In this paper, we propose a novel joint source-channel coding (JSCC) approach for channel-adaptive digital semantic communications. In semantic communication systems with digital modulation and demodulation, robust design of JSCC encoder and decoder becomes challenging not only due to the unpredictable dynamics of channel conditions but also due to diverse modulation orders. 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 and modulation orders. 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. We also develop a channel-adaptive modulation technique for an inference phase, in order to reduce the communication latency while maintaining task performance. In this technique, we adaptively determine modulation orders for the latent variables based on channel conditions. Using simulations, we demonstrate the superior performance of the proposed JSCC approach for image classification, reconstruction, and retrieval tasks compared to existing JSCC approaches.

翻译：本文提出一种新型联合信源信道编码（JSCC）方法，用于信道自适应的数字语义通信。在采用数字调制解调方式的语义通信系统中，JSCC编解码器的鲁棒性设计面临双重挑战：不仅需要应对信道条件的不可预测动态变化，还需适配多样化的调制阶数。为应对该挑战，我们首先提出一种新的解调方法，通过评估解调输出的不确定性来提升数字语义通信系统的鲁棒性。随后设计鲁棒训练策略，增强JSCC编解码器对不同信道条件与调制阶数的适应性与灵活性。为此，我们采用二元对称擦除信道建模编码器输出与解码器输入间的映射关系，并从异构分布中采样信道参数。同时提出面向推理阶段的信道自适应调制技术，在维持任务性能的前提下降低通信时延。该技术根据信道条件自适应确定潜在变量的调制阶数。仿真结果表明，在图像分类、重建与检索任务中，所提JSCC方法相比现有方案具有显著性能优势。