Communication, Sensing, and Computation in Low-Altitude Embodied Intelligence: From Functional Integration to Closed-Loop Coordination

LIU Peng; LUO Jihao; WANG Xinyi; FEI Zesong

doi:10.11999/JEIT261177

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LIU Peng, LUO Jihao, WANG Xinyi, FEI Zesong. Communication, Sensing, and Computation in Low-Altitude Embodied Intelligence: From Functional Integration to Closed-Loop Coordination[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT261177

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LIU Peng, LUO Jihao, WANG Xinyi, FEI Zesong. Communication, Sensing, and Computation in Low-Altitude Embodied Intelligence: From Functional Integration to Closed-Loop Coordination[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT261177

Citation:

LIU Peng, LUO Jihao, WANG Xinyi, FEI Zesong. Communication, Sensing, and Computation in Low-Altitude Embodied Intelligence: From Functional Integration to Closed-Loop Coordination[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT261177

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Communication, Sensing, and Computation in Low-Altitude Embodied Intelligence: From Functional Integration to Closed-Loop Coordination

doi: 10.11999/JEIT261177 cstr: 32379.14.JEIT261177

School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China

Funds: National Natural Science Foundation of China (No. 62471039, No. 62301032), BIT Research and Innovation Promoting Project (No. 2025YCXY015)

