基于深度学习的通感一体化系统综述

冉鑫怡; 陈前斌; 徐勇军; 左文科; 赵耘; 陈莉

doi:10.11959/j.issn.1000-436x.2025103

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基于深度学习的通感一体化系统综述

综述 | 更新时间：2025-07-04

- 基于深度学习的通感一体化系统综述
- Survey on deep learning-based integrated sensing and communication systems
- 通信学报 2025年46卷第6期页码：233-250
- 作者机构：
  
  1.重庆邮电大学通信与信息工程学院，重庆 400065
  2.中国电信股份有限公司重庆分公司，重庆 401120
- 作者简介：
  
  [ "冉鑫怡（1998- ），女，重庆人，重庆邮电大学博士生，主要研究方向为通感一体化、人工智能、智能反射面等。" ]
  [ "陈前斌（1967- ），男，四川营山人，博士，重庆邮电大学教授、博士生导师，主要研究方向为通信网络、移动通信、通感一体化等。" ]
  [ "徐勇军（1986- ），男，湖北赤壁人，博士，重庆邮电大学教授、博士生导师，主要研究方向为通感一体化、人工智能、智能反射面、卫星通信等。" ]
  [ "左文科（1983- ），男，辽宁凌源人，中国电信股份有限公司重庆分公司工程师，主要研究方向为通感一体化、人工智能等。" ]
  [ "赵耘（1991- ），男，四川绵阳人，中国电信股份有限公司重庆分公司工程师，主要研究方向为通感一体化、人工智能等。" ]
  陈莉（1979- ），女，四川泸州人，重庆邮电大学工程师，主要研究方向为信号与信息处理、物联网通信等。
- 基金信息：
  
  国家自然科学基金资助项目(U23A20279;62271094)
- DOI：10.11959/j.issn.1000-436x.2025103
  中图分类号： TN92
- 收稿：2025-04-23，
  
  修回：2025-05-27，
  
  纸质出版：2025-06-25
- 稿件说明：
移动端阅览
冉鑫怡,陈前斌,徐勇军等.基于深度学习的通感一体化系统综述[J].通信学报,2025,46(06):233-250.

RAN Xinyi,CHEN Qianbin,XU Yongjun,et al.Survey on deep learning-based integrated sensing and communication systems[J].Journal on Communications,2025,46(06):233-250.
冉鑫怡,陈前斌,徐勇军等.基于深度学习的通感一体化系统综述[J].通信学报,2025,46(06):233-250. DOI： 10.11959/j.issn.1000-436x.2025103.

RAN Xinyi,CHEN Qianbin,XU Yongjun,et al.Survey on deep learning-based integrated sensing and communication systems[J].Journal on Communications,2025,46(06):233-250. DOI： 10.11959/j.issn.1000-436x.2025103.

摘要

随着无线通信与雷达感知技术的深度融合，通感一体化（ISAC）通过共享硬件平台与频谱资源，在提升系统效率方面展现出显著潜力。然而，传统ISAC依赖先验模型和专家知识，难以应对动态环境下的实时通信与感知需求。近年来，深度学习的快速发展为破解这一困境提供了新范式，使得系统能够更有效地处理大量数据，实现自适应学习，并在复杂环境中做出智能决策，进而优化系统性能。为此，针对基于深度学习的ISAC展开综述。首先，介绍了ISAC原理、系统模型、网络架构和技术方案类型；其次，阐述了ISAC主要采用的深度学习模型架构；然后，分析了深度学习在ISAC信道估计、信道编码、资源分配、人体检测、目标识别与追踪等典型场景的研究现状；最后，探讨了深度学习驱动的ISAC所面临的技术挑战和未来方向。此外，上述研究对推动6G网络通信感知深度融合、促进智能网络全要素协同发展，具有重要的理论意义与现实价值。

Abstract

With the deep integration of wireless communication and radar sensing technologies

integrated sensing and communication (ISAC) shares hardware platforms and spectrum resources. It has demonstrated significant potential for enhancing system efficiency. However

traditional ISAC relying on prior models and expert knowledge has struggled to address real-time communication and sensing demands in dynamic environments. The rapid development of deep learning recently provided a novel paradigm to resolve these limitations

enabling systems to process massive data more effectively

achieve adaptive learning

and make intelligent decisions in complex environments

thereby optimizing system performance. A comprehensive review was conducted on deep learning-based ISAC. Firstly

the principles

the system model

the network architecture

and the types of technical solutions of ISAC were introduced. Then

the mainly adopted deep learning model architectures in ISAC were analyzed. Furthermore

the research situation of deep learning in typical scenarios such as channel estimation

channel coding

resource allocation

human detection

and target recognition and tracking was systematically investigated. Finally

key technical challenges and future directions in deep learning-driven ISAC were discussed. The research contributed to the deep integration of communication and sensing in 6G networks and facilitated the coordinated development of intelligent networks

holding important theoretical and practical value.

关键词

Keywords

references

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