Error-trellises for convolutional codes-part II: Decoding methods

Soft-decision maximum-likelihood decoding of convolutional codes over GF(g) can be accomplished via searching through an error-trellis for the least weighing error sequence. The error-trellis is obtained by a syndrome-based construction. Its structure lends itself particularly well to the application of expedited search procedures. The method to carry out such error-trellis-based decoding is formulated by four algorithms. Three of these algorithms are aimed at reducing the worst case computational complexity, whereas by applying the fourth algorithm, the average computational complexity is reduced under low to moderate channel noise level. The syndrome decoder achieves substantial worst case and average computational gains in comparison with the conventional maximum-likelihood decoder, namely the Viterbi decoder, which searches for the most likely codeword directly within the code.