Motivated by the recent success of Machine Learning (ML) tools in wireless
communications, the idea of semantic communication by Weaver from 1949 has
received considerable attention. It breaks with the classic design paradigm of
Shannon by aiming to transmit the meaning of a message, i.e., semantics, rather
than its exact copy 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 at 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.

本研究通过推广 Basu 等人的方法，将语义建模扩展至完整的通信马尔科夫链，提出了一种 ML-based 的语义通信系统 SINFONY，通过传输信息的同时进行压缩，有效提高了信息传输质量，尤其在多分发情况下，SNR 比传统通信系统提高了 20dB。