Overview of the Research on Key Technologies for AI-powered Integrated Sensing, Communication and Computing

ZHU Zhengyu; YIN Menglin; YAO Xinwei; XU Yongjun; SUN Gangcan; XU Mingliang

doi:10.11999/JEIT250242

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ZHU Zhengyu, YIN Menglin, YAO Xinwei, XU Yongjun, SUN Gangcan, XU Mingliang. Overview of the Research on Key Technologies for AI-powered Integrated Sensing, Communication and Computing[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250242

Citation:

ZHU Zhengyu, YIN Menglin, YAO Xinwei, XU Yongjun, SUN Gangcan, XU Mingliang. Overview of the Research on Key Technologies for AI-powered Integrated Sensing, Communication and Computing[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250242

Citation:

ZHU Zhengyu, YIN Menglin, YAO Xinwei, XU Yongjun, SUN Gangcan, XU Mingliang. Overview of the Research on Key Technologies for AI-powered Integrated Sensing, Communication and Computing[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250242

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Overview of the Research on Key Technologies for AI-powered Integrated Sensing, Communication and Computing

doi: 10.11999/JEIT250242 cstr: 32379.14.JEIT250242

1.
School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China
2.
School of Compute Science and Technology, Zhejiang University Of Technology, Hangzhou 310014, China
3.
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
4.
School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China

Received Date: 2025-04-07
Rev Recd Date: 2025-06-04

Available Online: 2025-06-14

Abstract

Abstract

The Integration of Sensing, Communication and Computing (ISCC) combined with Artificial Intelligence(AI) algorithms has emerged as a critical enabler of Sixth-Generation (6G) networks due to its high spectral efficiency and low hardware cost. AI-powered ISCC systems, which combine sensing, communication, computing, and intelligent algorithms, support fast data processing, real-time resource allocation, and adaptive decision-making in complex and dynamic environments. These systems are increasingly applied in intelligent vehicular networks—including Unmanned Aerial Vehicles (UAVs) and autonomous driving—as well as in radar, positioning, tracking, and beamforming. This overview outlines the development and advantages of AI-enabled ISCC systems, focusing on performance benefits, application potential, evaluation metrics, and enabling technologies. It concludes by discussing future research directions. Future 6G networks are expected to evolve beyond data transmission to form an integrated platform that unifies sensing, communication, computing, and intelligence, enabling pervasive AI services. Significance AI-powered ISCC marks a transformative shift in wireless communication, enabling more efficient spectrum utilization, reduced hardware cost, and improved adaptability in complex environments. This integration is central to the development of 6G networks, which aim to deliver intelligent and efficient services across applications such as autonomous vehicles, UAVs, and smart cities. The significance of this research lies in its potential to reshape the management and optimization of communication, sensing, and computing resources, advancing the realization of a ubiquitously connected and intelligent infrastructure. Progress Recent advances in AI—particularly in machine learning, deep learning, and reinforcement learning—have substantially improved the performance of ISCC systems. These methods enable real-time data processing, intelligent resource management, and adaptive decision-making, which are critical for future 6G requirements. Notable progress includes AI-driven waveform design, beamforming, channel estimation, and dynamic spectrum allocation, all of which enhance ISCC efficiency and reliability. Additionally, the integration of edge computing and federated learning has mitigated challenges related to latency, data privacy, and scalability, facilitating broader deployment of AI-enabled ISCC systems. Conclusions Research on AI-powered ISCC systems highlights the benefits of integrating AI with sensing, communication, and computing. AI algorithms improve resource efficiency, sensing precision, and real-time adaptability, making ISCC systems well suited for dynamic and complex environments. The adoption of lightweight models and distributed learning has broadened applicability to resource-limited platforms such as drones and IoT sensors. Overall, AI-enabled ISCC systems advance the realization of 6G networks, where sensing, communication, and computing are unified to support intelligent and ubiquitous services. Prospects The advancement of AI-powered ISCC systems depends on addressing key challenges, including data quality, model complexity, security, and real-time performance. Future research should focus on developing robust AI models capable of generalizing across diverse wireless environments. Progress in lightweight AI and edge computing will be critical for deployment in resource-constrained devices. The integration of multi-modal data and the design of secure, privacy-preserving algorithms will be essential to ensure system reliability and safety. As 6G networks evolve, AI-powered ISCC systems are expected to underpin intelligent, efficient, and secure communication infrastructures, reshaping human-technology interaction in the digital era.
- 6G,
- Artificial Intelligence (AI),
- Integration of Sensing Communication and Computing (ISCC),
- Deep reinforcement learning

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References(35)

