Semantic Communication: An Information Bottleneck View

Motivated by recent success of machine learning tools at the PHY layer and driven by high bandwidth demands of the next wireless communication standard 6G, the old idea of semantic communication by Weaver from 1949 has received considerable attention. It breaks with the classic design paradigm according to Shannon by aiming to transmit the meaning of a message rather than its exact copy and thus potentially allows for savings in bandwidth. In this work, inspired by Weaver, we propose an information-theoretic framework where the semantic context is explicitly introduced into probabilistic models. In particular, for bandwidth efficient transmission, we define semantic communication system design as an Information Bottleneck optimization problem and consider important implementation aspects. Further, we uncover the restrictions of the classic 5G communication system design w.r.t. semantic context. Notably, based on the example of distributed image classification, we reveal the huge potential of a semantic communication system design. Numerical results show a tremendous saving in bandwidth of 20 dB with our proposed approach ISCNet compared to a classic PHY layer design.