Parallel-trellis turbo equalizers for sparse-coded transmission over sparse multipath channels

A novel coding and turbo equalization scheme for sparse multipath channels is presented in this paper. A burst-error-correcting convolutional code, which coding and interleaving via a sparse encoder and a convolutional interleaver, is selected as the channel code. A previously-proposed parallel-trellis framework is adopted for implementing the maximum a posteriori (MAP) equalizer and decoder in the turbo equalizer. Such a system exhibits a similar level of performance to a conventional system using a random-error-correcting convolutional code together with a row /column (R/C) or random block interleaver. Yet, the proposed system has the advantages of low latency and low memory requirements when compared to those conventional systems. This is due to (i) the elimination of the block-type interleavers and deinterleavers and (ii) the parallelism achieved in the turbo equalizer. One disadvantage of the proposed system is to decrease in throughput, as more tail symbols are required for proper termination of all sub-trellises in the decoder. Extension of the proposed system to turbo codes is considered. Also, the effect of prefiltering on a nonminimum-phase, high definition television (HDTV) channel is examined. Results indicate that using the feedforward filter (FFF) of a nonuniformly-spaced decision feedback equalizer (NU-DFE) performs almost as good as using one with a large number of uniformly-spaced taps. However, far fewer computations are needed to find the optimum tap values of the sparse prefilter.