A soft output decoding algorithm for concatenated systems

The reliability measure for a decoded symbol is the probability P _c that the symbol is correct or the probability of error P _e=1-P_c. Such quantities can be obtained by the symbol-by-symbol MAP (maximum a posteriori probability) algorithm. Unfortunately this algorithm is computationally inefficient. A soft output Viterbi algorithm (SOVA) can provide an estimate of P_e which is accurate only for large SNR. This paper proposes an efficient modified MAP algorithm for obtaining P_c for the outputs of convolutional inner decoders. The outer decoder uses P_c to perform soft decision decoding by choosing a codeword which maximizes the maximum likelihood (ML) metric. Decoding based on this ML metric is referred to as generalised soft decision decoding since it includes the Euclidean metric on AWGN channels and binary memoryless channels as special cases