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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 - PowerPoint PPT Presentation
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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
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
Conclusion
Time and frequency selective channel
Context MIMO transmission
Objectives
6
Multi-antenna (MIMO) transmissions
s PrincipleQMulti-antenna at transmitter and receiver
s MIMO capacity [Telatar 95]
Introduction
MIMO techniques
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
Conclusion
: covariance of
: rank of
: singular values
of SISO capacity
Context MIMO transmission
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
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
Conclusion
Context MIMO transmission
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
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
Conclusion
Context MIMO transmission
Objectives
9
Part II: MIMO techniques
Introduction
MIMO techniques
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
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
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
Conclusion
Classification LD code Equivalent representation CAITransmitter
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
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
Conclusion
Classification LD code Equivalent representation CAITransmitter
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
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
Conclusion
Classification LD code Equivalent representation CAITransmitter
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
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
Conclusion
Classification LD code Equivalent representation CAITransmitter
MIMO Channel
14
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
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
Conclusion
Classification LD code Equivalent representation CAITransmitter
MIMO Channel
15
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
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
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
Conclusion
Classification LD code Equivalent representation CAITransmitter
MIMO Channel
16
LD Code
STC latency:
Input block length:
STC rate:
Introduction
MIMO techniques
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
Conclusion
Classification LD code Equivalent representation CAITransmitter
MIMO Channel
17
Equivalent representation
Joint space-time coding and channel representation
Introduction
MIMO techniques
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
Conclusion
Classification LD code Equivalent representation CAITransmitter
MIMO Channel
18
Special LD Code
Examples
Introduction
MIMO techniques
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
Conclusion
Classification LD code Equivalent representation CAITransmitter
MIMO Channel
19
Solution
s Transmission matrices
s Reception matrices
s Equivalent channel matrix
Introduction
MIMO techniques
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
Conclusion
Classification LD code Equivalent representation CAITransmitter
MIMO Channel
20
Example: Alamouti Code over channel
s Transmission matrices
s Equivalent model
Introduction
MIMO techniques
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
Conclusion
Classification LD code Equivalent representation CAITransmitter
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
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
Conclusion
Classification LD code Equivalent representation CAITransmitter
MIMO Channel
22
Part III: Generic iterative receiver
Reception strategies
Principle
MIMO equalizer
Asymptotical analysis
Complexity analysis
Performance results
Introduction
MIMO techniques
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
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]
a.MAP MIMO detection
•[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
Near optimal performance
Very high complexity
Optimal performance
Reception strategies
Principle
MIMO equalizer
Asymptotical analysis
Complexity analysis
Performance results
Introduction
MIMO techniques
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
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
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]
a.MAP MIMO detection
•[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
Reception strategies
Principle
MIMO equalizer
Asymptotical analysis
Complexity analysis
Performance results
Introduction
MIMO techniques
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
Conclusion
reduced complexity
Near optimal performance
25
Principle
s Application of the turbo-equalization concept to MIMO
Channel decoding stageMIMO equalization stage
Reception strategies
Principle
MIMO equalizer
Asymptotical analysis
Complexity analysis
Performance results
Introduction
MIMO techniques
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
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
Reception strategies
Principle
MIMO equalizer
Asymptotical analysis
Complexity analysis
Performance results
Introduction
MIMO techniques
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
Conclusion
27
MIMO equalizer (2)
s Optimal solution: MMSE-IC
s Time invariant approximation: MMSE-IC(1)
Reception strategies
Principle
MIMO equalizer
Asymptotical analysis
Complexity analysis
Performance results
TNr x TNr matrix inversion
Introduction
MIMO techniques
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
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
Reception strategies
Principle
MIMO equalizer
Asymptotical analysis
Complexity analysis
Performance results
Introduction
MIMO techniques
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
Conclusion
29
Complexity analysis (MIMO equalizer)
Proposed iterative receivers provide complexity gain
Reception strategies
Principle
MIMO equalizer
Asymptotical analysis
Complexity analysis
Performance results
Introduction
MIMO techniques
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
Conclusion
30
Asymptotical analysis
s Asymptotical performances = Genie aided receiver
s Asymptotical equivalent channel
Reception strategies
Principle
MIMO equalizer
Asymptotical analysis
Complexity analysis
Performance results
Introduction
MIMO techniques
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
Conclusion
31
s Pair-wise error probability
s Chi-square approximation and Chernoff bound
Asymptotical diversity
Reception strategies
Principle
MIMO equalizer
Asymptotical analysis
Complexity analysis
Performance results
Introduction
MIMO techniques
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
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
Reception strategies
Principle
MIMO equalizer
Asymptotical analysis
Complexity analysis
Performance results
Introduction
MIMO techniques
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
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
Reception strategies
Principle
MIMO equalizer
Asymptotical analysis
Complexity analysis
Performance results
Introduction
MIMO techniques
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
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)
Reception strategies
Principle
