Soft-decision decoding of binary linear block codes using t-algorithm

This paper proposes a new and computationally efficient soft-decision decoding algorithm for binary linear block codes. It uses the tree representation of a systematically encoded block code. The tree representation is traversed through in a breadth-first fashion and it is adaptively pruned during the decoding using the following principle: if the metric difference between the best partial path and any other partial path at the same level is greater than a threshold value, then such a path is not extended any further. The threshold value is chosen based on a signal-to-noise ratio estimate. The efficiency of the pruning procedure is enhanced by constructing the tree so that the most reliable symbols are processed first. We show that practically optimum bit error rate performance in the additive white Gaussian noise channel can be efficiently achieved