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:在无线通信中机械学习工具最近的成功激励下,Weaver自1949年以来的语义通信概念受到相当大的注意,它与香农典型设计范式的香农典型设计范式相违背,目的是传递信息的含义,即语义学,而不是其准确的副本,从而节省信息率。在这项工作中,我们从Basu et al. 将建模语义通信系统的基本方法从Basu et al. 推广到完整的通信Markov 链。因此,我们用隐藏随机变量来模拟语义通信,并将语义通信任务定义为通过一个通信频道对信息进行数据再修改和可靠传输,这样语义才能得到最好的保存。我们将此任务作为一个端到端的信息波特内克问题,允许压缩,同时最多保留相关信息。我们建议以ML为基础的语义通信系统SINFONY为解决方案,并将其用于分布式多点设想:SINFNY将不同发送者观察到的多条信息背后的含义定义为一个单一接收器,用于恢复语义性语言。我们把SINFONY的图像转换为Slimalalalal 图像,将Smargimalal dONY作为样本,将Slevyalalal viewdd viewd dresmal vidud viewd vidud vidud vidud vidudd vidud immmmmaged viewd viewd viagesmages vimmmages viaged vid vicald vical vicald vical vicild 。</s>