无人机使能的通信感知一体化组网与技术研究综述

胡杨林; 张天魁; 李博; 杨鼎成

doi:10.11999/JEIT241116

无人机使能的通信感知一体化组网与技术研究综述

doi: 10.11999/JEIT241116

1.
北京邮电大学信息与通信工程学院北京 100876
2.
哈尔滨工业大学(威海)信息科学与工程学院威海 264209
3.
南昌大学信息工程学院南昌 330031

基金项目: 国家自然科学基金(62371068)

详细信息

作者简介:
胡杨林：男，博士生，研究方向为通感一体化技术、通感算一体化网络、无人机通信网络

张天魁：男，教授，研究方向为移动算力网络、无人机通信网络、移动边缘协同计算、未来网络融合与管理、大规模天线与协作通信等

李博：男，教授，研究方向为无人机通信网络、通感一体化、卫星通信等

杨鼎成：男，教授，研究方向为低空智联网、移动物联网、无线通信网络规划与优化等

通讯作者:
张天魁　zhangtiankui@bupt.edu.cn

中图分类号: TN92
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- 被引次数: 0
出版历程
- 收稿日期: 2024-12-19
- 修回日期: 2025-03-29
- 网络出版日期: 2025-04-07
- 刊出日期: 2025-04-01

A Survey on UAV-Enabled Integrated Sensing and Communication Networking and Technologies

1.
School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
2.
School of Information Science and Engineering, Harbin Institute of Technology at Weihai, Weihai 264209, China
3.
School of Information Engineering, Nanchang University, Nanchang 330031, China

Funds: The National Natural Science Foundation of China (62371068)

摘要

摘要: 无人机(UAV)凭借灵活部署和高移动性，在通信感知一体化(ISAC)技术的推动下，展现出广阔的应用前景。该文系统地综述了无人机使能的ISAC组网与关键技术的研究进展。首先，概述了ISAC技术的原理与特点。其次，针对感知辅助通信任务的应用场景，探讨了ISAC设备在无人机与地面基站中部署的不同组网结构及其优势；针对通感融合任务的应用场景，分析了无人机在定位、边缘计算与缓存等面向通感融合任务中的组网模式及关键作用。此外，从感知使能技术和资源分配技术两个维度，总结了无人机使能的ISAC关键技术发展现状。最后，针对无人机面临的能量受限、复杂传播环境、地理环境和网络安全性等挑战，探讨了无人机与无线携能、可重构智能表面、地理信息辅助及隐蔽通信等技术的融合进展，为未来智慧城市、地理测绘、应急救援等新兴低空经济场景提供技术路径与研究方向。
- 通感一体化 /
- 无人机组网 /
- 无线资源分配
Abstract: Significance Unmanned Aerial Vehicles (UAVs) have attracted significant attention due to their flexibility, high mobility, and potential for widespread applications across various industries. The integration of UAVs with Integrated Sensing and Communication (ISAC) technology enables the combination of sensing and communication capabilities on a single platform, facilitating high-quality data collection, processing, and real-time communication, particularly in complex environments. This integration offers substantial benefits in both communication and environmental sensing, addressing key challenges in emerging fields, particularly in low-altitude economic scenarios such as smart cities, geomatics, and emergency rescue. Progress This paper provides a systematic survey of UAV-enabled ISAC networks, offering a comprehensive discussion on their underlying principles and the integration of communication and sensing tasks. The first section introduces the foundational principles and characteristics of ISAC technology, including a review of waveform sensing-communication integration and multi-modal sensing-communication technologies. The paper also examines recent efforts toward standardizing ISAC technology and emphasizes the importance of sharing and co-scheduling hardware and spectrum resources to improve overall system efficiency. Subsequently, the paper explores two main network architectures for deploying ISAC devices on UAVs and ground stations. First, it investigates sensing-assisted communication tasks, where the deployment of ISAC devices within UAV communication networks ensures efficient resource allocation, improved coverage, and enhanced communication performance, particularly in challenging environments. Second, it discusses sensing-communication fusion tasks, where UAV-enabled ISAC networks integrate functions such as positioning, edge computing, and data caching. UAVs play a pivotal role in combining these functionalities to optimize overall system performance. Through UAV-enabled ISAC technology, the system’s capacity to collect environmental data, perform real-time communication, and support intelligent decision-making in complex, dynamic conditions is significantly enhanced. Additionally, the paper surveys the current state of key UAV-enabled ISAC technologies, focusing on two main aspects: sensing-enabled techniques and resource allocation strategies. From the perspective of sensing-enabled technologies, advanced techniques such as massive MIMO, collaborative sensing, near-field communication, and multi-modal sensing notably improve UAVs’ sensing precision and coverage in dynamic environments, thereby facilitating the successful execution of various tasks. Furthermore, the paper examines resource allocation techniques, addressing the challenges of distributing energy, spectrum, and processing power within energy-constrained UAV systems. It also covers the integration of wireless energy harvesting, Reconfigurable Intelligent Surfaces (RIS), and advanced communication techniques, such as covert communication, which enable UAVs to operate more efficiently in challenging environments with limited energy resources. Conclusions UAV-enabled ISAC technology is progressing rapidly and holds significant potential to transform the integration of communication and sensing tasks within UAV networks. By capitalizing on UAV mobility and versatility, ISAC networks facilitate the seamless integration of communication and environmental sensing on a single platform. This integration enhances UAV performance and adaptability in complex environments while improving resource utilization, ensuring the efficient operation of UAV networks in applications such as smart cities, geographic surveying, and emergency response. Prospects Although significant progress has been made in developing UAV-enabled ISAC networks, several challenges persist. Energy limitations, complex transmission environments, and network security are critical issues that must be addressed for UAVs to operate effectively in dynamic and diverse environments. Future research will need to focus on overcoming these challenges by integrating emerging technologies such as wireless energy harvesting and RIS, which can enhance energy efficiency and network performance. Furthermore, geographic information-enabled technologies, such as radio maps, will increasingly play a crucial role in optimizing UAV deployment and navigation, particularly in complex environments. In addition, integrating covert communication techniques into UAV networks offers a promising avenue for improving the security and privacy of UAV-based communication systems, especially in sensitive applications such as defense and surveillance. The future of UAV-enabled ISAC networks will depend on addressing challenges related to energy constraints, environmental complexity, and security concerns, while enhancing the efficiency and effectiveness of communication and sensing tasks. As these technologies mature, UAVs will become even more integral to emerging low-altitude economies, fostering the development of smart cities, efficient disaster response systems, and intelligent traffic management.
- Integrated Sensing and Communication (ISAC) /
- UAV networking /
- Wireless resource allocation