Accepted Date: 2026-10-16
Rev Recd Date: 2026-10-16

Available Online: 2026-10-22

Abstract

Abstract

Significance Low-altitude embodied intelligence is becoming an important technological foundation for intelligent inspection, logistics delivery, security monitoring, emergency rescue, and other low-altitude applications. Unlike conventional unmanned aerial systems that mainly rely on preset routes, remote control, or isolated perception modules, low-altitude embodied agents are required to continuously perceive the surrounding environment, exchange information with other agents and infrastructure, perform task-oriented inference, generate decisions, execute control actions, and update their behaviors according to environmental feedback. Typical application scenarios include urban infrastructure inspection, dynamic target tracking, autonomous logistics delivery, and disaster-response operations (Fig. 1). Such tasks are characterized by open and highly dynamic operating spaces, rapidly varying air-ground channels and network topologies, heterogeneous multimodal sensing data, and strict constraints on onboard payload, energy, computing capability, and storage. Sensing errors, wireless transmission delays, computing latency, and control feedback delays are therefore strongly coupled and may accumulate along the task chain. Optimizing sensing accuracy, communication rate, or computing latency separately cannot guarantee that environmental information is transformed into reliable control actions within the required time. Integrated sensing, communication, and computing (ISCC) provides a promising pathway for coordinating information acquisition, transmission, processing, and decision-making under shared spectrum, power, energy, and computing-resource constraints. Existing surveys mainly discuss network architectures, functional integration, resource management, or intelligent enhancement, whereas the complete autonomous task process of low-altitude embodied intelligence has received less systematic attention. Therefore, this review takes the closed-loop task process as the main thread and examines ISCC from the perspective of the complete perception–transmission–computation–decision–control–feedback chain, with particular attention to task-level metrics such as closed-loop latency, information freshness, task success rate, tracking performance, and control stability. Progress Research on integrated sensing, communication, and computing (ISCC) is evolving from pairwise functional integration toward task-oriented coordination. Communication–sensing integration has progressed from spectrum coexistence to waveform and hardware sharing, bidirectional assistance, multimodal perception, and networked cooperative sensing (Fig. 2). Communication–computation integration has developed from conventional cloud and edge offloading toward joint communication–computing design, over-the-air computation, federated edge learning, and distributed intelligence, reflecting a transition from serial transmission-and-computation processing toward deeper functional integration (Fig. 3). Sensing–computation integration further supports multimodal fusion, state estimation, feature extraction, and task-oriented information selection. These developments collectively provide the technical basis for coordinating perception, transmission, and processing around unified task objectives.For low-altitude embodied intelligence, a cloud–edge–device cooperative architecture can be adopted to support the closed-loop process of environmental perception, information transmission, computation and inference, decision generation, control execution, and state feedback (Fig. 4). Onboard nodes undertake time-critical functions such as local state estimation and emergency response, edge nodes support cooperative perception and latency-sensitive inference, and cloud platforms provide global coordination, model training, and long-term planning. Since control actions continuously change sensing viewpoints, wireless channels, network topology, and computing demands, the different functional modules are dynamically coupled throughout the task process. Meanwhile, the exchanged information is evolving from raw sensing data and complete tasks toward intermediate features, model parameters, and task-relevant semantic information, thereby reducing redundant transmission and processing while preserving information that is useful for perception, inference, and control.Current research can be summarized into four main directions. First, environmental information acquisition is shifting from single-node sensing toward multimodal and multi-agent cooperative perception, where complementary observations from aerial agents, ground infrastructure, and heterogeneous sensors are fused to improve environmental awareness. Second, sensing-task offloading is evolving from coarse-grained migration of complete tasks toward fine-grained cloud–edge–device collaborative inference. Model partitioning and intermediate-feature transmission allow inference workloads to be dynamically distributed according to channel quality, computing load, and task deadlines. Representative results show that joint optimization of deep neural network partitioning, sensing/communication beamforming, and multi-tier computing resources can reduce inference latency by more than 55% compared with local inference under the considered setting (Fig. 5). Third, multidimensional resource coordination jointly considers spectrum, transmit power, beamforming, computing resources, task partitioning, and UAV trajectories. Multi-resource coordination can achieve lower sensing-task completion latency than schemes optimizing only part of the available resources, especially under higher sensing-data compression ratios (Fig. 6). Finally, system optimization is extending toward decision and control feedback, with increasing attention to closed-loop latency, information freshness, tracking error, control stability, task success rate, and energy consumption. Overall, ISCC is gradually moving from resource sharing and functional integration toward task-driven optimization of the complete perception–transmission–computation–decision–control–feedback chain. Conclusions Low-altitude embodied intelligence requires an end-to-end transition from function-oriented integration to task-driven closed-loop coordination. Sensing acquires task-related environmental and agent states; communication exchanges sensing data, cooperative information, intermediate features, and control commands; and computation transforms the acquired information into environmental understanding, task inference, and decision support. Decisions are then converted into control actions that change the physical state of aerial agents and, in turn, reshape the conditions of subsequent sensing, transmission, and computation. Therefore, the essential role of ISCC is not simply to combine three functions or improve their individual efficiencies, but to coordinate the complete closed-loop process according to the final task objective. Task deadlines and control risks should determine sensing-update intervals, information granularity, offloading locations, communication priorities, and computing-resource allocation, while control-induced state variations should be fed back to subsequent sensing modes, link selection, task placement, and resource scheduling. The reviewed studies further indicate that low-altitude ISCC is evolving along three related dimensions: from pairwise integration to multi-functional coordination, from static single-node processing to cloud–edge–device and multi-agent cooperation, and from module-level performance metrics to task-level closed-loop objectives. This evolution provides a technical basis for supporting safer, more autonomous, and more scalable operation of low-altitude embodied agents. Prospects Several challenges remain before large-scale deployment of low-altitude embodied intelligence can be supported. First, unified sensing–communication–computation–control models are required to characterize the propagation and coupling of sensing uncertainty, communication unreliability, computing and inference errors, control feedback delays, and system dynamics within a common framework. Environmental state, information freshness, semantic value, inference accuracy, control cost, and system stability should be jointly represented so that task requirements can be mapped backward to sensing, communication, and computing constraints. Second, real-time coordination under highly dynamic environments remains difficult because aerial mobility, intermittent blockage, interference, changing network topology, and time-varying computing loads can rapidly invalidate static resource allocation. Predictive scheduling, lightweight edge inference, dynamic model partitioning, elastic cloud–edge–device migration, digital twins, and online learning are promising directions for rapid adaptation under incomplete information and resource fluctuations. Third, security, privacy, and trustworthy intelligence need to be incorporated throughout sensing, transmission, inference, and control. Identity authentication, trusted access, privacy-preserving computation, anomaly detection, uncertainty evaluation, interpretable decision-making, and secure control should be jointly considered because attacks on data, models, wireless links, or control commands may directly affect physical safety. Finally, large-scale heterogeneous swarms require scalable hierarchical and distributed coordination. As the number of agents increases, communication overhead, state-space dimension, and decision complexity grow rapidly, while purely centralized solutions face bandwidth bottlenecks, single-point failures, and excessive computational burden. Hierarchical and distributed architectures, graph learning, multi-agent reinforcement learning, swarm intelligence, semantic communication, event-triggered interaction, and knowledge sharing may reduce redundant information exchange and improve self-organization, self-recovery, and task reconfiguration.
- Low-altitude embodied intelligence,
- Integrated sensing, communication, and computing,
- Edge computing,
- Closed-loop coordination