References

[1]	ZHANG Shunqing, XIANG Chenlu, and XU Shugong. 6G: Connecting everything by 1000 times price reduction[J]. IEEE Open Journal of Vehicular Technology, 2020, 1: 107–115. doi: 10.1109/OJVT.2020.2980003.
[2]	余显祥, 姚雪, 杨婧, 等. 面向感知应用的通感一体化信号设计技术与综述[J]. 雷达学报, 2023, 12(2): 247–261. doi: 10.12000/JR23015. YU Xianxiang, YAO Xue, YANG Jing, et al. Radar-centric DFRC signal design: Overview and future research avenues[J]. Journal of Radars, 2023, 12(2): 247–261. doi: 10.12000/JR23015.
[3]	TAN D K P, HE Jia, LI Yanchun, et al. Integrated sensing and communication in 6G: Motivations, use cases, requirements, challenges and future directions[C]. IEEE International Online Symposium on Joint Communications & Sensing (JC&S), Dresden, Germany, 2021: 1–6. doi: 10.1109/JCS52304.2021.9376324.
[4]	WU Nan, JIANG Rongkun, WANG Xinyi, et al. AI-enhanced integrated sensing and communications: Advancements, challenges, and prospects[J]. IEEE Communications Magazine, 2024, 62(9): 144–150. doi: 10.1109/MCOM.001.2300724.
[5]	林奕森, 甘德樵, 葛晓虎. AI赋能6G: 绿色通信的未来[J]. 移动通信, 2024, 48(8): 20–24, 55. doi: 10.3969/j.issn.1006-1010.20240621-0001. LIN Yisen, GAN Deqiao, and GE Xiaohu. AI-powered 6G: The future of green communications[J]. Mobile Communications, 2024, 48(8): 20–24, 55. doi: 10.3969/j.issn.1006-1010.20240621-0001.
[6]	闫实, 彭木根, 王文博. 通信-感知-计算融合: 6G愿景与关键技术[J]. 北京邮电大学学报, 2021, 44(4): 1–11. doi: 10.13190/j.jbupt.2021-081. YAN Shi, PENG Mugen, and WANG Wenbo. Integration of communication, sensing and computing: The vision and key technologies of 6G[J]. Journal of Beijing University of Posts and Telecommunications, 2021, 44(4): 1–11. doi: 10.13190/j.jbupt.2021-081.
[7]	王辉, 孟士尧, 贾敏. 空天地一体化场景中的6G通感算融合与数字孪生技术[J]. 无线电通信技术, 2024, 50(6): 1057–1066. doi: 10.3969/j.issn.1003-3114.2024.06.001. WANG Hui, MENG Shiyao, and JIA Min. 6G communication-sensing-computing integrated space-air-ground digital twin network[J]. Radio Communications Technology, 2024, 50(6): 1057–1066. doi: 10.3969/j.issn.1003-3114.2024.06.001.
[8]	陈新宇, 王卫斌, 陆光辉. 基于AI agent的6G内生智能技术框架及其应用[J]. 移动通信, 2024, 48(7): 28–32. doi: 10.3969/j.issn.1006-1010.20240613-0001. CHEN Xinyu, WANG Weibin, and LU Guanghui. 6G native intelligent technology framework and its application based on AI agent[J]. Mobile Communications, 2024, 48(7): 28–32. doi: 10.3969/j.issn.1006-1010.20240613-0001.
[9]	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.
[10]	SALEM H, QUAMAR M D M, MANSOOR A, et al. Data-Driven integrated sensing and communication: Recent advances, challenges, and future prospects[J]. arXiv: 2308.09090, 2023.
[11]	陈真, 杜晓宇, 唐杰, 等. 基于深度强化学习的RIS辅助通感融合网络: 挑战与机遇[J]. 电子信息学报, 2024, 46(9): 3467–3473. doi: 10.11999/JEIT240086. CHEN Zhen, DU Xiaoyu, TANG Jie, et al. DRL-based RIS-assisted ISAC Network: Challenges and opportunities[J]. Journal of Electronics & Information Technology, 2024, 46(9): 3467–3473. doi: 10.11999/JEIT240086.
[12]	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.
[13]	LIANG Yipeng, XHEN Qimei, and HAO Jiang. Federated learning with integrated sensing, communication, and computation: Frameworks and performance analysis[J]. arXiv: 2409.11240, 2024.
[14]	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.
[15]	JIAO Licheng, SHAO Yilin, SUN Long, et al. Advanced deep learning models for 6G: Overview, opportunities, and challenges[J] IEEE Access, 2024, 12: 133245–133314. doi: 10.1109/ACCESS.2024.3418900.
[16]	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.
[17]	王友祥, 裴郁杉, 黄蓉, 等. 6G通感算一体化网络架构和关键技术研究[J]. 移动通信, 2023, 47(9): 2–10. doi: 10.3969/j.issn.1006-1010.20230904-0002. WANG Youxiang, PEI Yushan, HUANG Rong, et al. Network architecture and key technologies for 6G integrated communication, sensing and computing[J]. Mobile Communications, 2023, 47(9): 2–10. doi: 10.3969/j.issn.1006-1010.20230904-0002.
[18]	ZHANG Jifa, GUO Shaoyong, GONG Shiqi, et al. Intelligent waveform design for integrated sensing and communication[J]. IEEE Wireless Communications, 2025, 32(1): 166–173. doi: 10.1109/MWC.003.2400044.
