Coupled Decoding of Block-Convolutional Concatenated Codes

Straightforward implementation of a maximum likelihood decoder implies a complexity that grows algebraically with the inverse of error probability. Forney has suggested an approach, concatenation, for which error probability decreases exponentially with increasing complexity. This paper presents the results of an evaluation of a particular concatenation system, structurally similar to the hybrid system of Falconer, employing a Reed-Solomon outer code and an inner convolutional code. The inner decoder is a Viterbi decoder of constraint length less than the corresponding encoding constraint length (nonmaximum likelihood). The outer decoder assumes one of three possible forms, all employing the likelihood information developed by the inner decoder to assist in outer decoding. Error corrections and erasure fill-ins achieved by the outer decoder are fed back to the inner decoder. Performance is evaluated through computer simulation. The three outer decoders are found to provide approximately the same performance, all yielding low error probabilities at rates somewhat above Rcompof sequential decoding and at signal energy to noise density ratios per information bit around 1.7 dB.