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References(79)

References

[1]	国务院. 政府工作报告[EB/OL]. https://www.gov.cn/gongbao/2024/issue_11246/202403/content_6941846.html, 2024. State Council of the People’s Republic of China. Report on the work of the government[EB/OL]. https://www.gov.cn/gongbao/2024/issue_11246/202403/content_6941846.html, 2024. (查阅网上资料,未找到对应的英文翻译,请确认).
[2]	中共中央. 中共中央关于进一步全面深化改革、推进中国式现代化的决定[R]. 2024. (查阅网上资料, 未找到本条文献信息, 请确认). Central Committee of the Communist Party of China. Resolution of the central committee of the communist party of china on further deepening reform comprehensively to advance Chinese modernization[R]. 2024.
[3]	国务院. 政府工作报告[EB/OL]. https://www.gov.cn/yaowen/liebiao/202503/content_7013163.htm, 2025. State Council of the People’s Republic of China. Report on the work of the government[EB/OL]. https://www.gov.cn/yaowen/liebiao/202503/content_7013163.htm, 2025. (查阅网上资料,未找到对应的英文翻译,请确认).
[4]	国务院. 政府工作报告[EB/OL]. https://www.gov.cn/gongbao/2026/issue_12646/202603/content_7064134.html, 2026. State Council of the People’s Republic of China. Report on the work of the government[EB/OL]. https://www.gov.cn/gongbao/2026/issue_12646/202603/content_7064134.html, 2026. (查阅网上资料,未找到对应的英文翻译,请确认).
[5]	工业和信息化部, 科学技术部, 财政部, 等. 工业和信息化部科学技术部财政部中国民用航空局关于印发《通用航空装备创新应用实施方案(2024-2030年)》的通知[EB/OL]. https://www.gov.cn/zhengce/zhengceku/202403/content_6942115.htm, 2024. Ministry of Industry and Information Technology, Ministry of Science and Technology, Ministry of Finance, et al. Implementation plan for innovative applications of general aviation equipment (2024–2030)[EB/OL]. https://www.gov.cn/zhengce/zhengceku/202403/content_6942115.htm, 2024. (查阅网上资料,未找到对应的英文翻译,请确认).
[6]	中华人民共和国中央人民政府. 中华人民共和国国民经济和社会发展第十五个五年规划纲要[EB/OL]. https://www.gov.cn/yaowen/liebiao/202603/content_7062633.htm, 2026. The Central People’s Government of the People's Republic of China. Outline of the 15th five-year plan for national economic and social development of the People’s Republic of China[EB/OL]. https://www.gov.cn/yaowen/liebiao/202603/content_7062633.htm, 2026.
[7]	FEI Zesong, WANG Xinyi, WU Nan, et al. Air-ground integrated sensing and communications: Opportunities and challenges[J]. IEEE Communications Magazine, 2023, 61(5): 55–61. doi: 10.1109/MCOM.007.2200459.
[8]	MU Junsheng, ZHANG Ronghui, CUI Yuanhao, et al. UAV meets integrated sensing and communication: Challenges and future directions[J]. IEEE Communications Magazine, 2023, 61(5): 62–67. doi: 10.1109/MCOM.008.2200510.
[9]	DUAN Jiafei, YU S, TAN Huili, et al. A survey of embodied AI: From simulators to research tasks[J]. IEEE Transactions on Emerging Topics in Computational Intelligence, 2022, 6(2): 230–244. doi: 10.1109/TETCI.2022.3141105.
[10]	LUO Jihao, FEI Zesong, WANG Xinyi, et al. Toward low-altitude embodied intelligence: A sensing-communication-computation-control closed-loop perspective[J]. IEEE Communications Magazine, 2026, 64(9): 110–117. doi: 10.1109/MCOM.001.2600091.
[11]	WEN Dingzhu, ZHOU Yong, LI Xiaoyang, et al. A survey on integrated sensing, communication, and computation[J]. IEEE Communications Surveys & Tutorials, 2025, 27(5): 3058–3098. doi: 10.1109/COMST.2024.3521498.
