
CLC number:
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2021-10-19
Cited: 0
Clicked: 6562
Citations: Bibtex RefMan EndNote GB/T7714
Zeyu WANG, Yaohua SUN, Shuo YUAN. Intelligent radio access networks: architectures, key techniques, and experimental platforms[J]. Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/FITEE.2100305 @article{title="Intelligent radio access networks: architectures, key techniques, and experimental platforms", %0 Journal Article TY - JOUR
智能无线接入网:架构、关键技术和试验平台北京邮电大学网络与交换技术国家重点实验室,中国北京市,100876 摘要:智能无线接入网(RANs)是一种很有前途的范例,能够更好地满足各种应用需求并支持各种服务场景。本文概述了智能RANs最新进展。首先,总结了标准组织和运营商的工作,介绍了学术界提出的几种智能RAN体系架构,例如意图驱动RAN和具有增强数据分析功能的网络。然后,总结了使能技术,包括人工智能驱动的网络切片、意图感知、智能运维、基于AI的云边协同组网和智能多维资源分配。此外,介绍了近期在开放试验平台方面取得的进展。最后,鉴于研究领域的广泛性,从标准开放数据集、AI使能的算力网络、边缘智能和软件定义的智能地面卫星网络等未来方向进行探讨。 关键词组: Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article
Reference[1]3GPP, 2019a. TR23.791 V16.2.0: Study of Enablers for Network Automation for 5G. ![]() [2]3GPP 2019b. TR28.805 V1.1.0: Study on Management Aspects of Communication Services. ![]() [3]3GPP, 2020. TR28.809 V0.3.0: Study on Enhancement of Management Data Analytics (MDA). ![]() [4]Asghar A, Farooq H, Imran A, 2018. Self-healing in emerging cellular networks: review, challenges, and research directions. IEEE Commun Surv Tutor, 20(3):1682-1709. ![]() [5]Bega D, Gramaglia M, Garcia-Saavedra A, et al., 2020. Network slicing meets artificial intelligence: an AI-based framework for slice management. IEEE Commun Mag, 58(6):32-38. ![]() [6]Bonati L, D'Oro S, Polese M, et al., 2020. Intelligence and learning in O-RAN for data-driven nextG cellular networks. https://arxiv.org/abs/2012.01263. ![]() [7]Cao Y, Wang R, Chen M, et al., 2020. AI agent in software-defined network: agent-based network service prediction and wireless resource scheduling optimization. IEEE Int Things J, 7(7):5816-5826. ![]() [8]Chen XF, Zhang HG, Wu C, et al., 2019. Optimized computation offloading performance in virtual edge computing systems via deep reinforcement learning. IEEE Int Things J, 6(3):4005-4018. ![]() [9]China Unicom, 2019. Computing Power Network. White Paper. ![]() [10]Ding ZG, Poor HV, 2020. A simple design of IRS-NOMA transmission. IEEE Commun Lett, 24(5):1119-1123. ![]() [11]El Azzaoui A, Singh SK, Pan Y, et al., 2020. Block5GIntell: blockchain for AI-enabled 5G networks. IEEE Access, 8:145918-145935. ![]() [12]ETSI, 2017. Improved Operator Experience Through Experiential Networked Intelligence (ENI). ![]() [13]He HT, Jin S, Wen CK, et al., 2019. Model-driven deep learning for physical layer communications. IEEE Wirel Commun, 26(5):77-83. ![]() [14]He T, Cao C, Tang XY, et al., 2020. Research on computing power network technology for 6G requirements. Mob Commun, 44(6):131-135 (in Chinese). ![]() [15]Huawei, 2020. Autonomous Driving Network (ADN) Solution. White Paper. ![]() [16]Issa A, Hakem N, Kandil N, 2019. Wireless SDN architecture testbed to support IP multimedia subsystem. 4th Int Conf on Advances in Computational Tools for Engineering Applications, p.1-6. ![]() [17]ITU-T, 2020. Framework for Evaluating Intelligence Levels of Future Networks Including IMT. ![]() [18]Liu J, Du XQ, Cui JH, et al., 2020. Task-oriented intelligent networking architecture for the space-air-ground-aqua integrated network. IEEE Int Things J, 7(6):5345-5358. ![]() [19]Liu YQ, Peng MG, Shou GC, et al., 2020. Toward edge intelligence: multiaccess edge computing for 5G and Internet of Things. IEEE Int Things J, 7(8):6722-6747. ![]() [20]Lu YL, Huang XH, Dai YY, et al., 2020. Differentially private asynchronous federated learning for mobile edge computing in urban informatics. IEEE Trans Ind Inform, 16(3):2134-2143. doi: 10.1109/TII.2019.2942179 ![]() [21]Mao Q, Hu F, Hao Q, 2018. Deep learning for intelligent wireless networks: a comprehensive survey. IEEE Commun Surv Tutor, 20(4):2595-2621. doi: 10.1109/COMST.2018.2846401 ![]() [22]Nguyen DC, Cheng P, Ding M, et al., 2021. Enabling AI in future wireless networks: a data life cycle perspective. IEEE Commun Surv Tutor, 23(1):553-595. doi: 10.1109/COMST.2020.3024783 ![]() [23]Pateromichelakis E, Moggio F, Mannweiler C, et al., 2019. End-to-end data analytics framework for 5G architecture. IEEE Access, 7:40295-40312. doi: 10.1109/ACCESS.2019.2902984 ![]() [24]Peng MG, Yan S, Zhang KC, et al., 2016. Fog-computing-based radio access networks: issues and challenges. IEEE Netw, 30(4):46-53. doi: 10.1109/MNET.2016.7513863 ![]() [25]Peng