Semantic communications is considered as a promising technology to increase the efficiency of next-generation communication systems, particularly targeting human-machine and machine-type communications. In contrast to the source-agnostic approach of conventional wireless communication systems, semantic communication seeks to ensure that only the relevant information for the underlying task is communicated to the receiver. Considering that most semantic communication applications have strict latency, bandwidth, and power constraints, a prominent approach is to model them as a joint source-channel coding (JSCC) problem. Although JSCC has been a long-standing open problem in communication and coding theory, remarkable performance gains have been shown recently over existing separate source and channel coding systems, particularly in low-latency and low-power scenarios. Recent progress is thanks to the adoption of deep learning techniques for joint source-channel code design that outperform the concatenation of state-of-the-art compression and channel coding schemes, which are results of decades-long research efforts. In this article, we present an adaptive deep learning based JSCC (DeepJSCC) architecture for semantic communications, introduce its design principles, highlight its benefits, and outline future research challenges that lie ahead.

翻译：语义通信被认为是提升下一代通信系统效率的前景技术，尤其针对人机通信与机器类通信场景。与传统无线通信系统中忽视信源特性的方法不同，语义通信致力于仅传输与底层任务相关的必要信息至接收端。考虑到多数语义通信应用具有严格的时延、带宽和功耗约束，主流方法将其建模为联合信源信道编码问题。尽管联合信源信道编码是通信与编码理论中长期存在的未决难题，但近年来已有研究表明，其在现有分离式信源信道编码系统基础上展现出显著性能优势，尤其适用于低时延低功耗场景。这一进展得益于将深度学习技术应用于联合信源信道编码设计，其性能超越了融合最先进压缩与信道编码方案的级联系统——后者凝聚了数十年的研究成果。本文提出一种基于自适应深度学习的语义通信联合信源信道编码架构，阐述其设计原理，突出其优势，并展望未来面临的研究挑战。