[12]	LIU Fan, CUI Yuanhao, MASOUROS C, et al. Integrated sensing and communications: Toward dual-functional wireless networks for 6G and beyond[J]. IEEE Journal on Selected Areas in Communications, 2022, 40(6): 1728–1767. doi: 10.1109/JSAC.2022.3156632.
[13]	吴子君, 张海君, 马旭, 等. 6G通感算一体化体系架构与关键技术[J]. 电子与信息学报, 2025, 47(4): 876–887. doi: 10.11999/JEIT241151. WU Zijun, ZHANG Haijun, MA Xu, et al. System architecture and key technologies of 6G integrated sensing, communication, and computing[J]. Journal of Electronics & Information Technology, 2025, 47(4): 876–887. doi: 10.11999/JEIT241151.
[14]	武燕燕, 吴松, 邓伟. 6G天地一体通感算智能协同网络资源管理技术综述[J]. 电子与信息学报, 2025, 47(8): 2448–2472. doi: 10.11999/JEIT250140. WU Yanyan, WU Song, and DENG Wei. An overview of resource management technology of 6G integrated communication, sensing, and computation enabled satellite-terrestrial intelligent network[J]. Journal of Electronics & Information Technology, 2025, 47(8): 2448–2472. doi: 10.11999/JEIT250140.
[15]	朱政宇, 殷梦琳, 姚信威, 等. AI赋能的通感算一体化关键技术研究综述[J]. 电子与信息学报, 2025, 47(10): 3426–3438. doi: 10.11999/JEIT250242. ZHU Zhengyu, YIN Menglin, YAO Xinwei, et al. Overview of the research on key technologies for AI-powered integrated sensing, communication and computing[J]. Journal of Electronics & Information Technology, 2025, 47(10): 3426–3438. doi: 10.11999/JEIT250242.
[16]	GU Xiaohui and ZHANG Guoan. A survey on UAV-assisted wireless communications: Recent advances and future trends[J]. Computer Communications, 2023, 208: 44–78. doi: 10.1016/j.comcom.2023.05.013.
[17]	MARSHALL J A, SUN Wei, and L’AFFLITTO A. A survey of guidance, navigation, and control systems for autonomous multi-rotor small unmanned aerial systems[J]. Annual Reviews in Control, 2021, 52: 390–427. doi: 10.1016/j.arcontrol.2021.10.013.
[18]	CHIRIYATH A R, PAUL B, and BLISS D W. Radar-communications convergence: Coexistence, cooperation, and co-design[J]. IEEE Transactions on Cognitive Communications and Networking, 2017, 3(1): 1–12. doi: 10.1109/TCCN.2017.2666266.
[19]	HASSANIEN A, AMIN M G, ABOUTANIOS E, 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.
[20]	DONG Fuwang, LIU Fan, LU Shihang, et al. Communication-assisted sensing in 6G networks[J]. IEEE Journal on Selected Areas in Communications, 2025, 43(4): 1371–1386. doi: 10.1109/JSAC.2025.3531548.
[21]	NISHIO T, KODA Y, PARK J, et al. When wireless communications meet computer vision in beyond 5G[J]. IEEE Communications Standards Magazine, 2021, 5(2): 76–83. doi: 10.1109/MCOMSTD.001.2000047.
[22]	ALKHATEEB A, CHARAN G, OSMAN T, et al. DeepSense 6G: A large-scale real-world multi-modal sensing and communication dataset[J]. IEEE Communications Magazine, 2023, 61(9): 122–128. doi: 10.1109/MCOM.006.2200730.
[23]	CUI Yue, DING Haichuan, ZHAO Lian, et al. Integrated sensing and communication: A network level perspective[J]. IEEE Wireless Communications, 2024, 31(1): 103–109. doi: 10.1109/MWC.015.2200275.
[24]	OSORIO D P M, BARUA B, BESSER K L, et al. The rise of networked ISAC: Emerging aspects and challenges[J]. IEEE Open Journal of the Communications Society, 2025, 6: 5072–5091. doi: 10.1109/OJCOMS.2025.3575729.
[25]	KENNEY R H, MERLO J M, MGHABGHAB S R, et al. Wireless distributed frequency and phase synchronization for coherent distributed arrays[J]. IEEE Transactions on Microwave Theory and Techniques, 2024, 72(8): 4723–4736. doi: 10.1109/TMTT.2024.3367017.
