Intelligent integrated sensing and communication:a survey  

作　　者：Jifa ZHANG Weidang LU Chengwen XING Nan ZHAO Naofal AL-DHAHIR George K.KARAGIANNIDIS Xiaoniu YANG 

机构地区：[1]School of Information and Communication Engineering,Dalian University of Technology,Dalian 116024,China [2]College of Information Engineering,Zhejiang University of Technology,Hangzhou 310023,China [3]School of Information and Electronics,Beijing Institute of Technology,Beijing 100081,China [4]Department of Electrical and Computer Engineering,The University of Texas at Dallas,Richardson TX 75080,USA [5]Department of Electrical and Computer Engineering,Aristotle University of Thessaloniki,Thessaloniki 54124,Greece

出　　处：《Science China(Information Sciences)》2025年第3期1-42,共42页中国科学(信息科学)(英文版)

基　　金：supported by National Natural Science Foundation of China(Grant Nos.U23A20271,62325103);Application and Fundamental Research Planning Project in Liaoning Province(Grant No.2023TH2/101300197)。

摘　　要：Integrated sensing and communication(ISAC)is a promising technique to increase spectral efficiency and support various emerging applications by sharing the spectrum and hardware between these functionalities.However,the traditional ISAC schemes are highly dependent on the accurate mathematical model and suffer from the challenges of high complexity and poor performance in practical scenarios.Recently,artificial intelligence(AI)has emerged as a viable technique to address these issues due to its powerful learning capabilities,satisfactory generalization capability,fast inference speed,and high adaptability for dynamic environments,facilitating a system design shift from model-driven to data-driven.Intelligent ISAC,which integrates AI into ISAC,has been a hot topic that has attracted many researchers to investigate.In this paper,we provide a comprehensive overview of intelligent ISAC,including its motivation,typical applications,recent trends,and challenges.In particular,we first introduce the basic principle of ISAC,followed by its key techniques.Then,an overview of AI and a comparison between model-based and AI-based methods for ISAC are provided.Furthermore,the typical applications of AI in ISAC and the recent trends for AI-enabled ISAC are reviewed.Finally,the future research issues and challenges of intelligent ISAC are discussed.

关 键 词：artificial intelligence deep learning deep reinforcement learning federated learning generative artificial intelli-gence integrated sensing and communication machine learning transfer learning 

分 类 号：TP212[自动化与计算机技术—检测技术与自动化装置] TN91[自动化与计算机技术—控制科学与工程] TP18[电子电信—通信与信息系统]