面向低空经济的通感一体化关键技术

朱政宇; 温鑫平; 李兴旺; 尉志青; 张沛昌; 刘凡; 冯志勇

doi:10.11999/JEIT250747

面向低空经济的通感一体化关键技术

doi: 10.11999/JEIT250747 cstr: 32379.14.JEIT250747

1.
郑州大学电气与信息工程学院河南郑州 450000
2.
河南理工大学物理与电子信息学院河南焦作 454000
3.
北京邮电大学信息与通信工程学院北京 100876
4.
深圳大学电子与信息工程学院广东深圳 518000
5.
东南大学信息科学与工程学院江苏南京 210096

基金项目: 河南省高校科技创新人才支持计划资助(No.23HASTIT019)，河南省自然科学基金优青项目(No.232300421097)，西安电子科技大学空天地一体化综合业务网全国重点实验室(No. ISN25-24)

详细信息

中图分类号: TN92
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出版历程
- 收稿日期: 2025-08-12
- 修回日期: 2025-11-03
- 录用日期: 2025-11-03
- 网络出版日期: 2025-11-11

An Overview on Integrated Sensing and Communication for Low altitude economy

Funds: Sponsored by Program for Science & Technology Innovation Talents in Universities of Henan Province (No.23HASTIT019), Natural Science Foundation of Henan Province (No.232300421097)

摘要

摘要: 随着低空物联网的发展，低空经济逐渐成为国家战略性新兴产业。面向低空经济的通感一体化技术能够在复杂环境中执行多任务协同操作，可显著提升无人机的安全性、灵活性和多场景适应性。本文系统综述面向低空经济的通感一体化关键技术。首先，概述了通感一体化和低空经济的理论基础，并讨论面向低空经济的通感一体化技术的优势；然后，探讨隐蔽通信、智能反射面、毫米波通信等第6代(6G)网络关键前沿技术在面向低空经济的通感一体化网络中的潜在应用；最后，总结了未来面向低空经济的通感一体化技术的关键挑战和研究方向。
- 低空经济 /
- 通感一体化 /
- 无人机 /
- 雷达感知
Abstract: With the development of the Low-altitude Internet of Things (IoT), the Low-altitude economy has gradually become a national strategic emerging industry. Therefore, the Integrated Sensing and Communication (ISAC) for Low-altitude economy can perform more complex tasks in more complex environments, laying the foundation for improving the security, flexibility, and multi-application scenarios of drones. This paper provides an overview on ISAC for Low-altitude economy. Firstly, it summarizes the theoretical foundations of the ISAC and Low altitude economy is, and the advantage of ISAC for Low- altitude economy are discussed. Then, it investigates the potential applications of 6G key technologies, such as covert communication, millimeter wave (mm wave) in the ISAC for Low altitude economy. Finally, the key technical challenges of the ISAC for Low altitude economy in the future were summarized. Significance The integration of UAVs with ISAC technology will offer considerable advantages in future developments. By implementing ISAC, the overall system payload can be minimized, greatly enhancing UAV maneuverability and operational freedom. This integration provides strong technical support for versatile UAV applications. Equipped with ISAC, low-altitude network systems can perform increasingly complex tasks in challenging environments. Unlike single-function UAV platforms, those incorporating ISAC benefit from synergistic improvements in both communication and sensing capabilities. As a result, ISAC-enabled drones are expected to see expanded use in fields such as aerial photography, agriculture, surveying, remote sensing, and telecommunications. This growth will further accelerate the advancement of relevant theoretical and technical frameworks while broadening the scope of ISAC applications. Progress ISAC networks for the low-altitude economy provide efficient and flexible solutions for applications such as military reconnaissance, emergency disaster relief, and smart city management. However, the open aerial environment and dynamic deployment requirements introduce multiple challenges, including vulnerability to hostile interception due to limited stealth, signal obstruction in complex terrains, and the need for high bandwidth and low latency. In response, both academic and industrial communities have been actively investigating technologies such as covert communications, intelligent reflecting surfaces, and millimeter-wave communications to enhance the reliability and intelligence of ISAC in low-altitude operational scenarios. Conclusions This paper provides a systematic overview of the current applications, critical technologies, and ongoing challenges associated with ISAC in low-altitude environments. It examines the synergistic integration of emerging 6G technologies—including covert communication, RIS and mm-Wave communications—within ISAC frameworks. In response to the highly dynamic and complex nature of low-altitude operations, the study also summarizes recent advances in UAV swarm power control algorithms and covert trajectory optimization based on deep reinforcement learning. Furthermore, it highlights key unresolved challenges such as spatiotemporal synchronization, multi-UAV resource allocation, and privacy preservation, offering valuable directions for future research. Prospects ISAC technology provides highly precise and reliable support for applications such as drone logistics, urban air mobility, and large-scale environmental monitoring in the low-altitude economy. Nevertheless, the large-scale deployment of ISAC systems in complex and dynamic low-altitude environments still remains challenging. Key obstacles include: suboptimal coordination and resource allocation in UAV swarms, spatiotemporal synchronization among heterogeneous devices, conflicting objectives between sensing and communication functions, as well as growing concerns over privacy and security in open airspace. These challenges represent major impediments to the high-quality development of the low-altitude economy.
- Low-altitude economy /
- Integrated Sensing and Communication (ISAC) /
- Unmanned Aerial Vehicle (UAV) /
- Radar Perception