MG, Sun YH, Wang WB, 2020. Intelligent-concise radio access networks in 6G: architecture, techniques and insight. J Beijing Univ Posts Telecommun, 43(3):1-10 (in Chinese). doi: 10.13190/j.jbupt.2020-079 ![]() [26]RAN Alliance, 2018. O-RAN: Towards an Open and Smart RAN. White Paper. https://www.coursehero.com/file/93485199/O-RANWPFInal181017pdf/ ![]() [27]Ren YJ, Sun YH, Peng MG, 2021. Deep reinforcement learning based computation offloading in fog enabled industrial Internet of Things. IEEE Trans Ind Inform, 17(7):4978-4987. doi: 10.1109/TII.2020.3021024 ![]() [28]Srinivasan SM, Truong-Huu T, Gurusamy M, 2019. Machine learning-based link fault identification and localization in complex networks. IEEE Int Things J, 6(4):6556-6566. doi: 10.1109/JIOT.2019.2908019 ![]() [29]Sun YH, Peng MG, Zhou YC, et al., 2019a. Application of machine learning in wireless networks: key techniques and open issues. IEEE Commun Surv Tutor, 21(4):3072-3108. doi: 10.1109/COMST.2019.2924243 ![]() [30]Sun YH, Peng MG, Mao SW, 2019b. Deep reinforcement learning-based mode selection and resource management for green fog radio access networks. IEEE Int Things J, 6(2):1960-1971. doi: 10.1109/JIOT.2018.2871020 ![]() [31]Sun YH, Peng MG, Mao SW, 2019c. A game-theoretic approach to cache and radio resource management in fog radio access networks. IEEE Trans Veh Technol, 68(10):10145-10159. doi: 10.1109/TVT.2019.2935098 ![]() [32]Sun YH, Wang ZY, Yuan S, et al., 2021. The sixth-generation mobile communication network with endogenous intelligence: architectures, use cases and challenges. Appl Electron Tech, 47(3):8-13, 17 (in Chinese). doi: 10.16157/j.issn.0258-7998.211392 ![]() [33]Wang Z, Li LH, Xu Y, et al., 2018. Handover control in wireless systems via asynchronous multiuser deep reinforcement learning. IEEE Int Things J, 5(6):4296-4307. doi: 10.1109/JIOT.2018.2848295 ![]() [34]Wu WB, Peng MG, Chen WY, et al., 2020. Unsupervised deep transfer learning for fault diagnosis in fog radio access networks. IEEE Int Things J, 7(9):8956-8966. doi: 10.1109/JIOT.2020.2997187 ![]() [35]Xia WC, Zhang XR, Zheng G, et al., 2020. The interplay between artificial intelligence and fog radio access networks. China Commun, 17(8):1-13. doi: 10.23919/JCC.2020.08.001 ![]() [36]Xiang HY, Xiao YW, Zhang X, et al., 2017. Edge computing and network slicing technology in 5G. Telecommun Sci, 33(6):54-63 (in Chinese). doi: 10.11959/j.issn.1000-0801.2017200 ![]() [37]Xiang HY, Yan S, Peng MG, 2020. A realization of fog-RAN slicing via deep reinforcement learning. IEEE Trans Wirel Commun, 19(4):2515-2527. doi: 10.1109/TWC.2020.2965927 ![]() [38]Ye H, Li GY, Juang BF, 2019. Deep reinforcement learning based resource allocation for V2V communications. IEEE Trans Veh Technol, 68(4):3163-3173. doi: 10.1109/TVT.2019.2897134 ![]() [39]Yu C, Liu Y, Yao DZ, et al., 2017. Modeling user activity patterns for next-place prediction. IEEE Syst J, 11(2):1060-1071. doi: 10.1109/JSYST.2015.2445919 ![]() [40]Yu P, Li WJ, Feng L, et al., 2020. Intelligent network management and control architecture and key technologies for future 6G networks. Front Data Comput, 2(3):32-44 (in Chinese). doi: 10.11871/jfdc.issn.2096-742X.2020.03.003 ![]() [41]Yuan S, Ren YJ, Wang ZY, et al., 2021. Software defined intelligent satellite-terrestrial integrated wireless network. Telecommun Sci, 37(6):66-77 (in Chinese). doi: 10.11959/j.issn.1000-0801.2021123 ![]() [42]Zhang HJ, Liu N, Chu XL, et al., 2017. Network slicing based 5G and future mobile networks: mobility, resource management, and challenges. IEEE Commun Mag, 55(8):138-145. doi: 10.1109/MCOM.2017.1600940 ![]() [43]Zhang P, Peng MG, Cui SG, et al., 2022. Theory and techniques for "intellicise" wireless networks. Front Inform Technol Electron Eng, 23(1):1-4. doi: 10.1631/FITEE.2210000 ![]() [44]Zhao ZY, Feng CY, Yang HH, et al., 2020. Federated-learning-enabled intelligent fog radio access networks: fundamental theory, key techniques, and future trends. IEEE Wirel Commun, 27(2):22-28. doi: 10.1109/MWC.001.1900370 ![]() [45]Zhou YC, Yan S, Peng MG, 2020. Intent-driven 6G radio access network. Chin J Int Things, 4(1):72-79 (in Chinese). doi: 10.11959/j.issn.2096-3750.2020.00146 ![]() Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou
310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn Copyright © 2000 - 2026 Journal of Zhejiang University-SCIENCE | ||||||||||||||


ORCID:
Open peer comments: Debate/Discuss/Question/Opinion
<1>