Iterative decoding of convolutionally encoded signals over multipath Rayleigh fading channels

We analyze and compare several strategies for iteratively decoding trellis-encoded signals over channels with memory. Soft-in/soft-out extensions of reduced-complexity trellis search algorithms such as delayed decision-feedback sequence estimating (DDFSE) or parallel decision-feedback decoding (PDFD) algorithms are used instead of conventional BCJR and min-log-BCJR algorithms. It has been shown that for long channel impulse responses and/or high modulation orders where the BCJR algorithm becomes prohibitively complex, the proposed algorithms offer very good performance with low complexity. The problem of channel estimation in practical implementation of turbo detection schemes is studied in the second part. Two methods of channel reestimation are proposed: one based on the expectation-maximization (EM) algorithm and the second on a simple Bootstrap technique. Both algorithms are shown to dramatically improve the performance of the classical pseudo inverse channel estimation performed initially on a training sequence.