Semantic communications have emerged as a new paradigm for improving communication efficiency by transmitting the semantic information of a source message that is most relevant to a desired task at the receiver. Most existing approaches typically utilize neural networks (NNs) to design end-to-end semantic communication systems, where NN-based semantic encoders output continuously distributed signals to be sent directly to the channel in an analog communication fashion. In this work, we propose a joint coding-modulation framework for digital semantic communications by using variational autoencoder (VAE). Our approach learns the transition probability from source data to discrete constellation symbols, thereby avoiding the non-differentiability problem of digital modulation. Meanwhile, by jointly designing the coding and modulation process together, we can match the obtained modulation strategy with the operating channel condition. We also derive a matching loss function with information-theoretic meaning for end-to-end training. Experiments conducted on image semantic communication validate that our proposed joint coding-modulation framework outperforms separate design of semantic coding and modulation under various channel conditions, transmission rates, and modulation orders. Furthermore, its performance gap to analog semantic communication reduces as the modulation order increases while enjoying the hardware implementation convenience.

翻译：语义通信通过传输与接收端目标任务最相关的信源语义信息，已成为提升通信效率的新范式。现有方法通常采用神经网络（NN）设计端到端语义通信系统，其中基于NN的语义编码器输出连续分布信号，以模拟通信方式直接发送至信道。本文提出一种基于变分自编码器（VAE）的数字语义通信联合编码调制框架。该方法学习从源数据到离散星座符号的转移概率，从而规避数字调制的不可微问题；同时通过联合设计编码调制过程，使所得调制策略与运行信道条件相匹配。我们推导出具有信息论意义的匹配损失函数用于端到端训练。在图像语义通信上的实验表明，所提联合编码调制框架在不同信道条件、传输速率和调制阶数下均优于语义编码与调制的独立设计方案。此外，随着调制阶数增加，该方法与模拟语义通信的性能差距逐渐缩小，同时兼具硬件实现便利性。