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.

翻译：受机器学习（ML）工具在无线通信领域近期成功的启发，韦弗于1949年提出的语义通信理念重新获得关注。该理念突破了香农经典设计范式，其目标在于传输消息的意义（即语义）而非精确版本，从而能够在信息速率上实现节省。本文中，我们将巴苏等人提出的语义建模基础方法扩展至完整的通信马尔可夫链。具体而言，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：在通信信道上进行数据精简且可靠的传输，以使语义得到最佳保留。我们将该任务表述为一个端到端的信息瓶颈问题，在保留最相关信息的前提下实现压缩。作为解决方案，我们提出基于机器学习的语义通信系统SINFONY，并将其应用于分布式多点场景：SINFONY将多个发送端观测到的不同消息背后的含义传输至单个接收端以实现语义恢复。我们以图像作为消息示例对SINFONY进行分析。数值结果表明，相较于经典设计的通信系统，该系统在归一化信噪比上可实现高达20 dB的显著增益。