[26]	CAMAJORI TEDESCHINI B, BRAMBILLA M, BARBIERI L, et al. Cooperative LiDAR sensing for pedestrian detection: Data association based on message passing neural networks[J]. IEEE Transactions on Signal Processing, 2023, 71: 3028–3042. doi: 10.1109/TSP.2023.3304002.
[27]	YANG Xiaoyu, WEI Zhiqing, XU Jie, et al. Coordinated transmit beamforming for networked ISAC with imperfect CSI and time synchronization[J]. IEEE Transactions on Wireless Communications, 2024, 23(12): 18019–18035. doi: 10.1109/TWC.2024.3459036.
[28]	NIU Yangyang, WEI Zhiqing, WANG Lin, et al. Interference management for integrated sensing and communication systems: A survey[J]. IEEE Internet of Things Journal, 2025, 12(7): 8110–8134. doi: 10.1109/JIOT.2024.3506162.
[29]	HU Yue, FANG Shaoheng, LEI Zixing, et al. Where2comm: Communication-efficient collaborative perception via spatial confidence maps[C]. Proceedings of the 36th Conference on Neural Information Processing Systems, New Orleans, USA, 2022: 4874–4886.
[30]	MAO Yuyi, ZHANG Jun, and LETAIEF K B. Dynamic computation offloading for mobile-edge computing with energy harvesting devices[J]. IEEE Journal on Selected Areas in Communications, 2016, 34(12): 3590–3605. doi: 10.1109/JSAC.2016.2611964.
[31]	TRAN T X and POMPILI D. Joint task offloading and resource allocation for multi-server mobile-edge computing networks[J]. IEEE Transactions on Vehicular Technology, 2019, 68(1): 856–868. doi: 10.1109/TVT.2018.2881191.
[32]	CHEN Min, HAO Yixue, LI Yong, et al. On the computation offloading at ad hoc cloudlet: Architecture and service modes[J]. IEEE Communications Magazine, 2015, 53(6): 18–24. doi: 10.1109/MCOM.2015.7120041.
[33]	ZHU Guangxu and HUANG Kaibin. MIMO over-the-air computation for high-mobility multimodal sensing[J]. IEEE Internet of Things Journal, 2019, 6(4): 6089–6103. doi: 10.1109/JIOT.2018.2871070.
[34]	YOU Lizhao, ZHAO Xinbo, CAO Rui, et al. Broadband digital over-the-air computation for wireless federated edge learning[J]. IEEE Transactions on Mobile Computing, 2024, 23(5): 5212–5228. doi: 10.1109/TMC.2023.3304652.
[35]	ZHANG Deyou, XIAO Ming, PANG Zhibo, et al. IRS assisted federated learning: A broadband over-the-air aggregation approach[J]. IEEE Transactions on Wireless Communications, 2024, 23(5): 4069–4082. doi: 10.1109/TWC.2023.3313968.
[36]	MO Xiaopeng and XU Jie. Energy-efficient federated edge learning with joint communication and computation design[J]. Journal of Communications and Information Networks, 2021, 6(2): 110–124. doi: 10.23919/JCIN.2021.9475121.
[37]	WANG Sihua, CHEN Mingzhe, SHEN Cong, et al. Digital over-the-air federated learning in multi-antenna systems[J]. IEEE Transactions on Wireless Communications, 2024, 23(10): 15125–15141. doi: 10.1109/TWC.2024.3425732.
[38]	AZIMI-ABARGHOUYI S M and FODOR V. Scalable hierarchical over-the-air federated learning[J]. IEEE Transactions on Wireless Communications, 2024, 23(8): 8480–8496. doi: 10.1109/TWC.2024.3350923.
[39]	QAISAR S, BILAL R M, IQBAL W, et al. Compressive sensing: From theory to applications, a survey[J]. Journal of Communications and Networks, 2013, 15(5): 443–456. doi: 10.1109/JCN.2013.000083.
[40]	BALTRUŠAITIS T, AHUJA C, and MORENCY L P. Multimodal machine learning: A survey and taxonomy[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, 41(2): 423–443. doi: 10.1109/TPAMI.2018.2798607.
