channel including channel estimation is satisfactory

Introduction

MIMO techniques




Conclusion


64

Major contributions

s Proposition and analysis of a MIMO iterative receiver QGeneric structure

QReduced complexity algorithms

QTheoretical analysis (complexity and asymptotical behavior)

s Proposition of new optimal LD codesQDTST

s Application of iterative reception QMC-CDMA

QLinear precoding

s Performance results QTheoretical channels

QRealistic channels (channel estimation and spatial correlation)

Introduction

MIMO techniques




Conclusion


65

Future prospects

s Iterative channel estimationQJoint channel estimation and decoding

s Turbo-codes instead of convolutional codes as channel

codingQMulti-loop iterative scheme

s Real channelsQRealistic spatial correlation model

s Application to OFDMA

s Implementation issues

Introduction

MIMO techniques




Conclusion


66

Publications and patents

s International ConferenceQP-J. Bouvet and M. Hélard, «Near optimal performance for high data rate MIMO MC-CDMA scheme», MC-SS 05

QB. Le Saux, M. Hélard and P-J. Bouvet, « Comparison of coherent and non-coherent space time schemes for frequency selective fast-varying channels », IEEE ISWCS 05

QP-J. Bouvet, M. Hélard and V. Le Nir, «Low complexity iterative receiver for linear precoded OFDM», IEEE WiMob 05

QP-J. Bouvet and M. Hélard, «Efficient iterative receiver for spatial multiplexed OFDM system over time and frequency selective channels», WWC 05

QP-J. Bouvet, M. Hélard and V. Le Nir, «Low complexity iterative receiver for non-orthogonal space-time block code with channel coding», IEEE VTC Fall 04

QP-J. Bouvet, V. Le Nir, M. Hélard and R. Le Gouable, «Spatial multiplexed coded MC-CDMA with iterative receiver» IEEE PIMRC 04

Introduction

MIMO techniques




Conclusion


67


s International Conference (cont’d)QP-J. Bouvet, M. Hélard and V. Le Nir, «Low complexity iterative receiver for linear precoded MIMO systems», IEEE ISSSTA 04

QM. Hélard, P-J. Bouvet, C. Langlais, Y. M. Morgan and I. Siaud, «On the performance of a Turbo Equalizer including Blind Equalizer over Time and Frequency Selective Channel. Comparison with an OFDM system», Symposium Turbo 03

QC. Langlais, P-J. Bouvet, M. Hélard and C. Laot, «Which Interleaver for turbo-equalization system on frequency and time selective channels for high order modulations ? », IEEE SPAWC 03

s National conferenceQB. Le Saux, M. Hélard and P.-J Bouvet, «Comparaison de technique MIMO cohérents et non-cohérentes sur canal rapide sélectif en fréquence», MajeSTIC 05

Introduction

MIMO techniques




Conclusion


68


s PatentsQP-J Bouvet and M. Hélard, « Procédé d’émission d’un signal ayant subi un précodage linéaire, procédé de réception, signal, dispositifs et programmes d’ordinateur correspondant », Nov. 05

QJ-P. Javaudin and P-J. Bouvet, «Procédé de codage d'un signal multiporteuse de type OFDM/OQAM utilisant des symboles à valeurs complexes, signal, dispositifs et programmes d'ordinateur correspondants», May 05

QJ-P. Javaudin and P-J. Bouvet, «Procédé de décodage itératif d'un signal OFDM/OQAM utilisant des symboles à valeurs complexes, dispositif et programme d'ordinateur correspondants», May 05

QP-J. Bouvet and M. Hélard, «Procédé de réception itératif d'un signal multiporteuse à annulation d'interférence, récepteur et programme d'ordinateur correspondants», March 05

Introduction

MIMO techniques




Conclusion


69


s Patents (cont’d)QP-J. Bouvet, M. Hélard and V. Le Nir, « Procédé de réception itératif pour système de type MIMO, récepteur et programme d'ordinateur correspondants », Nov. 04

QP-J. Bouvet, V. Le Nir and M. Hélard, « Procédé de réception d'un signal ayant subi un précodage linéaire et un codage de canal, dispositif de réception et produit programme d'ordinateur correspondants », Jun. 04

QM. Hélard, P-J. Bouvet, V. Le Nir and R. Le Gouable, « Procédé de décodage d'un signal codé à l'aide d'une matrice espace-temps, récepteur et procédé de codage et décodage correspondant », Sept. 03

Introduction

MIMO techniques




Conclusion


70

Questions

Questions?

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

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