Efficient joint source-channel decoding of multi-state Markov sequences

Abstract

In this study, joint source-channel decoding for non-binary source samples is conducted. The non-binary source samples can be modelled as the output of a multi-state Markov chain (MC). As the source samples are directly transmitted after channel coding without source compression, the transmitted signals can be highly correlated. At the receiver, the multi-state MC module can be designed to exploit the statistical correlation of source samples to improve the error correcting performance. However, as the number of states is increased, the multi-state MC module requires high computational complexity. To alleviate this problem, a simplified MC module is proposed. In the simplified MC module, the multi-state MC is replaced with multiple number of two-state MCs each of which exploits bit-level correlation of samples. Simulation results demonstrate that the simplified MC module can lead to competitive reduction in the required signal-to-noise ratio in comparison with the multi-state MC module with reduced computational complexity.