[41]	LIU Peixi, ZHU Guangxu, WANG Shuai, et al. Toward ambient intelligence: Federated edge learning with task-oriented sensing, computation, and communication integration[J]. IEEE Journal of Selected Topics in Signal Processing, 2023, 17(1): 158–172. doi: 10.1109/JSTSP.2022.3226836.
[42]	QIAO Donghao and ZULKERNINE F. Adaptive feature fusion for cooperative perception using LiDAR point clouds[C]. 2023 IEEE/CVF Winter Conference on Applications of Computer Vision, Waikoloa, USA, 2023: 1186–1195. doi: 10.1109/WACV56688.2023.00124.
[43]	MAN Yunze, GUI Liangyan, and WANG Yuxiong. BEV-guided multi-modality fusion for driving perception[C]. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, Canada, 2023: 21960–21969. doi: 10.1109/CVPR52729.2023.02103.
[44]	王新奕, 费泽松, 周一青, 等. 面向物联网的通感算智融合: 关键技术与未来展望[J]. 电子与信息学报, 2025, 47(4): 888–908. doi: 10.11999/JEIT240806. WANG Xinyi, FEI Zesong, ZHOU Yiqing, et al. Integrated sensing, communication, computation, and intelligence towards IoT: Key technologies and future directions[J]. Journal of Electronics & Information Technology, 2025, 47(4): 888–908. doi: 10.11999/JEIT240806.
[45]	ZHUANG Zeming, WEN Dingzhu, SHI Yuanming, et al. Integrated sensing-communication-computation for over-the-air edge AI inference[J]. IEEE Transactions on Wireless Communications, 2024, 23(4): 3205–3220. doi: 10.1109/TWC.2023.3306465.
[46]	WANG Diao, WEN Dingzhu, HE Yinghui, et al. Joint device scheduling and resource allocation for ISCC-based multiview–multitask inference[J]. IEEE Internet of Things Journal, 2024, 11(24): 40814–40830. doi: 10.1109/JIOT.2024.3456569.
[47]	LIU Shuai, WEN Dingzhu, LI Da, et al. Energy-efficient optimal mode selection for edge AI inference via integrated sensing-communication-computation[J]. IEEE Transactions on Mobile Computing, 2024, 23(12): 14248–14262. doi: 10.1109/TMC.2024.3440581.
[48]	ZHAO Junhui, REN Ruixing, ZOU Dan, et al. IoV-oriented integrated sensing, computation, and communication: System design and resource allocation[J]. IEEE Transactions on Vehicular Technology, 2024, 73(11): 16283–16294. doi: 10.1109/TVT.2024.3422270.
[49]	DENG Cailian, FANG Xuming, and WANG Xianbin. Integrated sensing, communication, and computation with adaptive DNN splitting in multi-UAV networks[J]. IEEE Transactions on Wireless Communications, 2024, 23(11): 17429–17445. doi: 10.1109/TWC.2024.3453650.
[50]	HOU Peng, HUANG Yi, ZHU Hongbin, et al. Distributed DRL-based integrated sensing, communication, and computation in cooperative UAV-enabled intelligent transportation systems[J]. IEEE Internet of Things Journal, 2025, 12(5): 5792–5806. doi: 10.1109/JIOT.2024.3489655.
[51]	LI Guoliang, WANG Shuai, YE Kejiang, et al. Multi-point integrated sensing and communication: Fusion model and functionality selection[J]. IEEE Wireless Communications Letters, 2022, 11(12): 2660–2664. doi: 10.1109/LWC.2022.3213883.
[52]	ZHOU Longyu, LENG Supeng, WANG Qing, et al. Integrated sensing and communication in UAV swarms for cooperative multiple targets tracking[J]. IEEE Transactions on Mobile Computing, 2023, 22(11): 6526–6542. doi: 10.1109/TMC.2022.3193499.
