Joint Source-Channel Coding for Robust Digital Semantic Communications
Joohyuk Park, Yongjeong Oh, Seonjung Kim, Yo–Seb Jeon · 2024
This paper proposes a novel joint source-channel coding (JSCC) approach for robust digital semantic communications. When employing a binary-output JSCC encoder with digital modulation, end-to-end training becomes challenging due to the unpredictable dynamics of channel conditions. To address this challenge, we first develop a new demodulation method which assesses the uncertainty of the demodulation output to improve the robustness of the digital semantic communication system. We then devise a robust training strategy which enhances the robustness and flexibility of the JSCC encoder and decoder against diverse channel conditions. To this end, we model the relationship between the encoder’s output and decoder’s input using binary symmetric erasure channels and then sample the parameters of these channels from diverse distributions. Using simulations, we demonstrate the superior performance of the proposed JSCC approach for image classification and reconstruction tasks compared to existing JSCC approaches.