TURBO - CODING - 2006 4 International Symposium on Turbo Codes & Related Topics

1. 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 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. • Method • MAP Expectation/Maximization (EM) channel estimation algorithm proposed for SFBC-Turbo coded-OFDM and SFBC-Convolutionally coded-OFDM 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. İstanbul University Bilkent University Electirical&Electronics Engineering

2. Rx O F D M Paralel -to- Serial Channel Estimation & SFBC OFDM Demodulator MAP Decoder -1 SFBC Encoder Tx2 Channel Encoder  O F D M nonlinear function  • 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 Received Signal Model EM Based Channel Estimation Expectation Step Channel Model Maximization Step Signal Model for Channel estimation

3. The optimal truncation property of KL reduces the amount ofinformation required to represent statistically dependent channel frequency response to minimum. Thus this property can further reduce computational load onthe channel estimationalgorithm. if the number of parameters in theexpansion include dominant eigenvalues (Rank=8), we are able toobtain a good approximation with a relatively small number of KLcoefficients. For instance, by replacing 256x256 diagonal with 8x8 diagonaldecreases 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.