[53]	WANG Binglu, ZHANG Lei, WANG Zhaozhong, et al. Core: Cooperative reconstruction for multi-agent perception[C]. 2023 IEEE/CVF International Conference on Computer Vision, Paris, France, 2023: 8676–8686. doi: 10.1109/ICCV51070.2023.00800.
[54]	HONG Shixin, LIU Yu, LI Zhi, et al. Multi-agent collaborative perception via motion-aware robust communication network[C]. 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, USA, 2024: 15301–15310. doi: 10.1109/CVPR52733.2024.01449.
[55]	RASHID M and NANZER J A. Frequency and phase synchronization in distributed antenna arrays based on consensus averaging and Kalman filtering[J]. IEEE Transactions on Wireless Communications, 2023, 22(4): 2789–2803. doi: 10.1109/TWC.2022.3213788.
[56]	SONG Zhiying, YANG Lei, WEN Fuxi, et al. TraF-Align: Trajectory-aware feature alignment for asynchronous multi-agent perception[C]. 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, USA, 2025: 12048–12057. doi: 10.1109/CVPR52734.2025.01125.
[57]	LIU Sifan, LIU Rang, LU Zhiping, et al. Cooperative cell-free ISAC networks: Joint BS mode selection and beamforming design[C]. 2024 IEEE Wireless Communications and Networking Conference, Dubai, United Arab Emirates, 2024: 1–6. doi: 10.1109/WCNC57260.2024.10571110.
[58]	WANG Yi, ZU Keke, XIANG Luping, et al. ISAC enabled cooperative detection for cellular-connected UAV network[J]. IEEE Transactions on Wireless Communications, 2025, 24(2): 1541–1554. doi: 10.1109/TWC.2024.3509978.
[59]	MENG Kaitao, MASOUROS C, CHEN Guangji, et al. Network-level integrated sensing and communication: Interference management and BS coordination using stochastic geometry[J]. IEEE Transactions on Wireless Communications, 2024, 23(12): 19365–19381. doi: 10.1109/TWC.2024.3483031.
[60]	DING Changfeng, WANG Junbo, ZHANG Hua, et al. Joint MIMO precoding and computation resource allocation for dual-function radar and communication systems with mobile edge computing[J]. IEEE Journal on Selected Areas in Communications, 2022, 40(7): 2085–2102. doi: 10.1109/JSAC.2022.3157389.
[61]	HUANG Ning, DOU Chenglong, WU Yuan, et al. Unmanned-aerial-vehicle-aided integrated sensing and computation with mobile-edge computing[J]. IEEE Internet of Things Journal, 2023, 10(19): 16830–16844. doi: 10.1109/JIOT.2023.3270332.
[62]	LIU Peng, FEI Zesong, WANG Xinyi, et al. Joint beamforming and offloading design for integrated sensing, communication, and computation system[J]. IEEE Transactions on Vehicular Technology, 2025, 74(9): 14933–14937. doi: 10.1109/TVT.2025.3561430.
[63]	LIU Peng, FEI Zesong, WANG Xinyi, et al. Joint offloading and beamforming design in integrating sensing, communication, and computing systems: A distributed approach[J]. IEEE Transactions on Communications, 2025, 73(7): 4697–4712. doi: 10.1109/TCOMM.2024.3516488.
[64]	LIU Peng, FEI Zesong, WANG Xinyi, et al. Toward intelligent edge sensing for ISCC network: Joint multi-tier DNN partitioning and beamforming design[J]. IEEE Transactions on Wireless Communications, 2026, 25: 6774–6789. doi: 10.1109/TWC.2025.3626555.
[65]	LIU Peng, TANG Shuntian, LIN Ke, et al. Joint split inference design and bandwidth allocation for intelligent edge perception network[C]. 2025 IEEE/CIC International Conference on Communications in China, Shanghai, China, 2025: 388–393. doi: 10.1109/ICCCWorkshops67136.2025.11148170.
