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 fashion. In this work, we propose a joint coding-modulation (JCM) 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 on image semantic communication validate the superiority of our proposed JCM framework over the state-of-the-art quantization-based digital semantic coding-modulation methods across a wide range of 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：语义通信作为一种通过传输源消息中接收端特定任务最相关的语义信息来提升通信效率的新范式已经兴起。现有方法通常利用神经网络设计端到端语义通信系统，其中基于神经网络的语义编码器输出连续分布的信号，以模拟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(JCM)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，通过联合设计编码与调制过程，可使所得调制策略匹配工作信道条件。我们还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验证了，所提出的JCM框架在多种信道条件、传输速率和调制阶数下均优于基于量化的最先进数字语义编码调制方法。此外，随着调制阶数增加，其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。