  Motivated by the recent success of Machine Learning (ML) tools in wireless
communications, the idea of semantic communication by Weaver from 1949 has
gained attention. It breaks with Shannon's classic design paradigm by aiming to
transmit the meaning of a message, i.e., semantics, rather than its exact
version and thus allows for savings in information rate. In this work, we
extend the fundamental approach from Basu et al. for modeling semantics to the
complete communications Markov chain. Thus, we model semantics by means of
hidden random variables and define the semantic communication task as the
data-reduced and reliable transmission of messages over a communication channel
such that semantics is best preserved. We cast this task as an end-to-end
Information Bottleneck problem, allowing for compression while preserving
relevant information most. As a solution approach, we propose the ML-based
semantic communication system SINFONY and use it for a distributed multipoint
scenario: SINFONY communicates the meaning behind multiple messages that are
observed at different senders to a single receiver for semantic recovery. We
analyze SINFONY by processing images as message examples. Numerical results
reveal a tremendous rate-normalized SNR shift up to 20 dB compared to
classically designed communication systems.
