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

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 multistate 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.