Robust Deep Joint Source Channel Coding with Time-Varying Noise

Deep Joint Source-Channel Coding (JSCC) has gained increased attention, asserting its significance in the communication field. However, existing Deep JSCC techniques struggle to mitigate time-varying noise due to the deep neural networks being trained beforehand and fixed. To address this issue, we propose a robust deep JSCC scheme. Firstly, a multi-network parallel structure, as well as error-correcting codes, is introduced to effectively exploit label information. Secondly, a closed-form linear encoder and decoder pair is employed at the input and output ends of the channel to deal with the varying noise, which releases the neural network from dealing with a large range of varying noise levels. Thirdly, a transfer learning algorithm is utilized for estimating real-time noise statistics, which outperforms conventional estimation methods when noise statistics are time-dependent. These three components are effectively integrated as a comprehensive transmission system. Experimental results demonstrate that our optimized scheme outperforms existing approaches in the literature.