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图 1 无人机辅助无线通信的三个典型用例

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图 2 无人机中继器协助对抗无人机监测者

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图 3 建筑物智能反射面辅助无人机通信系统

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表 1 现有 ISCA 波形设计分类简要总结

波形设计	技术原理	优点	缺点
以通信为中心^[12][13]	利用通信波形的目标回波来提取雷达信息，将雷达感知融合到通信系统中	不牺牲通信性能的情况下实现感知功能	依赖于多输入多输出难以协调其感知性能
以雷达为中心^{[14 -19]}	保证雷达性能的同时将通信信息嵌入到雷达波形中	与雷达架构具有高度的兼容性	数据速率降低
通感联合设计^[20–23]	达到雷达通信性能的折中	受任何现有通信或雷达标准的限制	设计复杂度高

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表 2 技术发展挑战与未来研究方向简要总结

问题类别	主要困难	关键挑战	解决办法
时空同步与干扰	高速移动导致时间不同步	GPS同步失效；无人机之间及外部环境干扰严重；通信和感知功能难以协同	研发自主时间同步技术；对飞行轨迹、功率和通信资源进行联合优化
合作频谱感知	多无人机协作效率低	组网协作慢、开销大；现有研究模型过于理想，忽略安全性	设计低开销、高效率的新型协作算法；建立更贴近实际的动态感知模型
协同通信	地面控制信号易受干扰	遥控信号对干扰非常敏感，直接影响无人机操控和任务安全	研究先进的干扰抑制技术动态资源分配策略以增强链路可靠性

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参考文献(90)

