人工智能驱动的雷达通信一体化研究综述

王先梅; 刘向博; 任语铮; 陆阳; 张海君

doi:10.12000/JR24252

人工智能驱动的雷达通信一体化研究综述

DOI: 10.12000/JR24252 CSTR: 32380.14.JR24252

1.
北京科技大学计算机与通信工程学院北京 100083
2.
中国电力科学研究院北京 102209

基金项目: 国家自然科学基金(U22B2003)，中央高校科研业务经费(FRF-TP-22-002C2)，通信抗干扰全国重点实验室资助项目(IFN20230201)，小米青年学者基金

详细信息

作者简介:
王先梅，博士，副教授，主要研究方向为通感一体化、机器学习

刘向博，硕士生，主要研究方向为雷达通信一体化、联邦学习

任语铮，博士，副教授，主要研究方向为未来通信网络、网络人工智能

陆　阳，博士，教授级高级工程师，主要研究方向为电力传感与通信技术

张海君，博士，教授，博士生导师，主要研究方向为6G移动通信、人工智能与无线网络

通讯作者:
张海君 zhanghaijun@ustb.edu.cn

责任主编：崔原豪 Corresponding Editor: CUI Yuanhao
中图分类号: TN929.5
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出版历程
- 收稿日期: 2024-12-16
- 修回日期: 2025-04-02

Review of Research on Artificial Intelligence-Driven Joint Radar Communication

1.
University of Science and Technology Beijing, Beijing 100083, China
2.
China Electric Power Research Institute Co. Ltd, Beijing 102209, China

Funds: The National Natural Science Foundation of China (U22B2003), The Fundamental Research Funds for the Central Universities (FRF-TP-22-002C2), The National Key Laboratory of Wireless Communications Foundation (IFN20230201), The Xiaomi Fund of Young Scholar

More Information

Corresponding author: ZHANG Haijun, zhanghaijun@ustb.edu.cn

摘要

摘要: 雷达通信一体化通过资源共享机制，在提高系统频谱利用率的同时实现了轻量化设计，广泛应用于空中交通管制、医疗监测、自动驾驶等领域。传统的雷达通信一体化算法通常依赖于精确的数学建模和信道估计，无法适应难以刻画的动态复杂环境。人工智能凭借其强大的学习能力直接从大量数据中自动学习特征，无需对数据进行显式建模，促进了雷达通信的深度融合。该文围绕人工智能驱动的雷达通信一体化研究展开系统的综述。具体而言，首先阐述了雷达通信一体化系统模型与核心问题。在此基础上，从雷达通信共存和双功能雷达通信一体化两个方面梳理了人工智能驱动的雷达通信一体化最新研究进展。最后，总结全文并对该领域潜在的技术挑战和未来的研究方向进行了展望。
- 人工智能 /
- 雷达通信一体化 /
- 机器学习 /
- 干扰管控 /
- 资源分配
Abstract: Joint radar communication leverages resource-sharing mechanisms to improve system spectrum utilization and achieve lightweight design. It has wide applications in air traffic control, healthcare monitoring, and autonomous vehicles. Traditional joint radar communication algorithms often rely on precise mathematical modeling and channel estimation and cannot adapt to dynamic and complex environments that are difficult to describe. Artificial Intelligence (AI), with its powerful learning ability, automatically learns features from large amounts of data without the need for explicit modeling, thereby promoting the deep fusion of radar communication. This article provides a systematic review of the research on AI-driven joint radar communication. Specifically, the model and challenges of the joint radar communication system are first elaborated. On this basis, the latest research progress on AI-driven joint radar communication is summarized from two aspects: radar communication coexistence and dual-functional radar communication. Finally, the article is summarized, and the potential technical challenges and future research directions in this field are described.
- Artificial Intelligence (AI) /
- Joint Radar Communication (JRC) /
- Machine Learning (ML) /
- Interference control /
- Resource allocation

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图 1 雷达通信一体化应用场景

Figure 1. Application scenarios of joint radar communication

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图 2 雷达通信共存系统模型

Figure 2. Radar communication coexistence model

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图 3 双功能雷达通信一体化系统模型

Figure 3. Dual-function radar-communication model

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图 4 基于资源分配的智能干扰管控策略

Figure 4. Intelligent interference control strategy based on resource allocation

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图 5 AI驱动的信号检测处理

Figure 5. AI-driven signal detection processing

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图 6 基于学习的波束赋形优化

Figure 6. Learning-based beamforming optimization

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图 7 面向动态需求的智能资源管控策略

Figure 7. Intelligent resource management and control policies for dynamic requirements

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表 1 人工智能驱动的雷达通信共存算法比较

Table 1. Comparison of AI-driven radar communication coexistence algorithms

算法类型	适用的核心问题	优点	缺点	文献
强化学习	基于资源分配的干扰管控	通过与环境交互，适应频谱状态的快速变化，能处理不确定的干扰问题	RL与环境大量交互带来了高昂的成本问题	[46]
在线学习	基于资源分配的干扰管控	通过持续学习以缓解频谱冲突，且每步训练的计算复杂度较低	易错失全局最优解	[58]
深度强化学习	基于资源分配的干扰管控	通过强化学习适应频谱状态的快速变化，利用深度学习处理高维状态空间	计算复杂度较高	[38,59,60,61,64]
元学习	基于资源分配的干扰管控	缩短模型训练时间，快速获取干扰抑制决策	当新任务与训练任务差异较大时，效果不佳	[65]
深度学习	信号检测处理	直接从原始数据中学习特征，避免了对信道状态信息的依赖	计算资源需求大	[48,67−69]
模型驱动的深度学习	信号检测处理	将雷达通信的物理底层原理与深度学习框架相结合，更易于模型训练	模型的表达能力受制于理论假设	[70]

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表 2 基于人工智能的双功能雷达通信一体化算法比较

Table 2. Comparison of dual-functional radar communication algorithms based on AI

算法类型	适用的核心问题	优点	缺点	文献
深度学习	波束赋形优化	通过损失函数设计实现性能折中，可解决复杂的非凸优化问题	模型易过拟合，泛化能力不足	[76,77,79,81]
在线学习	波束赋形优化、面向动态需求的资源管控	通过处理小批样本，实时捕捉环境变化，可动态优化波束指向与管控策略	难以处理高维度优化问题	[78,84]
强化学习	波束赋形优化	具备较强的全局优化能力，可用于时变信道下的波束赋形	数据采样效率较低	[82]
深度强化学习学习	面向动态需求的资源管控	利用神经网络处理高维度状态空间，通过强化学习动态调整分配策略	算法收敛速度较慢	[10,13]
多智能体深度强化学习	面向动态需求的资源管控	引入GNN的结构信息捕获能力，可协调多智能体间的资源分配	图结构快速变化会引起模型的泛化能力下降	[86]
迁移学习	面向动态需求的资源管控	加快了切换到新环境中的策略收敛速度	负迁移会导致学习性能下降	[37]

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