Semantic Communication: A Survey on Research Landscape, Challenges, and Future Directions

Abstract

Amid the global rollout of fifth-generation (5G) services, researchers in academia, industry, and national laboratories have been developing proposals for the sixth-generation (6G), whose materialization is fraught with many fundamental challenges. To alleviate these challenges, a deep learning (DL)-enabled semantic communication (SemCom) has emerged as a promising 6G technology enabler, which embodies a paradigm shift that can change the status quo viewpoint that wireless connectivity is an opaque data pipe carrying messages whose context-dependent meanings have been ignored. Since 6G is also critical for the materialization of major SemCom use cases, the paradigms of 6G for SemCom and SemCom for 6G call for a tighter integration of 6G and SemCom. For this purpose, this comprehensive article provides the fundamentals of semantic information, semantic representation, and semantic entropy; details the state-of-the-art SemCom research landscape; presents the major SemCom trends and use cases; discusses current SemCom theories; exposes the fundamental and major challenges of SemCom; and offers future research directions for SemCom. We hope this article stimulates many lines of research on SemCom theories, algorithms, and implementation.