[1]	钟成林, 胡雪萍. 低空经济高质量发展的新质生产力逻辑与提升路径[J]. 深圳大学学报(人文社会科学版), 2024, 41(5): 84–93. doi: 10.3969/j.issn.1000-260X.2024.05.008. ZHONG Chenglin and HU Xueping. The new quality productivity logic and promotion path for high-quality development of low altitude economy[J]. Journal of Shenzhen University (Humanities & Social Sciences), 2024, 41(5): 84–93. doi: 10.3969/j.issn.1000-260X.2024.05.008.
[2]	中国电信集团有限公司, 爱立信, 诺基亚, 等. 通感一体低空网络白皮书[R]. 2024. (查阅网上资料, 未找到报告编号信息, 请确认). China Telecom Corp Ltd, Ericsson, Nokia, et al. The low-altitude network by integrated sensing and communication[R]. 2024.
[3]	林丽芸, 孔德智. 关于低空智联网发展的思考[J]. 电子质量, 2024(2): 105–109. doi: 10.3969/j.issn.1003-0107.2024.02.022. LIN Liyun and KONG Dezhi. Reflections on the development of low-altitude internet of intelligences[J]. Electronics Quality, 2024(2): 105–109. doi: 10.3969/j.issn.1003-0107.2024.02.022.
[4]	CUI Yuanhao, LIU Fan, JING Xiaojun, et al. Integrating sensing and communications for ubiquitous IoT: Applications, trends, and challenges[J]. IEEE Network, 2021, 35(5): 158–167. doi: 10.1109/MNET.010.2100152.
[5]	MU Junsheng, GONG Yi, ZHANG Fangpei, et al. Integrated sensing and communication-enabled predictive beamforming with deep learning in vehicular networks[J]. IEEE Communications Letters, 2021, 25(10): 3301–3304. doi: 10.1109/LCOMM.2021.3098748.
[6]	ZHANG J A, RAHMAN M L, WU Kai, et al. Enabling joint communication and radar sensing in mobile networks - a survey[J]. IEEE Communications Surveys & Tutorials, 2022, 24(1): 306–345. doi: 10.1109/COMST.2021.3122519.
[7]	谢鑫, 邓云开, 杨志军, 等. 基于地形辅助的无人机载InSAR图像分区配准方法[J]. 雷达学报, 2024, 13(1): 116–133. doi: 10.12000/JR23182. XIE Xin, DENG Yunkai, YANG Zhijun, et al. Topography-assisted UAV InSAR image registration method with image partition[J]. Journal of Radars, 2024, 13(1): 116–133. doi: 10.12000/JR23182.
[8]	KIM D, LEE J, and QUEK T Q S. Multi-layer unmanned aerial vehicle networks: Modeling and performance analysis[J]. IEEE Transactions on Wireless Communications, 2020, 19(1): 325–339. doi: 10.1109/TWC.2019.2944378.
[9]	HASSANIEN A, AMIN M G, ZHANG Y D, et al. Signaling strategies for dual-function radar communications: An overview[J]. IEEE Aerospace and Electronic Systems Magazine, 2016, 31(10): 36–45. doi: 10.1109/MAES.2016.150225.
[10]	WANG Xinyi, FEI Zesong, ZHANG A J, et al. Constrained utility maximization in dual-functional radar-communication multi-UAV networks[J]. IEEE Transactions on Communications, 2021, 69(4): 2660–2672. doi: 10.1109/TCOMM.2020.3044616.
[11]	KUMARI P, MYERS N J, and HEATH R W. Adaptive and fast combined waveform-beamforming design for MMWave automotive joint communication-radar[J]. IEEE Journal of Selected Topics in Signal Processing, 2021, 15(4): 996–1012. doi: 10.1109/JSTSP.2021.3071592.
[12]	KESKIN M F, WYMEERSCH H, and KOIVUNEN V. MIMO-OFDM joint radar-communications: Is ICI friend or foe?[J]. IEEE Journal of Selected Topics in Signal Processing, 2021, 15(6): 1393–1408. doi: 10.1109/JSTSP.2021.3109431.
[13]	HUANG Tianyao, SHLEZINGER N, XU Xingyu, et al. MAJoRCom: A dual-function radar communication system using index modulation[J]. IEEE Transactions on Signal Processing, 2020, 68: 3423–3438. doi: 10.1109/TSP.2020.2994394.
[14]	HASSANIEN A, AMIN M G, ZHANG Y D, et al. Dual-function radar-communications: Information embedding using sidelobe control and waveform diversity[J]. IEEE Transactions on Signal Processing, 2016, 64(8): 2168–2181. doi: 10.1109/TSP.2015.2505667.
[15]	WEI Zhongxiang, LIU Fan, MASOUROS C, et al. Toward multi-functional 6G wireless networks: Integrating sensing, communication, and security[J]. IEEE Communications Magazine, 2022, 60(4): 65–71. doi: 10.1109/MCOM.002.2100972.
[16]	YAO Xue, YANG Zhihang, QIU Hui, et al. DFRC signal design with hybrid index modulation[J]. IEEE Sensors Journal, 2024, 24(13): 20855–20867. doi: 10.1109/JSEN.2024.3395789.