MIMO equalizer
Asymptotical analysis
Complexity analysis
Performance results
Disjoint decodin
g
Iterative decodin
g
Introduction
MIMO techniques
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
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
Reception strategies
Principle
MIMO equalizer
Asymptotical analysis
Complexity analysis
Performance results
Disjoint decodin
g
Iterative decodin
g
Introduction
MIMO techniques
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
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
Reception strategies
Principle
MIMO equalizer
Asymptotical analysis
Complexity analysis
Performance results
Disjoint decodin
g
Iterative decodin
g
Introduction
MIMO techniques
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
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
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
Conclusion
38
Part IV: Optimal space-time coding
Optimality conditions DTST coding Performance results
Introduction
MIMO techniques
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
Conclusion
39
Optimality conditions
1. Maximizing data rate
2. Maximizing space-time coding diversity
3. Minimizing and
4. Minimizing the non orthogonal terms of
Optimality conditions DTST coding Performance results
Introduction
MIMO techniques
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
Conclusion
40
Optimality conditions
1. Maximizing data rate
2. Maximizing space-time coding diversity
3. Minimizing and
4. Minimizing the non orthogonal terms of
Optimality conditions DTST coding Performance results
Introduction
MIMO techniques
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
Conclusion
41
Maximizing data rate
s Ergodic Capacity
s High SNR approximation (Foschini et al. 96)
Optimality conditions DTST coding Performance results
Introduction
MIMO techniques
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
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”
Optimality conditions DTST coding Performance results
Introduction
MIMO techniques
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
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
Optimality conditions DTST coding Performance results
Introduction
MIMO techniques
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
Conclusion
44
Diagonal Threaded Space Time (DTST) coding
Optimality conditions DTST coding Performance results
Introduction
MIMO techniques
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
Conclusion
45
Example over a channel
Optimal with iterative decoding
Optimality conditions DTST coding Performance results
Introduction
MIMO techniques
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
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
Optimality conditions DTST coding Performance results
Introduction
MIMO techniques
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
Conclusion
47
System Parameters
Alamouti AS Double Alamouti (DA) Jafarkhani
DTST
Optimality conditions DTST coding Performance results
Introduction
MIMO techniques
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
Conclusion
48
Ergodic capacity
Near optimal exploitation for DA and DTST schemes
Optimality conditions DTST coding Performance results
Introduction
MIMO techniques
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
Conclusion
49
BER Performance
Best performance achieved with DTST (and DA)
Optimality conditions DTST coding Performance results
2 bps/Hz
Introduction
MIMO techniques
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
Conclusion
50
Capacity at BER=10-4
When increasing the spectral efficiency, only the iterative system is able to exploit the MIMO capacity
Optimality conditions DTST coding Performance results
Introduction
MIMO techniques
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
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
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
Conclusion
52
Part V: Application to MC-CDMA
Introduction
MIMO techniques
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
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
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
Conclusion
MC-CDMA Transmitter Equivalent model
Iterative receiver
Performance results
54
MIMO MC-CDMA Transmitter
Introduction
MIMO techniques
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
Conclusion
MC-CDMA Transmitter Equivalent model
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
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
Conclusion
MC-CDMA Transmitter Equivalent model
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
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
Conclusion
MC-CDMA Transmitter Equivalent model
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
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
Conclusion
MC-CDMA Transmitter Equivalent model
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
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
Conclusion
MC-CDMA Transmitter Equivalent model
Iterative receiver
Performance results
Performance results: 4x2 Bran E channel
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
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
Conclusion
MC-CDMA Transmitter Equivalent model
Iterative receiver
Performance results
Performance results: 4x2 Bran E channel
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
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
Conclusion
MC-CDMA Transmitter Equivalent model
Iterative receiver
Performance results
Performance results: 4x2 Bran E channel
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
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
Conclusion
62
Part VII: Conclusion
Introduction
MIMO techniques
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
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
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
Conclusion
Major contributions Future prospectsGeneral conclusion Publications and patents
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
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
Conclusion
Major contributions Future prospectsGeneral conclusion Publications and patents
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
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
Conclusion
Major contributions Future prospectsGeneral conclusion Publications and patents
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
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
Conclusion
Major contributions Future prospectsGeneral conclusion Publications and patents
67
Publications and patents
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
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
Conclusion
Major contributions Future prospectsGeneral conclusion Publications and patents
68
Publications and patents
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
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
Conclusion
Major contributions Future prospectsGeneral conclusion Publications and patents
69
Publications and patents
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
Generic iterative receiver
Optimal space-time coding
Application to MC-CDMA
Conclusion
Major contributions Future prospectsGeneral conclusion Publications and patents
70
Questions
Questions?