Maximum weight basis decoding of convolutional codes

We describe a new suboptimal decoding technique for linear codes based on the calculation of maximum weight basis of the code. The idea is based on estimating the maximum number locations in a codeword which have the least probability of estimation error without violating the codeword structure. In this paper we discuss the details of the algorithm for a convolutional code. The error correcting capability of the convolutional code increases with the constraint length of the code. Unfortunately the decoding complexity of Viterbi (1967) algorithm grows exponentially with the constraint length. We also augment the maximal weight basis algorithm by incorporating the ideas of list decoding technique. The complexity of the algorithm grows only quadratically with the constraint length and the performance of the algorithm is comparable to the optimal Viterbi decoding method