[17]	MA Dingyou, SHLEZINGER N, HUANG Tianyao, et al. Spatial modulation for joint radar-communications systems: Design, analysis, and hardware prototype[J]. IEEE Transactions on Vehicular Technology, 2021, 70(3): 2283–2298. doi: 10.1109/TVT.2021.3056408.
[18]	XU Rui, WEN Ruiming, CUI Guolong, et al. Radar performance degradation elimination for sub-pulse-based FMCW in DFRC[J]. IEEE Signal Processing Letters, 2023, 30: 1582–1586. doi: 10.1109/LSP.2023.3327539.
[19]	WEN Cai and DAVIDSON T N. Transceiver design for MIMO-DFRC systems[C]. Proceedings of the ICASSP 2023 – 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece, 2023: 1–5. doi: 10.1109/ICASSP49357.2023.10096008.
[20]	LIU Fan, ZHOU Longfei, MASOUROS C, et al. Toward dual-functional radar-communication systems: Optimal waveform design[J]. IEEE Transactions on Signal Processing, 2018, 66(16): 4264–4279. doi: 10.1109/TSP.2018.2847648.
[21]	KOBAYASHI M, CAIRE G, and KRAMER G. Joint state sensing and communication: Optimal tradeoff for a memoryless case[C]. Proceedings of the 2018 IEEE International Symposium on Information Theory (ISIT), Vail, USA, 2018: 111–115. doi: 10.1109/ISIT.2018.8437621.
[22]	ZHANG Wenyi, VEDANTAM S, MITRA U. Joint transmission and state estimation: A constrained channel coding approach[J]. IEEE Transactions on Information Theory, 2011, 57(10): 7084–7095. doi: 10.1109/TIT.2011.2158488.
[23]	CHU Chunhua, CHEN Yijun, ZHANG Qun, et al. MIMO radar waveform joint optimization design in time and frequency domain[C]. Proceedings of the 2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM), Hangzhou, China, 2020: 1–5. doi: 10.1109/SAM48682.2020.9104351.
[24]	HASSANIEN A, AMIN M G, ABOUTANIOS W, et al. Dual-function radar communication systems: A solution to the spectrum congestion problem[J]. IEEE Signal Processing Magazine, 2019, 36(5): 115–126. doi: 10.1109/MSP.2019.2900571.
[25]	AVDOGDU C, GARCIA N, and WYMEERSCH H. Improved pedestrian detection under mutual interference by FMCW radar communications[C]. Proceedings of the 2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Bologna, Italy, 2018: 101–105. doi: 10.1109/PIMRC.2018.8581028.
[26]	BRAUN M, STURM C, NIETHAMMER A, et al. Parametrization of joint OFDM-based radar and communication systems for vehicular applications[C]. Proceedings of the 2009 IEEE 20th International Symposium on Personal, Indoor and Mobile Radio Communications, Tokyo, Japan, 2009: 3020–3024. doi: 10.1109/PIMRC.2009.5449769.
[27]	CAI Yuanxin, WEI Zhiqiang, LI Ruide, et al. Joint trajectory and resource allocation design for energy-efficient secure UAV communication systems[J]. IEEE Transactions on Communications, 2020, 68(7): 4536–4553. doi: 10.1109/TCOMM.2020.2982152.
[28]	曾婷, 才宇, 张捷宝, 等. 面向6G通信感知一体化的关键技术和系统架构研究[J]. 无线电通信技术, 2024, 50(3): 461–468. doi: 10.3969/j.issn.1003-3114.2024.03.007. ZENG Ting, CAI Yu, ZHANG Jiebao, et al. Study on key technologies and system architecture of integrated sensing and communication for 6G[J]. Radio Communications Technology, 2024, 50(3): 461–468. doi: 10.3969/j.issn.1003-3114.2024.03.007.
[29]	ZHOU Lingyun, CHEN Xihan, HONG Mingyi, et al. Efficient resource allocation for multi-UAV communication against adjacent and co-channel interference[J]. IEEE Transactions on Vehicular Technology, 2021, 70(10): 10222–10235. doi: 10.1109/TVT.2021.3104279.
[30]	LYU Jiangbin, ZENG Yong, ZHANG Rui, et al. Placement optimization of UAV-mounted mobile base stations[J]. IEEE Communications Letters, 2017, 21(3): 604–607. doi: 10.1109/LCOMM.2016.2633248.
[31]	SOHAIL M F, LEOW C Y, and WON S. Non-orthogonal multiple access for unmanned aerial vehicle assisted communication[J]. IEEE Access, 2018, 6: 22716–22727. doi: 10.1109/ACCESS.2018.2826650.
[32]	LIU Yuanwei, QIN Zhijin, ELKASHLAN M, et al. Nonorthogonal multiple access for 5G and beyond[J]. Proceedings of the IEEE, 2017, 105(12): 2347–2381. doi: 10.1109/JPROC.2017.2768666.
