  Semantic communications utilize the transceiver computing resources to
alleviate scarce transmission resources, such as bandwidth and energy. Although
the conventional deep learning (DL) based designs may achieve certain
transmission efficiency, the uninterpretability issue of extracted features is
the major challenge in the development of semantic communications. In this
paper, we propose an explainable and robust semantic communication framework by
incorporating the well-established bit-level communication system, which not
only extracts and disentangles features into independent and semantically
interpretable features, but also only selects task-relevant features for
transmission, instead of all extracted features. Based on this framework, we
derive the optimal input for rate-distortion-perception theory, and derive both
lower and upper bounds on the semantic channel capacity. Furthermore, based on
the $\beta $-variational autoencoder ($\beta $-VAE), we propose a practical
explainable semantic communication system design, which simultaneously achieves
semantic features selection and is robust against semantic channel noise. We
further design a real-time wireless mobile semantic communication
proof-of-concept prototype. Our simulations and experiments demonstrate that
our proposed explainable semantic communications system can significantly
improve transmission efficiency, and also verify the effectiveness of our
proposed robust semantic transmission scheme.