[66]	WANG Zhaolin, MU Xidong, LIU Yuanwei, et al. NOMA-aided joint communication, sensing, and multi-tier computing systems[J]. IEEE Journal on Selected Areas in Communications, 2023, 41(3): 574–588. doi: 10.1109/JSAC.2022.3229447.
[67]	HUANG Ning, DOU Chenglong, WU Yuan, et al. Mobile edge computing aided integrated sensing and communication with short-packet transmissions[J]. IEEE Transactions on Wireless Communications, 2024, 23(7): 7759–7774. doi: 10.1109/TWC.2023.3344479.
[68]	CHENG Kaijun, FANG Xuming, and WANG Xianbin. Optimized resource allocation and time partitioning for integrated communication, sensing, and edge computing network[J]. Computer Communications, 2022, 194: 240–249. doi: 10.1016/j.comcom.2022.07.030.
[69]	ZHAO Lindong, WU Dan, ZHOU Liang, et al. Radio resource allocation for integrated sensing, communication, and computation networks[J]. IEEE Transactions on Wireless Communications, 2022, 21(10): 8675–8687. doi: 10.1109/TWC.2022.3168348.
[70]	LI Zhenyu, FU Yuchuan, TIAN Mengqiu, et al. Cooperative sensing, communication and computation resource allocation in mobile edge computing-enabled vehicular networks[J]. Journal of Information and Intelligence, 2024, 2(4): 339–354. doi: 10.1016/j.jiixd.2024.02.006.
[71]	LIU Peng, WANG Xinyi, FEI Zesong, et al. Latency minimization oriented radio and computation resource allocations for 6G V2X networks with ISCC[J]. IEEE Transactions on Communications, 2025, 73(12): 15851–15865. doi: 10.1109/TCOMM.2025.3616242.
[72]	ZHOU Yige, LIU Xin, ZHAI Xiangping, et al. UAV-enabled integrated sensing, computing, and communication for Internet of Things: Joint resource allocation and trajectory design[J]. IEEE Internet of Things Journal, 2024, 11(7): 12717–12727. doi: 10.1109/JIOT.2023.3335937.
[73]	PENG Sicong, LI Bin, LIU Lei, et al. Trajectory design and resource allocation for multi-UAV-assisted sensing, communication, and edge computing integration[J]. IEEE Transactions on Communications, 2025, 73(4): 2847–2861. doi: 10.1109/TCOMM.2024.3478115.
[74]	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.
[75]	LI Qingliang, LI Bin, HE Zhenqing, et al. Joint design of communication sensing and control with a UAV platform[J]. IEEE Transactions on Wireless Communications, 2024, 23(12): 19231–19244. doi: 10.1109/TWC.2024.3480705.
[76]	JIN Haijia, WU Jun, YUAN Weijie, et al. Co-design of sensing, communications, and control for low-altitude wireless networks[J]. IEEE Transactions on Mobile Computing, 2025, 24(11): 12035–12048. doi: 10.1109/TMC.2025.3581616.
[77]	MAO Sun, YUEN C, LIU Lei, et al. RIS-enhanced semantic-aware sensing, communication, computation, and control for internet of things[J]. IEEE Transactions on Wireless Communications, 2026, 25: 2231–2246. doi: 10.1109/TWC.2025.3595550.
[78]	FANG Xinran, LEI Chengleyang, FENG Wei, et al. Sensing-communication-computing-control closed-loop optimization for 6G digital twin-empowered robotic systems[J]. IEEE Journal on Selected Areas in Communications, 2025, 43(10): 3330–3346. doi: 10.1109/JSAC.2025.3574601.
[79]	KHAN M A, KUMAR N, ALSAMHI S H, et al. Security and privacy issues and solutions for UAVs in B5G networks: A review[J]. IEEE Transactions on Network and Service Management, 2025, 22(1): 892–912. doi: 10.1109/TNSM.2024.3487265.