[33]	ZHOU Fuhui, WU Yongpeng, HU R Q, et al. Energy-efficient NOMA enabled heterogeneous cloud radio access networks[J]. IEEE Network, 2018, 32(2): 152–160. doi: 10.1109/MNET.2017.1700208.
[34]	王炳文, 唐菁敏, 宋耀莲. 智能反射面辅助无人机中继的资源优化算法[J]. 数据通信, 2024(3): 34–40. doi: 10.3969/j.issn.1002-5057.2024.03.008. WANG Bingwen, TANG Jingmin, and SONG Yaolian. Resource optimization algorithm for intelligent reflective surface assisted UAV relay[J]. Data Communications, 2024(3): 34–40. doi: 10.3969/j.issn.1002-5057.2024.03.008.
[35]	俞荣康, 胡晗, 杨龙祥. 智能反射面辅助的无人机认知网络资源优化算法[J]. 南京邮电大学学报(自然科学版), 2025, 45(3): 28–37. doi: 10.14132/j.cnki.1673-5439.2025.03.004. YU Rongkang, HU Han, and YANG Longxiang. A resource optimization algorithm for intelligent reflective surface-assisted UAV cognitive network[J]. Journal of Nanjing University of Posts and Telecommunications(Natural Science Edition), 2025, 45(3): 28–37. doi: 10.14132/j.cnki.1673-5439.2025.03.004.
[36]	袁伟杰, 伍军, 时玉叶. 基于多无人机协作通感一体化的隐蔽通信设计[J]. 雷达学报(中英文), 2025, 14(4): 797–808. doi: 10.12000/JR25018. YUAN Weijie, WU Jun, and SHI Yuye. Multi-UAV collaborative covert communications: An ISAC-based approach[J]. Journal of Radars, 2025, 14(4): 797–808. doi: 10.12000/JR25018.
[37]	孙君, 徐金童. 无人机通感一体化中基于干扰建模的多维能效方案[J/OL]. 物联网学报. https://link.cnki.net/urlid/10.1491.TP.20250807.1613.002, 2025. SUN Jun and XU Jintong. A multi-dimensional energy efficiency scheme based on interference modeling in unmanned aerial vehicle integrated sensing and communication[J/OL]. Chinese Journal on Internet of Things. https://link.cnki.net/urlid/10.1491.TP.20250807.1613.002, 2025.
[38]	赵川斌, 张腾宇, 冯源, 等. 面向低空无人机的通感一体化关键技术及原型验证研究[J]. 物联网学报, 2025, 9(2): 27–38. doi: 10.11959/j.issn.2096-3750.2025.00486. ZHAO Chuanbin, ZHANG Tengyu, FENG Yuan, et al. Key technologies and prototype validation research of integrated sensing and communications for low altitude UAV[J]. Chinese Journal on Internet of Things, 2025, 9(2): 27–38. doi: 10.11959/j.issn.2096-3750.2025.00486.
[39]	贡文新, 余泽琰, 杨柳旺, 等. 基于毫米波雷达的无人机障碍物分类方法[J]. 雷达科学与技术, 2025, 23(3): 317–327. doi: 10.3969/j.issn.1672-2337.2025.03.009. GONG Wenxin, YU Zeyan, YANG Liuwang, et al. Millimeter-wave radar-based obstacle classification method for unmanned aerial vehicles[J]. Radar Science and Technology, 2025, 23(3): 317–327. doi: 10.3969/j.issn.1672-2337.2025.03.009.
[40]	王迎山. 毫米波通信技术在5G无人驾驶中的应用研究[J]. 长江信息通信, 2025, 38(2): 13–15. doi: 10.20153/j.issn.2096-9759.2025.02.004. WANG Yingshan. Research on the application of millimeter wave communication technology in 5G autonomous driving[J]. Changjiang Information & Communications, 2025, 38(2): 13–15. doi: 10.20153/j.issn.2096-9759.2025.02.004.
[41]	柴蓉, 王丙燕, 孙瑞锦, 等. 基于系统成本函数优化的无人机辅助通感一体化系统数据调度、雷达预编码及飞行轨迹优化方法[J]. 电子学报, 2025, 53(3): 744–753. doi: 10.12263/DZXB.20240656. CHAI Rong, WANG Bingyan, SUN Ruijin, et al. System cost function optimization-based data scheduling, radar precoding and flight trajectory design for UAV-assisted integrated sensing and communication systems[J]. Acta Electronica Sinica, 2025, 53(3): 744–753. doi: 10.12263/DZXB.20240656.
[42]	吕芸昕, 苏颖, 张静. 基于NOMA的无人机通感一体化系统的轨迹与波束成形联合优化设计[J]. 上海师范大学学报(自然科学版中英文), 2025, 54(2): 172–179. doi: 10.20192/j.cnki.JSHNU(NS).2025.02.007. LÜ Yunxin, SU Ying, and ZHANG Jing. Joint trajectory and beamforming design based on integrated sensing and communication for NOMA-enabled UAV[J]. Journal of Shanghai Normal University (Natural Sciences), 2025, 54(2): 172‒179. doi: 10.20192/j.cnki.JSHNU(NS).2025.02.007.
[43]	SAIF M and VALAEE S. RIS alignment via virtual partitioning for resilient uplink multi-RIS-assisted UAV communications[J]. IEEE Transactions on Communications, 2025, 73(8): 6764–6779. doi: 10.1109/TCOMM.2025.3534527.
