What Is Semantic Communication?A View on Conveying Meaning in the Era of Machine Intelligence

In the 1940s,Claude Shannon developed the information theory focusing on quantifying the maximum data rate that can be supported by a communication channel.Guided by this fundamental work,the main theme of wireless system design up until the fifth generation(5G)was the data rate maximization.In Shannon’s theory,the semantic aspect and meaning of messages were treated as largely irrelevant to communication.The classic theory started to reveal its limitations in the modern era of machine intelligence,consisting of the synergy between Internet-of-things(IoT)and artificial intelligence(AI).By broadening the scope of the classic communication-theoretic framework,in this article,we present a view of semantic communication(SemCom)and conveying meaning through the communication systems.We address three communication modalities:human-to-human(H2H),human-to-machine(H2M),and machine-to-machine(M2M)communications.The latter two represent the paradigm shift in communication and computing,and define the main theme of this article.H2M SemCom refers to semantic techniques for conveying meanings understandable not only by humans but also by machines so that they can have interaction and“dialogue”.On the other hand,M2M SemCom refers to effective techniques for efficiently connecting multiple machines such that they can effectively execute a specific computation task in a wireless network.The first part of this article focuses on introducing the SemCom principles including encoding,layered system architecture,and two design approaches:1)layer-coupling design;and 2)end-to-end design using a neural network.The second part focuses on the discussion of specific techniques for different application areas of H2M SemCom[including human and AI symbiosis,recommendation,human sensing and care,and virtual reality(VR)/augmented reality(AR)]and M2M SemCom(including distributed learning,split inference,distributed consensus,and machine-vision cameras).Finally,we discuss the approach for designing SemCom systems based on knowledge graphs.We believe that this comprehensive introduction will provide a useful guide into the emerging area of SemCom that is expected to play an important role in sixth generation(6G)featuring connected intelligence and integrated sensing,computing,communication,and control.

An Overview of Data-Importance Aware Radio Resource Management for Edge Machine Learning

The 5G network connecting billions of Internet of things(IoT)devices will make it possible to harvest an enormous amount of real-time mobile data.Furthermore,the 5G virtualization architecture will enable cloud computing at the(network)edge.The availability of both rich data and computation power at the edge has motivated Internet companies to deploy artificial intelligence(AI)there,creating the hot area of edge-AI.Edge learning,the theme of this project,concerns training edge-AI models,which endow on IoT devices intelligence for responding to real-time events.However,the transmission of high-dimensional data from many edge devices to servers can result in excessive communication latency,creating a bottleneck for edge learning.Traditional wireless techniques deigned for only radio access are ineffective in tackling the challenge.Attempts to overcome the communication bottleneck has led to the development of a new class of techniques for intelligent radio resource management(RRM),called data-importance aware RRM.Their designs feature the interplay of active machine learning and wireless communication.Specifically,the metrics that measure data importance in active learning(e.g.,classification uncertainty and data diversity)are applied to RRM for efficient acquisition of distributed data in wireless networks to train AI models at servers.This article aims at providing an introduction to the emerging area of importance-aware RRM.To this end,we will introduce the design principles,survey recent advancements in the area,discuss some design examples,and suggest some promising research opportunities.