Similar articles

Abstract: Intelligent radio access networks (RANs) have been seen as a promising paradigm aiming to better satisfy diverse application demands and support various service scenarios. In this paper, a comprehensive survey of recent advances in intelligent RANs is conducted. First, the efforts made by standard organizations and vendors are summarized, and several intelligent RAN architectures proposed by the academic community are presented, such as intent-driven RAN and network with enhanced data analytic. Then, several enabling techniques are introduced which include AI-driven network slicing, intent perception, intelligent operation and maintenance, AI-based cloud-edge collaborative networking, and intelligent multi-dimensional resource allocation. Furthermore, the recent progress achieved in developing experimental platforms is described. Finally, given the extensiveness of the research area, several promising future directions are outlined, in terms of standard open data sets, enabling AI with a computing power network, realization of edge intelligence, and software-defined intelligent satellite-terrestrial integrated network.

智能无线接入网：架构、关键技术和试验平台

[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. 4^th 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