RDJSCC: Robust Deep Joint Source-Channel Coding Enabled Distributed Image Transmision over Severe Fading Channel
Biao Dong, Wenkai Tian, Bin Qian Cao, Yu Wang · 2024
In this paper, we investigate the effects of severe channel fading in the scenario of distributed deep learning-based joint source-channel coding (DJSCC) for image transmission without perfect channel state information (CSI). To tackle the challenges posed by imperfect CSI, we propose a robust DJSCC (RDJSCC) scheme that operates at three levels: modulation, encoding, and decoding, respectively. Firstly, at the modulation level, we adopt orthogonal frequency division multiplexing (OFDM) modulation for exploring the tradeoff between reconstruction performance and peak-to-average power ratio (PAPR). Secondly, at the encoding level, two parameter-efficient operators are introduced to combat channel fading with low encoding complexity. Finally, at the decoding level, we divide the decoding process into two stages, i.e., denoising and recovery, aiming to maximize the correlation between the encoded representations. Theoretic analysis and simulation results show that our proposed RDJSCC can effectively alleviate the effects of severe fading with imperfect CSI, leading to an improved reconstruction performance while maintaining low PAPR and encoding complexity.