Advanced Search
Turn off MathJax
Article Contents
ZHU Zhengyu, WEN Xinping, LI Xingwang, WEI Zhiqing, ZHANG Peichang, LIU Fan, FENG Zhiyong. An Overview on Integrated Sensing and Communication for Low altitude economy[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250747
Citation: ZHU Zhengyu, WEN Xinping, LI Xingwang, WEI Zhiqing, ZHANG Peichang, LIU Fan, FENG Zhiyong. An Overview on Integrated Sensing and Communication for Low altitude economy[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250747

An Overview on Integrated Sensing and Communication for Low altitude economy

doi: 10.11999/JEIT250747 cstr: 32379.14.JEIT250747
Funds:  The Program for Science & Technology Innovation Talents in Universities of Henan Province (23HASTIT019), Henan Provincial Natural Science Foundation Excellent Youth Project (232300421097), The Natural Science Foundation of Henan Province (232300421097)
  • Received Date: 2025-08-12
  • Accepted Date: 2025-11-03
  • Rev Recd Date: 2025-11-03
  • Available Online: 2025-11-11
  • The Low-altitude Internet of Things (IoT) develops rapidly, and the Low Altitude Economy is treated as a national strategic emerging industry. Integrated Sensing and Communication (ISAC) for the Low Altitude Economy is expected to support more complex tasks in complex environments and provides a foundation for improved security, flexibility, and multi-application scenarios for drones. This paper presents an overview of ISAC for the Low Altitude Economy. The theoretical foundations of ISAC and the Low Altitude Economy are summarized, and the advantages of applying ISAC to the Low Altitude Economy are discussed. Potential applications of key 6G technologies, such as covert communication and Millimeter-Wave (mm-wave) systems in ISAC for the Low Altitude Economy, are examined. The key technical challenges of ISAC for the Low Altitude Economy in future development are also summarized.  Significance   The integration of UAVs with ISAC technology is expected to provide considerable advantages in future development. When ISAC is applied, the overall system payload can be reduced, which improves UAV maneuverability and operational freedom. This integration offers technical support for versatile UAV applications. With ISAC, low-altitude network systems can conduct complex tasks in challenging environments. UAV platforms equipped with a single function do not achieve the combined improvement in communication and sensing that ISAC enables. ISAC-equipped drones are therefore expected to be used more widely in aerial photography, agriculture, surveying, remote sensing, and telecommunications. This development will advance related theoretical and technical frameworks and broaden the application scope of ISAC.  Progress  ISAC networks for the low-altitude economy offer efficient and flexible solutions for military reconnaissance, emergency disaster relief, and smart city management. The open aerial environment and dynamic deployment requirements create several challenges. Limited stealth increases exposure to hostile interception, and complex terrains introduce signal obstruction. High bandwidth and low latency are also required. Academic and industrial communities have investigated technologies such as covert communication, intelligent reflecting surfaces, and mm-wave communication to enhance the reliability and intelligence of ISAC in low-altitude operational scenarios.  Conclusions  This paper presents an overview of current applications, critical technologies, and ongoing challenges associated with ISAC in low-altitude environments. It examines the integration of emerging 6G technologies, including covert communication, Reconfigurable Intelligent Surfaces (RIS), and mm-wave communication within ISAC frameworks. Given the dynamic and complex characteristics of low-altitude operations, recent advances in UAV swarm power control algorithms and covert trajectory optimization based on deep reinforcement learning are summarized. Key unresolved challenges are also identified, such as spatiotemporal synchronization, multi-UAV resource allocation, and privacy preservation, which provide reference directions for future research.  Prospects   ISAC technology provides precise and reliable support for drone logistics, urban air mobility, and large-scale environmental monitoring in the low-altitude economy. Large-scale deployment of ISAC systems in complex and dynamic low-altitude environments remains challenging. Major obstacles include limited coordination and resource allocation within UAV swarms, spatiotemporal synchronization across heterogeneous devices, competing requirements between sensing and communication functions, and rising concerns regarding privacy and security in open airspace. These issues restrict the high-quality development of the low-altitude economy.
  • loading
  • [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.

    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]. 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]. 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, and 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]. 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]. 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]. 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]. 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]. 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]. 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]. 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]. 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]. 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]. 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]. 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]. 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]. 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]. The 2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring), Helsinki, Finland, 2022: 1–6. doi: 10.1109/VTC2022-Spring54318.2022.9860525.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(3)  / Tables(2)

    Article Metrics

    Article views (130) PDF downloads(39) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return