Intelligent integrated sensing and communication: a survey

被引:2
作者
Zhang, Jifa [1 ]
Lu, Weidang [2 ]
Xing, Chengwen [3 ]
Zhao, Nan [1 ]
Al-Dhahir, Naofal [4 ]
Karagiannidis, George K. [5 ]
Yang, Xiaoniu [2 ]
机构
[1] Dalian Univ Technol, Sch Informat & Commun Engn, Dalian 116024, Peoples R China
[2] Zhejiang Univ Technol, Coll Informat Engn, Hangzhou 310023, Peoples R China
[3] Beijing Inst Technol, Sch Informat & Elect, Beijing 100081, Peoples R China
[4] Univ Texas Dallas, Dept Elect & Comp Engn, Richardson, TX 75080 USA
[5] Aristotle Univ Thessaloniki, Dept Elect & Comp Engn, Thessaloniki 54124, Greece
基金
中国国家自然科学基金;
关键词
artificial intelligence; deep learning; deep reinforcement learning; federated learning; generative artificial intelligence; integrated sensing and communication; machine learning; transfer learning; WAVE-FORM DESIGN; FREQUENCY SPACE MODULATION; MIMO RADAR; PARAMETER-ESTIMATION; CHANNEL ESTIMATION; JOINT RADAR; ARTIFICIAL-INTELLIGENCE; WIRELESS COMMUNICATIONS; RESOURCE-ALLOCATION; TARGET DETECTION;
D O I
10.1007/s11432-024-4205-8
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
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.
引用
收藏
页数:42
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