Exploring Parallel Processing Levels for Convolutional Turbo Decoding

In forward error correction, convolutional turbo codes were introduced to increase error correction capability approaching the Shannon bound. Decoding of these codes, however, is an iterative process requiring high computation rate and latency. Thus, in order to achieve high throughput and to reduce latency, crucial in emerging digital communication applications, parallel implementations become mandatory. In this paper, we explore the parallelism in convolutional turbo decoding with the BCJR algorithm and propose a multi-level classification of the explored parallelism techniques. We also present promising results on sub-block and component-decoder levels of parallelism. Sub-block parallelism results show that for sub-block initializations, message passing technique outperforms the acquisition approach. Furthermore, sub-block parallelism becomes quite inefficient in terms of speed gain for high sub-block parallelism degree. Conversely component-decoder parallelism efficiency, which only depends on interleaving rules, increases with sub-block parallelism degree