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.

翻译：受机器学习工具在无线通信领域近期成功的启发，Weaver于1949年提出的语义通信理念获得了广泛关注。该理念突破了Shannon经典设计范式，旨在传输消息的"含义"（即语义）而非其精确副本，从而实现了信息速率的节省。本研究将Basu等人提出的语义建模基础方法扩展至完整通信马尔可夫链。我们通过隐随机变量对语义进行建模，并将语义通信任务定义为：通过通信信道实现消息的数据精简与可靠传输，以最大限度地保持语义完整性。我们将该任务建模为端到端信息瓶颈问题，在保留最相关语义信息的同时实现压缩。作为解决方案，我们提出了基于机器学习的语义通信系统SINFONY，并将其应用于分布式多节点场景：SINFONY实现将不同发送端观测到的多个消息背后的语义传输至单一接收端进行语义恢复。我们通过图像处理作为消息示例对SINFONY进行分析。数值结果表明，与传统设计的通信系统相比，SINFONY可实现高达20 dB的归一化信噪比增益。