HTML全文

图 1 通感一体化波形资源分配场景

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图 2 多模感知与通信一体化场景

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图 3 无人机通信网络示意图

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图 4 通感无人机定位场景

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图 5 通感无人机缓存计算场景

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图 6 通感地面基站辅助无人机应用场景

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表 1 通感一体化网络中的波形设计分类

面向感知功能的波形设计	面向通信功能的波形设计	通信感知统一的波形设计
		面向正交资源分配的ISAC设计			统一的ISAC设计
		时分ISAC波形设计	频分ISAC波形设计	空分ISAC波形设计	统一的ISAC设计
文献[10]	文献[11]	文献[12–15]	文献[16]	文献[17]	文献[12, 18–21]

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表 2 面向感知辅助通信任务的无人机通感一体化组网分类

无人机功能	一体化波形方案	网络部署特点	主要优化目标	参考文献
通信基站	时分波形	允许感知和通信在时间上交替进行	最大化用户通信速率	[12]
通信终端	蜂窝信号	使用蜂窝基站信号测距无人机	预测并补偿时钟同步偏差	[13]
通信基站	时分波形	允许无人机在通信过程中根据实际需求灵活配置感知时间	最大化平均系统吞吐量	[19]
通信基站	统一的波形设计	利用无人机高移动性特点提高基站服务的安全性	最大化基站实时保密率与合法用户通信速率	[20]
通信基站	时分波形设计	结合天地空地综合网络与卫星信息辅助	最大化系统能量效率	[21]
频谱感知	频分波形	使用认知无线电的压缩感知技术	最大化占用子信道检测概率	[33]
信道估计	/	深度融合无人机飞行仿真与实际场景	最小化信道路径损耗误差	[34]
通信终端	蜂窝信号	无人机记录无线下行信号反射并反馈基站	最小化系统能耗	[35]

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表 3 面向通感融合任务的无人机使能通感一体化组网分类

无人机功能	一体化波形方案	网络部署特点	主要优化目标	参考文献
定位/边缘缓存节点	频分波形	定位信号自发自收/多播技术和多天线结合完成内容交付与感知	最大化雷达接收功率与通信速率组合的效用函数	[7]
定位	无蜂窝通信信号	定位信号自发它收/基于无蜂窝网络架构实现ISAC	最小化感知目标位置的克拉美罗下界	[11]
定位	时分波形	定位信号自发它收/使用毫米波进行协作感知	最小化响应时间延迟	[14]
数据采集/边缘缓存节点	时分波形	考虑无人机传感与通信之间的相互干扰	最大化雷达估计速率	[15]
定位	频分波形	定位信号它发自收自发自收/考虑多无人机与多用户关联问题	最大化无人机间最小加权频谱效率	[16]
定位	时分波形	定位信号自发自收/多无人机重叠分配相同任务	最小化整体感知任务完成时间	[17]
定位	时分波形	定位信号自发自收/感知持续时间根据应用需求灵活配置	最大化平均系统吞吐量	[19]
定位	蜂窝信号	定位信号它发自收/无人机和基站形成双基地合成孔径雷达进行感知	最小化系统能耗	[35]
目标监测	统一的波形设计	考虑无人机感知信息对于控制中心的滞后性	最小化系统的平均峰值AoI	[36]
边缘计算节点	时分波形	无人机携能供给用户	最大化无人机能量效率和任务处理速率	[37]
终端应用	时分/空分波形	多基站协同组网模式辅助无人机终端应用	/	[38]

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