Deep Joint Source-Channel Coding for Semantic Communications

Abstract

Semantic communications is considered a promising technology that will increase the efficiency of next-generation communication systems, particularly human-machine and machine-type communications. In contrast to the source-agnostic approach of conventional wireless communication systems, semantic communications seek to ensure that only relevant information for the underlying task is communicated to the receiver. Considering 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 made recently over existing separate source and channel coding systems, particularly in low-la-tency and low-power scenarios. Recent progress has been made 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 the result 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.