A multiprocessor architecture for multiple path stack sequential decoders

Abstract

The Zigangirov-Jelinek (stack) algorithm allows decoding convolutional codes with a small computational effort compared to the optimum Viterbi algorithm. However, it suffers from a variability of that computational effort that is highly undesirable. The paper describes an architecture that implements a multiple-path-like stack algorithm for reducing this variability. This architecture is organized as a linear structure comprising special processors for extending tree nodes, called extenders, and priority stacks for storing nodes in sorted metric order. The architecture is shown to have a good potential for reducing the computational variability without adding much overhead to the system. Simulations have shown that this architecture effectively reduces computational variability as the number of processors increases, even for a relatively large number of extenders. Simulations run for up to 16 extenders have also shown that using 4 to 16 extenders is a good choice. The architecture is also shown to reduce computational variability like the multiple path algorithm does, while having a better time performance. >