Low-complexity high-throughput decoding architecture for convolutional codes

Abstract

Sequential decoding can achieve a very low computational complexity and short decoding delay when the signalto-noise ratio (SNR) is relatively high. In this article, a low-complexity high-throughput decoding architecture based on a sequential decoding algorithm is proposed for convolutional codes. Parallel Fano decoders are scheduled to the codewords in parallel input buffers according to buffer occupancy, so that the processing capabilities of the Fano decoders can be fully utilized, resulting in high decoding throughput. A discrete time Markov chain (DTMC) model is proposed to analyze the decoding architecture. The relationship between the input data rate, the clock speed of the decoder and the input buffer size can be easily established via the DTMC model. Different scheduling schemes and decoding modes are proposed and compared. The novel high-throughput decoding architecture is shown to incur 3-10% of the computational complexity of Viterbi decoding at a relatively high SNR.