[44]	HEMAVATHY P and PRIYA S B M. Energy-efficient UAV integrated RIS for NOMA communication[C]. Proceedings of the 2025 1st International Conference on Radio Frequency Communication and Networks (RFCoN), Thanjavur, India, 2025: 1–6. doi: 10.1109/RFCoN62306.2025.11085332.
[45]	SEONG H, KIM T, SONG J, et al. Hierarchical multi-agent reinforcement learning-based UAV control for wireless covert communications[C]. Proceedings of the 2025 IEEE 22nd Consumer Communications & Networking Conference (CCNC), Las Vegas, USA, 2025: 1–6. doi: 10.1109/CCNC54725.2025.10976044.
[46]	GAO Chan, TIAN Linying, ZHAO Qiuxia, et al. Covert and secure communication in untrusted UAV-assisted wireless systems[J]. IEEE Internet of Things Journal, 2025, 12(17): 35329–35339. doi: 10.1109/JIOT.2025.3578987.
[47]	ZENG Wen, FU Shu, and DI Boya. Optimal covert age of information for ARIS-assisted covert communication system[J]. IEEE Wireless Communications Letters, 2025, 14(8): 2277–2281. doi: 10.1109/LWC.2025.3548902.
[48]	ZHANG Yi, LIU Yu, LI Xinru, et al. A novel space-time-frequency non-stationary UAV-to-USV channel model for MMWave maritime communications[J]. IEEE Wireless Communications Letters, 2025, 14(11): 3515–3519. doi: 10.1109/LWC.2025.3596556.
[49]	JIN Xin, AN Jianping, DU Changhao, et al. Frequency-offset information aided self time synchronization scheme for high-dynamic multi-UAV networks[J]. IEEE Transactions on Wireless Communications, 2024, 23(1): 607–620. doi: 10.1109/TWC.2023.3280536.
[50]	CHEN Xinying, AN Jianping, ZHAO Nan, et al. UAV relayed covert wireless networks: Expand hiding range via drones[J]. IEEE Network, 2022, 36(4): 226–232. doi: 10.1109/MNET.104.2100496.
[51]	VAN HUYNH D, LI Yijiu, MASARACCHIA A, et al. Optimal resource allocation for 6G UAV-enabled mobile edge computing with mission-critical applications[C]. Proceedings of the 2023 IEEE International Conference on Metaverse Computing, Networking and Applications (MetaCom), Kyoto, Japan, 2023: 720–723. doi: 10.1109/MetaCom57706.2023.00135.
[52]	ZENG Yong, ZHANG Rui, and LIM T J. Wireless communications with unmanned aerial vehicles: Opportunities and challenges[J]. IEEE Communications Magazine, 2016, 54(5): 36–42. doi: 10.1109/MCOM.2016.7470933.
[53]	NGUYEN M T and LE Longbao. NOMA user pairing and UAV placement in UAV-based wireless networks[C]. Proceedings of the ICC 2019 - 2019 IEEE International Conference on Communications (ICC), Shanghai, China, 2019: 1–6. doi: 10.1109/ICC.2019.8761606.
[54]	ZHAO Nan, LU Weidang, SHENG Min, et al. UAV-assisted emergency networks in disasters[J]. IEEE Wireless Communications, 2019, 26(1): 45–51. doi: 10.1109/MWC.2018.1800160.
[55]	LIU Yuanwei, QIN Zhijin, CAI Yunlong, et al. UAV communications based on non-orthogonal multiple access[J]. IEEE Wireless Communications, 2019, 26(1): 52–57. doi: 10.1109/MWC.2018.1800196.
[56]	JIANG Wangjun, WANG Ailing, WEI Zhiqing, et al. Improve sensing and communication performance of UAV via integrated sensing and communication[C]. Proceedings of the 2021 IEEE 21st International Conference on Communication Technology (ICCT), Tianjin, China, 2021: 644–648. doi: 10.1109/ICCT52962.2021.9657955.
[57]	WANG Zhe, DUAN Lingjie, and ZHANG Rui. Adaptive deployment for UAV-aided communication networks[J]. IEEE Transactions on Wireless Communications, 2019, 18(9): 4531–4543. doi: 10.1109/TWC.2019.2926279.
[58]	CHEN Mingzhe, SAAD W, and YIN Changchuan. Liquid state machine learning for resource and cache management in LTE-U Unmanned Aerial Vehicle (UAV) networks[J]. IEEE Transactions on Wireless Communications, 2019, 18(3): 1504–1517. doi: 10.1109/TWC.2019.2891629.
[59]	GALKIN B, KIBILDA J, and DASILVA L A. Deployment of UAV-mounted access points according to spatial user locations in two-tier cellular networks[C]. Proceedings of the 2016 Wireless Days (WD), Toulouse, France, 2016: 1–6. doi: 10.1109/WD.2016.7461487.
