Iterative Joint Detection, Decoding, and Channel Estimation in Turbo Coded MIMO-OFDM GIGA SEMINAR ’08 Jari Ylioinas Outline Introduction System Model Iterative Receiver Soft MIMO Detector Channel Estimator Conclusions 11/27/08 CWC | Centre For Wireless Communications 2 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 CWC | Centre For Wireless Communications 3 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 CWC | Centre For Wireless Communications 4 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 CWC | Centre For Wireless Communications 5 System Model Source OFDM modulator Add S/P IFFT P/S Turbo Cyclic prefix Rayleigh encoder π MIMO MAPPER fading Add S/P IFFT P/S Cyclic prefix channel OFDM demodulator Remove Iterative P/S FFT S/P Cyclic prefix detection/ Sink decoding and channel Remove estimation P/S FFT S/P Cyclic prefix 4 Dec 2008 CWC | Centre For Wireless Communications 6 Iterative Receiver The optimal joint detector/decoder is approximated with iterative detection and decoding. The detected and decoded data is used in channel estimation. Channel Symbol estimator estimator LD1 LE1 LA2 OFDM LD2 OFDM demod. demod. Soft MIMO De- Turbo OFDM + demod. OFDM detector - interleaver decoder demod. Decoder iterations - Interleaver + Global LA1 LE2 iterations 12/1/08 CWC | Centre For Wireless Communications 7 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 Pilot based error rate target (FER) of 10 %.) 12/1/08 CWC | Centre For Wireless Communications 8 Soft MIMO Detector Channel Symbol estimator estimator LD1 LE1 LA2 OFDM LD2 OFDM demod. demod. Soft MIMO De- Turbo OFDM + demod. OFDM detector - interleaver decoder demod. - LA1 Interleaver + LE2 12/1/08 CWC | Centre For Wireless Communications 9 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 CWC | Centre For Wireless Communications 10 Soft MIMO Detector We derived a new list parallel interference cancellation (PIC) detector based on the space- alternating 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. List Detector OFDM OFDM demod. demod. OFDM List LLR demod. algorithm OFDM demod. LA1 12/1/08 CWC | Centre For Wireless Communications 11 Soft MIMO Detector Performance examples. MT=MR=2, 64QAM MT=MR=4, QPSK 12/3/08 CWC | Centre For Wireless Communications 12 Channel Estimator Channel Symbol estimator estimator LD1 LE1 LA2 OFDM LD2 OFDM demod. Soft MIMO De- Turbo demod. OFDM + demod. OFDM detector - interleaver decoder demod. - LA1 Interleaver + LE2 11/29/08 CWC | Centre For Wireless Communications 13 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 CWC | Centre For Wireless Communications 14 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 CWC | Centre For Wireless Communications 15 Channel Estimator Complexity and performance examples. Algorithm # complex # complex multiplications divisions LS 21418400 800 FD SAGE 186368 - GFD SAGE 840592 1200 TD SAGE 245880 120 MT=MR=2, L=10, K=512, MT=MR=4, 64QAM NI=3 (number of iterations) 12/3/08 CWC | Centre For Wireless Communications 16 Conclusions Iterative joint detection, decoding, and channel 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. 11/27/08 CWC | Centre For Wireless Communications 17 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!
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