A Decision-Feedback Maximum-Likelihood Decoder for Fading Channels

We propose a novel decision-feedback maximum-likelihood decoder for fading channels. These channels have memory due to the fading correlation, and the complexity of maximum-likelihood decoding for such channels grows exponentially with memory length. Therefore, in practice, the encoded bit stream is typically interleaved prior to transmission and the deinterleaved bit stream is decoded as for an AWGN channel, independent of the fading statistics. Our decision-feedback decoding algorithm uses a suucient statistic for past outputs which is computed recursively based on past channel outputs and bit decisions. Using this statistic, the maximum-likelihood input sequence is determined on a symbol-by-symbol basis, with complexity independent of the channel memory. In Rayleigh fading our decoding algorithm decreases BER by up to three orders of magnitude compared to the conventional technique. We also study another similar decoding algorithm which recursively computes the suucient statistic based only on past outputs. The decoder performance using either statistic is roughly equivalent, although the decision-feedback decoder performs slightly worse on poor channels due to error propagation. BER simulation results for our decoding techniques are presented for several diierent fading models and modulation types. Based on the extremely poor performance of the conventional decoder, we propose a simple improvement to conventional decoding which uses channel weighting. The channel weighting reduces the BER of the conventional decoder by more than an order of magnitude in all cases.