[19]	KHORAMNEJAD F and HOSSAIN E. Generative AI for the optimization of next-generation wireless networks: Basics, state-of-the-art, and open challenges[J]. IEEE Communications Surveys & Tutorials, 2025. doi: 10.1109/COMST.2025.3535554.
[20]	LIU Chang, YUAN Weijie, LI Shuangyang, et al. Learning-based predictive beamforming for integrated sensing and communication in vehicular networks[J]. IEEE Journal on Selected Areas in Communications, 2022, 40(8): 2317–2334. doi: 10.1109/JSAC.2022.3180803.
[21]	LIU Yiyang, ZHANG Siyao, LI Xinmin, et al. Deep reinforcement learning-based beamforming design in ISAC-assisted vehicular networks[C]. IEEE Wireless Communications and Networking Conference, Dubai, United Arab Emirates, 2024: 1–6. doi: 10.1109/WCNC57260.2024.10571018.
[22]	VALCARCE A, KELA P, MANDELLI S, et al. The role of AI in 6G MAC[C]. 2024 Joint European Conference on Networks and Communications & 6G Summit, Antwerp, Belgium, 2024: 723–728. doi: 10.1109/EuCNC/6GSummit60053.2024.10597082.
[23]	REN Ruixing. Integrated sensing, communication and computation: Research status and future prospects[J]. 2024.
[24]	马丁友, 刘祥, 黄天耀, 等. 雷达通信一体化: 共用波形设计和性能边界[J]. 雷达学报, 2022, 11(2): 198–212. doi: 10.12000/JR21146. MA Dingyou, LIU Xiang, HUANG Tianyao, et al. Joint radar and communications: Shared waveform designs and performance bounds[J]. Journal of Radars, 2022, 11(2): 198–212. doi: 10.12000/JR21146.
[25]	LIU Qian, ZHU Yuqian, LI Ming, et al. DRL-based secrecy rate optimization for RIS-assisted secure ISAC systems[J]. IEEE Transactions on Vehicular Technology, 2023, 72(12): 16871–16875. doi: 10.1109/TVT.2023.3297602.
[26]	ZHONG Kai, HU Jinfeng, PAN Cunhua, et al. Joint waveform and beamforming design for RIS-aided ISAC systems[J]. IEEE Signal Processing Letters, 2023, 30: 165–169. doi: 10.1109/LSP.2023.3242554.
[27]	徐明枫, 李阳, 韩凯峰, 等. 基于GAN的导频配置和信道估计联合优化算法[J]. 信息通信技术与政策, 2023, 49(9): 58–66. doi: 10.12267/j.issn.2096-5931.2023.09.009. XU Mingfeng, LI Yang, HAN Kaifeng, et al. GAN-based joint pilot configuration and channel estimation optimization method[J]. Information and Communications Technology and Policy, 2023, 49(9): 58–66. doi: 10.12267/j.issn.2096-5931.2023.09.009.
[28]	BALEVI E, DOSHI A, and ANDREWS J G. Massive MIMO channel estimation with an untrained deep neural network[J]. IEEE Transactions on Wireless Communications, 2020, 19(3): 2079–2090. doi: 10.1109/TWC.2019.2962474.
[29]	SAFARI M S, POURAHMADI V, and SODAGARI S. Deep UL2DL: Data-driven channel knowledge transfer from uplink to downlink[J]. IEEE Open Journal of Vehicular Technology, 2020, 1: 29–44. doi: 10.1109/OJVT.2019.2962631.
[30]	DU Ying, LI Yang, XU Mingfeng, et al. A joint channel estimation and compression method based on GAN in 6G communication systems[J]. Applied Sciences, 2023, 13(4): 2319. doi: 10.3390/app13042319.
[31]	NGUYEN C, HOANG T M, and CHEEMA A A. Channel estimation using CNN-LSTM in RIS-NOMA assisted 6G network[J]. IEEE Transactions on Machine Learning in Communications and Networking, 2023, 1: 43–60. doi: 10.1109/TMLCN.2023.3278232.
[32]	LIU Xiangnan, ZHANG Haijun, LONG Keping, et al. Distributed unsupervised learning for interference management in integrated sensing and communication systems[J]. IEEE Transactions on Wireless Communications, 2023, 22(12): 9301–9312. doi: 10.1109/TWC.2023.3269815.
[33]	GAO Kaixuan, WANG Huiqiang, LU Hongwu, et al. Toward 5G NR high-precision indoor positioning via channel frequency response: A new paradigm and dataset generation method[J]. IEEE Journal on Selected Areas in Communications, 2022, 40(7): 2233–2247. doi: 10.1109/JSAC.2022.3157397.
[34]	SAIDUTTA Y M, ABDI A, and FEKRI F. Joint source-channel coding over additive noise analog channels using mixture of variational autoencoders[J]. IEEE Journal on Selected Areas in Communications, 2021, 39(7): 2000–2013. doi: 10.1109/JSAC.2021.3078489.
[35]	LIU Boxun, LIU Yuanyu, GAO Shijian, et al. LLM4CP: Adapting large language models for channel prediction[J]. Communications and Information Networks, 2024, 9(2): 113–125. doi: 10.23919/JCIN.2024.10582829.