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TURBO - CODING - 2006 4 International Symposium on Turbo Codes & Related Topics 6 International ITG-Conference on Source and Channel Coding EM BASED MAP ITERATIVE CHANNEL ESTIMATION FOR TURBO CODED SFBC-OFDM SYSTEMS Hakan Doğan, Hakan Ali Çırpan, Erdal Panayırcı Method MAP Expectation/Maximization (EM) channel estimation algorithm proposed for SFBC-Turbo coded-OFDM and SFBC-Convolutionally codedOFDM sytems. Principles MAP-EM employs iterative channel estimation and it improves receiver performance by re-estimating the channel after each decoder iteration. Advantages MAP-EM approach considers the channel variations as random processes and applies the Karhunen-Loeve (KL) orthogonal series expansion. Complexity reduction by using optimal truncation property. Investigation The performance merits of the iterative channel estimator Sensitivity of Turbo codes on channel estimation errors. The aim of this study is The design of turbo receiver structures for space-frequency block coded (SFBC-) OFDM systems in unknown frequency selective fading channels. İstanbul University Bilkent University Electirical&Electronics Engineering How it Works First iteration EM based channel estimator computes channel gains according to pilot symbols Output of channel estimator is used SFBC demodulator Equalized symbol sequence is passed through a channel interleaver and MAP decoder module LLR of coded and uncoded bits are yielded Next iteration LLRs of coded bits are reinterleaved and passed through a nonlinearity (soft values calculated) MAP-EM channel estimator iteratively estimate channel by taking received signal and interleaved soft value of transmitted symbols which are computed bu outher channel decoder in the previous iteration. Tx1 b( n) Channel C( n ) X( n) SFBC Encoder Encoder X 0 ( n) X * ( n) 1 X ( n ) Nc 2 * X N ( n) c 1 O F D M X 1 ( n) X * ( n) 0 X N c 1 ( n ) * X Nc 2 ( n) O F D M Tx2 Received Signal Model R e (n) e (n)H1,e (n) o (n)H 2,e (n) We (n) R o (n) o† (n)H1,o (n) e† (n) H 2,o ( n) Wo ( n) Channel Model CH E GG † Signal Model for Channel estimation R e (n) e (n) o (n) H1,e (n) We (n) R (n) † (n) † (n) H (n) W (n) e o o 2 ,e o R(n) (n)H(n) W(n) R1 ( n) RNc 2 ( n) RNc 1 ( n) R0 ( n) Channel Estimation & SFBC OFDM Demodulator bˆ i (n) Paralel D(n) -1 -to- Z( n) Decoder Serial ˆ (n) X MAP nonlinear function j (n) EM Based Channel Estimation ˆ G MAP arg max p(G R) G Expectation Step p R G E p R , G H (n) G (n) † Rx ˆ G MAP arg max p(R G) p G G Maximization Step Taking derivatives with respect to G ˆ ( n) G1q 1 1 † ˆ e†( q ) R e (n) ˆ o ( q ) R o ( n) G q2 1 1 † †( q ) o R e (n) ˆ e ( q ) R o The optimal truncation property of KL reduces the amount of information required to represent statistically dependent channel frequency response to minimum. Thus this property can further reduce computational load on the channel estimation algorithm. if the number of parameters in the expansion include dominant eigen values (Rank=8), we are able to obtain a good approximation with a relatively small number of KL coefficients. For instance, by replacing 256 x256 diagonal with 8x8 diagonal r decreases computational complexity significiantly. Simulations Conclusion Low Comlexity Turbo receiver structure was proposed for SFBC-Turbo coded-OFDM in the case of MAP-EM Channel estimator. Turbo coded receiver structure more sensitive to channel estimation errors than convolutional coded receiver structure was shown.