Modified multiple stack algorithm for decoding convolutional codes

The multiple stack algorithm (MSA), devised by Chevillat and Costello, is an efficient algorithm for erasurefree decoding of long constraint length convolutional codes. rn the MSA, substack size and the number of transferred survivors (or successors) are assumed to be small. Lower error probabilities can be achieved by increasing the first stack size and/or increasing the computational limit. A large storage capacity for survivors is required to prevent memory overflow and achieve a low error probability. The authors present a modified MSA, in which the storage capacity for survivors is kept as a constant, while the substacks are arranged in a ring-like structure to handle the overflow problem of storage for survivors. In addition, the substack size and the number of transferred survivors are made large to improve performance. The performance of the modified MSA in decoding a convolutional code with constraint length m = 23 is investigated and compared with the performance unmodified MSA.