Task-Oriented Explainable Semantic Communications

doi:10.1109/TWC.2023.3269444

Task-Oriented Explainable Semantic Communications

2023-12-01

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AI

DOI：10.1109/TWC.2023.3269444

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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.

Keywords:

Semantics

Feature extraction

Wireless communication

Task analysis

Electronic mail

Data mining

Communication systems

Explainable semantic communications

feature selection

semantic communications prototype

Task-Oriented Explainable Semantic Communications

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