Stochastic Decoding of Turbo Codes

Abstract

Stochastic computation is a technique in which operations on probabilities are performed on random bit streams. Stochastic decoding of forward error-correction (FEC) codes is inspired by this technique. This paper extends the application of the stochastic decoding approach to the families of convolutional codes and turbo codes. It demonstrates that stochastic computation is a promising solution to improve the data throughput of turbo decoders with very simple implementations. Stochastic fully-parallel turbo decoders are shown to achieve the error correction performance of conventional a posteriori probability (APP) decoders. To our knowledge, this is the first stochastic turbo decoder which decodes a state-of-the-art turbo code. Additionally, an innovative systematic technique is proposed to cope with stochastic additions, responsible for the throughput bottleneck.