[60]	BHUSHAN N, LI Junyi, MALLADI D, et al. Network densification: The dominant theme for wireless evolution into 5G[J]. IEEE Communications Magazine, 2014, 52(2): 82–89. doi: 10.1109/MCOM.2014.6736747.
[61]	李兴旺, 田志发, 张建华, 等. IRS辅助NOMA网络下隐蔽通信性能研究[J]. 中国科学: 信息科学, 2024, 54(6): 1502–1515. doi: 10.1360/SSI-2023-0174. LI Xingwang, TIAN Zhifa, ZHANG Jianhua, et al. Performance analysis of covert communication in IRS-assisted NOMA networks[J]. Scientia Sinica Informationis, 2024, 54(6): 1502–1515. doi: 10.1360/SSI-2023-0174.
[62]	WANG Chao, CHEN Xinying, AN Jianping, et al. Covert communication assisted by UAV-IRS[J]. IEEE Transactions on Communications, 2023, 71(1): 357–369. doi: 10.1109/TCOMM.2022.3220903.
[63]	LI Zan, LIAO Xiaomin, SHI Jia, et al. MD-GAN-based UAV trajectory and power optimization for cognitive covert communications[J]. IEEE Internet of Things Journal, 2022, 9(12): 10187–10199. doi: 10.1109/JIOT.2021.3122014.
[64]	HU Jinsong, GUO Mingqian, YAN Shihao, et al. Deep reinforcement learning enabled covert transmission with UAV[J]. IEEE Wireless Communications Letters, 2023, 12(5): 917–921. doi: 10.1109/LWC.2023.3251357.
[65]	WANG Yida, YAN Shihao, ZHOU Xiaobo, et al. Covert communication with energy replenishment constraints in UAV networks[J]. IEEE Transactions on Vehicular Technology, 2022, 71(9): 10143–10148. doi: 10.1109/TVT.2022.3178021.
[66]	LU Xingbo, YANG Weiwei, YAN Shihao, et al. Covertness and timeliness of data collection in UAV-aided wireless-powered IoT[J]. IEEE Internet of Things Journal, 2022, 9(14): 12573–12587. doi: 10.1109/JIOT.2021.3137846.
[67]	YAN Shihao, HANLY S V, and COLLINGS I B. Optimal transmit power and flying location for UAV covert wireless communications[J]. IEEE Journal on Selected Areas in Communications, 2021, 39(11): 3321–3333. doi: 10.1109/JSAC.2021.3088667.
[68]	RAO Hangmei, XIAO Sa, YAN Shihao, et al. Optimal geometric solutions to UAV-enabled covert communications in line-of-sight scenarios[J]. IEEE Transactions on Wireless Communications, 2022, 21(12): 10633–10647. doi: 10.1109/TWC.2022.3185492.
[69]	LIU Pengpeng, LI Zan, SI Jiangbo, et al. Joint information-theoretic secrecy and covertness for UAV-assisted wireless transmission with finite blocklength[J]. IEEE Transactions on Vehicular Technology, 2023, 72(8): 10187–10199. doi: 10.1109/TVT.2023.3254882.
[70]	ZHOU Xiaobo, YAN Shihao, NG D W K, et al. Three-dimensional placement and transmit power design for UAV covert communications[J]. IEEE Transactions on Vehicular Technology, 2021, 70(12): 13424–13429. doi: 10.1109/TVT.2021.3121298.
[71]	潘钰, 胡航, 金虎, 等. 非授权频段下无人机辅助通信的轨迹与资源分配优化[J]. 电子与信息学报, 2024, 46(11): 4287–4294. doi: 10.11999/JEIT240275. PAN Yu, HU Hang, JIN Hu, et al. Trajectory and resource allocation optimization for unmanned aerial vehicles assisted communications in unlicensed bands[J]. Journal of Electronics & Information Technology, 2024, 46(11): 4287–4294. doi: 10.11999/JEIT240275.
[72]	MAO Haobin, LIU Yanming, XIAO Zhenyu, et al. Joint resource allocation and 3-D deployment for multi-UAV covert communications[J]. IEEE Internet of Things Journal, 2024, 11(1): 559–572. doi: 10.1109/JIOT.2023.3287838.
[73]	YANG Fangtao, WANG Chao, XIONG Jun, et al. UAV-enabled robust covert communication against active wardens[J]. IEEE Transactions on Vehicular Technology, 2024, 73(6): 9159–9164. doi: 10.1109/TVT.2024.3360998.
[74]	JIANG Xu, YANG Zhutian, ZHAO Nan, et al. Resource allocation and trajectory optimization for UAV-enabled multi-user covert communications[J]. IEEE Transactions on Vehicular Technology, 2021, 70(2): 1989–1994. doi: 10.1109/TVT.2021.3053936.
[75]	李兴旺, 王新莹, 田心记, 等. 基于非理想条件可重构智能超表面辅助无线携能通信-非正交多址接入系统通感性能研究[J]. 电子与信息学报, 2024, 46(6): 2434–2442. doi: 10.11999/JEIT231395. LI Xingwang, WANG Xinying, TIAN Xinji, et al. Communication and sensing performance analysis of RIS-assisted SWIPT-NOMA system under non-ideal conditions[J]. Journal of Electronics & Information Technology, 2024, 46(6): 2434–2442. doi: 10.11999/JEIT231395.
