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Iterative Joint Detection, Decoding, and Channel Estimation in Turbo Coded MIMO-OFDM GIGA SEMINAR ’08 Jari Ylioinas

Outline            

11/27/08

Introduction System Model Iterative Receiver Soft MIMO Detector Channel Estimator Conclusions

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Introduction   Orthogonal frequency division multiplexing (OFDM)   Divides the frequency selective fading channel into many parallel flat fading sub-channels.

  Simplifies the receiver design (usually, no need for time domain equalization.)

⇒ An attractive air interface for high-rate

communication systems with large bandwidths.

12/2/08

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Introduction

  Multiple-input multiple-output (MIMO) channels offer improved capacity and potential for improved reliability compared to single-input single-output (SISO) channels.

  Combining a MIMO processing with OFDM is

identified as a promising approach for future communication systems.

11/29/08

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Introduction   Iterative joint detection, decoding, and channel estimation is considered.   Iterative joint detection and decoding approximates the optimal joint detector/ decoder.   Taking channel estimation within the joint iterative processing improves spectral efficiency since the pilot overhead can be reduced.

12/1/08

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System Model Source


Turbo
 encoder


OFDM
modulator


π


S/P


IFFT


P/S


Add

 Cyclic
prefix


S/P


IFFT


P/S


Add

 Cyclic
prefix


MIMO
 MAPPER


OFDM
demodulator
 Sink


4 Dec 2008

Iterative
 detection/

 decoding
 and
channel
 estimation


P/S


FFT


S/P


Remove

 Cyclic
prefix


P/S


FFT


S/P


Remove

 Cyclic
prefix


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Rayleigh

 fading
 channel


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Iterative Receiver   The optimal joint detector/decoder is

approximated with iterative detection and decoding.   The detected and decoded data is used in channel estimation. Symbol
 estimator


Channel
 estimator
 OFDM
 OFDM
 demod.
 demod.
 OFDM
 demod.
 OFDM
 demod.


LD1

Soft
MIMO
 detector


LE1 + -

De-
 interleaver


Turbo

 decoder


LD2

Decoder
 iterations
 LA1

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LA2

Interleaver


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+ LE2

Global

 iterations


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Iterative Receiver   Motivation of the receiver

structure.   The spectral efficiency can be increased.   If ~0.8 dB higher SNR value is allowed, the pilot overhead can be decreased from 16.7 % to 0.5 %. (Assuming frame error rate target (FER) of 10 %.)

12/1/08

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Pilot based

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Soft MIMO Detector

Symbol
 estimator


Channel
 estimator
 OFDM
 OFDM
 demod.
 demod.
 OFDM
 demod.
 OFDM
 demod.


LD1

Soft
MIMO
 detector


LE1 + -

LA1

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LA2

De-
 interleaver


Interleaver


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Turbo

 decoder


LD2

+ LE2

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Soft MIMO Detector   A posteriori probability (APP) algorithm is the

optimal soft MIMO Detector.   Calculates the Euclidean distance of every possible candidate symbol vector and uses them in log-likelihood ratio (LLR) calculation.   Computationally too intensive in many cases.

  List detectors approximate the APP algorithm by

forming a candidate list which should include the most probable candidate symbol vectors.   In many cases based on the QR decomposition (QRD) of the channel matrix and tree search algorithms.

11/29/08

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Soft MIMO Detector   We derived a new list parallel interference

cancellation (PIC) detector based on the spacealternating generalized expectation-maximization (SAGE) detector.   Uses breadth-first search scheme.   Good in the implementation point of view.

  Shows good performance in 2 x 2 antenna

configuration.   We proposed list re-calculation in iterative detection and decoding. OFDM
 OFDM
 demod.
 demod.
 OFDM
 demod.
 OFDM
 demod.


List Detector

List algorithm

LLR

LA1 12/1/08

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Soft MIMO Detector   Performance examples.

MT=MR=2, 64QAM

12/3/08

MT=MR=4, QPSK

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Channel Estimator

Symbol
 estimator


Channel
 estimator
 OFDM
 OFDM
 demod.
 demod.
 OFDM
 demod.
 OFDM
 demod.


LD1

Soft
MIMO
 detector


LE1 + -

LA1

LA2

De-
 interleaver


Interleaver


Turbo

 decoder


LD2

+

LE2

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Channel Estimator   The least-squares (LS) estimation is the best

linear unbiased estimator for Gaussian noise.   However, in decision directed (DD) mode of operation a matrix inversion is required.   The frequency domain (FD) SAGE algorithm [Xie et al. IEEE Trans. Comm.]   Converts iteratively the LS estimation of MIMO channel into multiple SISO channel estimation problems (avoids matrix inversion).   With non-constant envelope constellations, it starts to lose to the LS estimation.

11/29/08

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Channel Estimator   We generalized the FD SAGE channel estimator for

non-constant envelope constellations.   The drawback with generalized FD SAGE is the required matrix inversion.   However, the size of the matrix to be inverted is smaller than that of with the LS estimator.   We derived the time domain (TD) SAGE channel estimator.   Avoids the matrix inversion without performance degradation with non-constant envelope constellation.

12/3/08

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Channel Estimator   Complexity and performance examples.

Algorithm

# complex multiplications

# complex divisions

LS

21418400

800

FD SAGE

186368

-

GFD SAGE

840592

1200

TD SAGE

245880

120

MT=MR=2, L=10, K=512, NI=3 (number of iterations)

12/3/08

MT=MR=4, 64QAM

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Conclusions   Iterative joint detection, decoding, and channel      

 

11/27/08

estimation was considered in MIMO-OFDM system. A new list PIC detector was discussed which gives nice performance in 2 x 2 antenna configuration. List re-calculation was presented as a way to speed up the convergence in iterative detection decoding. The time domain SAGE channel estimator was shown to solve the problem of the FD SAGE channel estimator with non-constant envelope constellations. The iterative receiver was shown to improve the spectral efficiency remarkably.

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Literature: J. Ylioinas, M.R. Raghavendra, M. Juntti ” Avoiding Matrix Inversion in DD SAGE Channel Estimation in MIMO-OFDM with M-QAM”, IEEE Signal Processing Letters, submitted J. Ylioinas, M. Juntti ”Iterative Joint Detection, Decoding, and Channel Estimation in Turbo Coded MIMO-OFDM”, IEEE Transactions on Vehicular Technology, In press.

Questions? Thank You!