Reference : Bayesian equalization for LDPC channel decoding
Scientific journals : Article
Engineering, computing & technology : Electrical & electronics engineering
http://hdl.handle.net/10993/19026
Bayesian equalization for LDPC channel decoding
English
Salamanca Mino, Luis mailto [University of Sevilla > Signal Theory and communications]
Murillo-Fuentes, Juan José mailto [University of Sevilla > Signal theory and communications]
Pérez-Cruz, Fernando mailto [Universidad Carlos III de Madrid > Signal theory and communications]
5-May-2012
IEEE Transactions on Signal Processing
60
5
2672-2676
Yes
International
1053587X
[en] Bayesian inference ; BCJR algorithm ; channel equalization ; expectation propagation ; fading channels ; intersymbol interference ; LDPC coding
[en] We describe the channel equalization problem, and its prior estimate of the channel state information (CSI), as a joint Bayesian estimation problem to improve each symbol posterior estimates at the input of the channel decoder. Our approach takes into consideration not only the uncertainty due to the noise in the channel, but also the uncertainty in the CSI estimate. However, this solution cannot be computed in linear time, because it depends on all the transmitted symbols. Hence, we also put forward an approximation for each symbol's posterior, using the expectation propagation algorithm, which is optimal from the Kullback-Leibler divergence viewpoint and yields an equalization with a complexity identical to the BCJR algorithm. We also use a graphical model representation of the full posterior, in which the proposed approximation can be readily understood. The proposed posterior estimates are more accurate than those computed using the ML estimate for the CSI. In order to illustrate this point, we measure the error rate at the output of a low-density parity-check decoder, which needs the exact posterior for each symbol to detect the incoming word and it is sensitive to a mismatch in those posterior estimates. For example, for QPSK modulation and a channel with three taps, we can expect gains over 0.5 dB with same computational complexity as the ML receiver.
http://hdl.handle.net/10993/19026
10.1109/TSP.2012.2184098

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