[76]	WANG Liang, WANG Kezhi, PAN Cunhua, et al. Joint trajectory and passive beamforming design for intelligent reflecting surface-aided UAV communications: A deep reinforcement learning approach[J]. IEEE Transactions on Mobile Computing, 2023, 22(11): 6543–6553. doi: 10.1109/TMC.2022.3200998.
[77]	CHANG Bo, TANG Wei, YAN Xiaoyu, et al. Integrated scheduling of sensing, communication, and control for mmWave/THz communications in cellular connected UAV networks[J]. IEEE Journal on Selected Areas in Communications, 2022, 40(7): 2103–2113. doi: 10.1109/JSAC.2022.3157366.
[78]	XU Jinlei, LI Dongdong, ZHU Zhengyu, et al. Aerial IRS aided anti-jamming scheme for ISAC[C]. Proceedings of the 2023 IEEE 98th Vehicular Technology Conference (VTC2023-Fall), Hong Kong, China, 2023: 1–5. doi: 10.1109/VTC2023-Fall60731.2023.10333422.
[79]	NARMEEN R, ALMADHOR A, ALKHAYYAT A, et al. Secure beamforming for unmanned aerial vehicles equipped reconfigurable intelligent surfaces[J]. IEEE Internet of Things Magazine, 2024, 7(2): 30–37. doi: 10.1109/IOTM.001.2300238.
[80]	ABDALLA A S and MAROJEVIC V. ARIS for safeguarding MISO wireless communications: A deep reinforcement learning approach[C]. Proceedings of the 2022 5th International Conference on Advanced Communication Technologies and Networking (CommNet), Marrakech, Morocco, 2022: 1–6. doi: 10.1109/CommNet56067.2022.9993913.
[81]	WEI Zhiqiang, CAI Yuanxin, SUN Zhuo, et al. Sum-rate maximization for IRS-assisted UAV OFDMA communication systems[J]. IEEE Transactions on Wireless Communications, 2021, 20(4): 2530–2550. doi: 10.1109/TWC.2020.3042977.
[82]	LIANG Haoyu, WU Jun, LIU Tianle, et al. Efficient cooperative spectrum sensing in UAV-assisted cognitive wireless sensor networks[J]. IEEE Sensors Letters, 2024, 8(10): 7500904. doi: 10.1109/LSENS.2024.3454718.
[83]	DO Q T, LAKEW D S, TRAN A T, et al. A review on recent approaches in mmWave UAV-aided communication networks and open issues[C]. Proceedings of the 2023 International Conference on Information Networking (ICOIN), Bangkok, Thailand, 2023: 728–731. doi: 10.1109/ICOIN56518.2023.10049043.
[84]	OUYANG Xing and ZHAO Jian. Orthogonal chirp division multiplexing[J]. IEEE Transactions on Communications, 2016, 64(9): 3946–3957. doi: 10.1109/TCOMM.2016.2594792.
[85]	GUO Xufeng, CHEN Yuanbin, and WANG Ying. Learning-based robust and secure transmission for reconfigurable intelligent surface aided millimeter wave UAV communications[J]. IEEE Wireless Communications Letters, 2021, 10(8): 1795–1799. doi: 10.1109/LWC.2021.3081464.
[86]	KISHK M, BADER A, and ALOUINI M S. Aerial base station deployment in 6G cellular networks using tethered drones: The mobility and endurance tradeoff[J]. IEEE Vehicular Technology Magazine, 2020, 15(4): 103–111. doi: 10.1109/MVT.2020.3017885.
[87]	WU Qingqing, XU Jie, ZENG Yong, et al. A comprehensive overview on 5G-and-beyond networks with UAVs: From communications to sensing and intelligence[J]. IEEE Journal on Selected Areas in Communications, 2021, 39(10): 2912–2945. doi: 10.1109/JSAC.2021.3088681.
[88]	XIANG Lanhua, WANG Fengyu, and XU Wenjun. Multi-target tracking with dual-functional radar-communication UAV swarm[J]. IEEE Communications Letters, 2024, 28(9): 2031–2035. doi: 10.1109/LCOMM.2024.3434446.
[89]	LIU Yuemin, LIU Xin, LIU Zechen, et al. Secure rate maximization for ISAC-UAV assisted communication amidst multiple eavesdroppers[J]. IEEE Transactions on Vehicular Technology, 2024, 73(10): 15843–15847. doi: 10.1109/TVT.2024.3412805.
[90]	ZHANG Jia, WU Jun, GAN Jipeng, et al. Energy efficiency of cooperative spectrum sensing under sensing delay constraint for CUAVNs[C]. Proceedings of the 2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring), Helsinki, Finland, 2022: 1–6. doi: 10.1109/VTC2022-Spring54318.2022.9860525.