AI-enabled industrial intelligent wireless networks based on converged 5G-A and Nearlink short-range communications: network architecture and key technologies

doi:10.12267/j.issn.2096-5931.2025.11.001

Abstract

Abstract:

To meet the development demands of digitalization, networking, and intelligentization for Industry 4.0, industrial networks are undergoing upgrades toward wireless and intelligence. Nearlink short-range wireless communication is a new technical standard led and promoted by China. It is expected to converge with 5G-Advanced (5G-A) to provide in-depth coverage and intelligent services for industrial networks. This paper proposes an AI-enabled end-edge-cloud collaborative industrial intelligent wireless network architecture based on the converged 5G-A and Nearlink short-range communication. Furthermore, considering the characteristics of industrial scenarios, the key technologies of the converged 5G-A and Nearlink network are analyzed, and some problem are pointed out for further research.

Key words: industrial intelligent wireless network, Nearlink, 5G-A, convergence of communication, sensing, intelligence and control

CLC Number:

TP391.9

LIU Ling, ZHOU Yiqing, SHI Ningzhe, YUAN Wenhao, SHI Jinglin. AI-enabled industrial intelligent wireless networks based on converged 5G-A and Nearlink short-range communications: network architecture and key technologies[J]. Information and Communications Technology and Policy, 2025, 51(11): 2-9.

Add to citation manager EndNote|Ris|BibTeX

URL:

http://ictp.caict.ac.cn/EN/10.12267/j.issn.2096-5931.2025.11.001

http://ictp.caict.ac.cn/EN/Y2025/V51/I11/2

Figures/Tables 1

References 42

[1]	HMS Networks. 2024全球工业网络市场份额预测报告[R], 2024.
[2]	工业互联网联盟. 工业无线电磁环境白皮书[R], 2018.
[3]	星闪联盟. 星闪无线短距通信技术(SparkLink 1.0)产业化推进白皮书[R], 2022.
[4]	ZHOU Y, LIU L, WANG L, et al. Service aware 6G: an intelligent and open network based on convergence of communication, computing and caching[J]. Digital Communications and Networks, 2020, 6(3): 253-260. doi: 10.1016/j.dcan.2020.05.003 URL
[5]	星闪联盟. 星闪在智能终端和智能家居领域产业应用研究报告[R], 2025.
[6]	TR 22.837. Feasibility study on integrated sensing and communication (Release 19)[R], 2024.
[7]	TR 23.700-80. Study on 5G system support for AI/ML-based services (Release 18)[R], 2022.
[8]	星闪联盟. 星闪无线通信系统AI融合标准项目建议书[R], 2025.
[9]	张建华, 王玉洁, 唐盼, 等. 工业互联网信道特性与建模研究综述[J]. 电波科学学报, 2023, 38(1): 3-14.
[10]	TS 23.288. Architecture enhancements for 5G System (5GS) to support network data analytics services[R], 2023.
[11]	施耐德电气, 中国信息通信研究院, 中国联通. 5G+PLC深度融合解决方案白皮书[R], 2023.
[12]	JIANG J, MA Z, WANG K, et al. Multi-crane smart sorting control system based on 5G and cloud PLC[C]// 2023 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB). Beijing: IEEE, 2023: 1-6.
[13]	邓伟, 于天意, 侯庆东. 基于5G连接的集中式PLC新型工业组网架构[J]. 中兴通讯技术, 2023, 29(4): 72-77.
[14]	冯毅雄, 杨晨, 胡炳涛, 等. 基于5G多接入边缘计算的云化PLC系统架构设计与应用[J]. 计算机辅助设计与图形学学报, 2024, 36(1): 1-11.
[15]	WANG Y, YANG C, LAN S, et al. End-edge-cloud collaborative computing for deep learning: a comprehensive survey[J]. IEEE Communications Surveys & Tutorials, 2024, 26(4): 2647-2683.
[16]	TR 38.801. Study on new radio access technology: radio access architecture and interfaces[R], 2017.
[17]	ZHOU Y, TIAN L, LIU L, et al. Fog computing enabled future mobile communication networks: a convergence of communication and computing[J]. IEEE Communications Magazine, 2019, 57(5): 20-27.
[18]	程景浩, 于咏钟, 丁亚杰, 等. 集团型工业企业IT、OT和CT融合组网技术应用研究[J]. 邮电设计技术, 2025(6): 12-17.
[19]	李浩, 邢志远, 李琳利, 等. 基于多智能体的工业数字孪生系统云边端架构与关键技术[J]. 计算机集成制造系统, 2024, 30(11): 3755-3770.
[20]	PARK J, SAMARAKOON S, SHIRI H, et al. Extreme ultra-reliable and low-latency communication[J]. Nature Electronics, 2022, 5(3): 133-141. doi: 10.1038/s41928-022-00728-8
[21]	POURKABIRIAN A, KORDAFSHARI M S, JINDAL A, et al. A vision of 6G URLLC: physical-layer technologies and enablers[J]. IEEE Communications Standards Magazine, 2024, 8(2): 20-27.
[22]	LONG Y, GAO Y, YANG T. Research on ultra-reliable and low-latency wireless communications in smart factory with finite block-length[C]// 2018 IEEE/CIC International Conference on Communications in China (ICCC Workshops). Beijing: IEEE, 2018: 158-162.
[23]	SHAO Y, OZFATURA E, PEROTTI A G, et al. AttentionCode: ultra-reliable feedback codes for short-packet communications[J]. IEEE Transactions on Communications, 2023, 71(8): 4437-4452. doi: 10.1109/TCOMM.2023.3280563 URL
[24]	YUAN W, LIU L, ZHOU Y, et al. CoMP-enabled uRLLC availability analysis with NR protocols[C]// 2025 IEEE International Conference on Communications (ICC). Montreal: IEEE, 2025: 1-6.
[25]	LIU Y, LEE J, HAN Y, et al. Lyapunov drift-based scheduling for short-packet transmission with finite blocklength coding[C]// IEEE Global Communications Conference (GLOBECOM). Rio de Janeiro: IEEE, 2022: 2068-2073.
[26]	卞留念, 周一青, 刘玲, 等. AI构建视距径地图的低时延数据路由方法[J]. 西安电子科技大学学报, 2025, 52(3): 188-201.
[27]	WANG Y, ZHENG H, CHEN C, et al. The effect of wall blockages on indoor small cell networks with LOS/NLOS user association strategies[C]// 2021 IEEE 93rd Vehicular Technology Conference (VTC-Spring). Helsinki: IEEE, 2021: 1-6.
[28]	PENG T, CAO J, LIU X, et al. A data-driven and load-aware interference management approach for ultra-sense networks[J]. IEEE Access, 2019, 7: 129514-129528. doi: 10.1109/Access.6287639 URL
[29]	KHOSHKBARI H, SHARIFI S, KADDOUM G. User association in a VHetNet with delayed CSI: a deep reinforcement learning approach[J]. IEEE Communications Letters, 2023, 27(8): 2257-2261. doi: 10.1109/LCOMM.2023.3291613 URL
[30]	XIANG H, YANG Y, HE G, et al. Multi-Agent deep reinforcement learning-based power control and resource allocation for D2D communications[J]. IEEE Wireless Communications Letters, 2022, 11(8): 1659-1663. doi: 10.1109/LWC.2022.3170998 URL
[31]	LIU F, ZHENG L, CUI Y, et al. Seventy years of radar and communications: the road from separation to integration[J]. IEEE Signal Processing Magazine, 2023, 40(5): 106-121. doi: 10.1109/MSP.2023.3272881 URL
[32]	LIU F, CUI Y, MASOUROUA 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 URL
[33]	TAN D K P, HE J, LI Y, et al. Integrated censing and communication in 6G:motivations, use cases, requirements, challenges and future directions[C]// 2021 1st IEEE International Online Symposium on Joint Communications & Sensing (JC&S). Dresden: IEEE, 2021: 1-6.
[34]	LEE W, PARK J. LLM-empowered resource allocation in wireless communications systems[J]. arXiv Preprint, arXiv:2408.02944, 2024.
[35]	MENG K, MASOUROS C, PETROPULU A P, et al. Cooperative ISAC networks: performance analysis, scaling laws, and optimization[J]. IEEE Transactions on Wireless Communications, 2025, 24(2): 877-892. doi: 10.1109/TWC.2024.3491356 URL
[36]	YAO M, TAO D, GAO R, et al. Anomaly detection for MEC enabled hierarchical industrial IoT with transformer enhanced variational auto encoder[J]. IEEE Transactions on Industrial Informatics, 2025, 21(1): 40-48. doi: 10.1109/TII.2024.3421600 URL
[37]	HU H, WU D, ZHOU F, et al. Intelligent resource allocation for edge-cloud collaborative networks: a hybrid DDPG-D3QN approach[J]. IEEE Transactions on Vehicular Technology, 2023, 72(8): 10696-10709. doi: 10.1109/TVT.2023.3253905 URL
[38]	CAI Q, ZHOU Y, LIU L, et al. Prioritized assignment with task dependency in collaborative mobile edge computing[J]. IEEE Transactions on Mobile Computing, 2024, 23(12): 13505-13521. doi: 10.1109/TMC.2024.3427380 URL
[39]	HU J, LUO W, TOLBA A, et al. Traffic-aware load balancing based on deep reinforcement learning in cloud-based industrial data centers[J]. IEEE Transactions on Industrial Informatics, 2025, early access.
[40]	CAI Q, ZHOU Y, LIU L, et al. Query-aware semantic encoder-based resource allocation in task-oriented communications[J]. IEEE Transactions on Mobile Computing, 2025, 24(7): 6413-6429. doi: 10.1109/TMC.2025.3541636 URL
[41]	DONG L, ZHOU Y, LIU L, et al. Age of information based client selection for wireless federated learning with diversified learning capabilities[J]. IEEE Transactions on Mobile Computing, 2024, 23(12): 14934-14945. doi: 10.1109/TMC.2024.3450549 URL
[42]	NIAZMAND V, YE Q. Joint task offloading, DNN pruning, and computing resource allocation for fault detection with dynamic constraints in industrial IoT[J]. IEEE Transactions on Cognitive Communications and Networking, 2025, 11(5): 3486-3501. doi: 10.1109/TCCN.2025.3529688 URL