  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.
