Intelligent radio access networks: architectures, key techniques, and experimental platforms

Zeyu WANG; Yaohua SUN; Shuo YUAN

doi:10.1631/FITEE.2100305

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Intelligent radio access networks: architectures, key techniques, and experimental platforms

Special Feature on Theory and Techniques for “Intellicise” Wireless Networks | Updated：2022-04-19

- Intelligent radio access networks: architectures, key techniques, and experimental platforms
  Enhanced Publication
- 智能无线接入网：架构、关键技术和试验平台
- Frontiers of Information Technology & Electronic Engineering Vol. 23, Issue 1, Pages: 5-18(2022)
- Affiliations：
  
  State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing100876, China
- Author bio：
  
  ‡Corresponding author
- Funds：
- DOI：10.1631/FITEE.2100305
  CLC： TN929.5
- Received：29 June 2021，
  
  Accepted：19 October 2021，
  
  Published：0 January 2022
- Accepted：
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Zeyu WANG, Yaohua SUN, Shuo YUAN. Intelligent radio access networks: architectures, key techniques, and experimental platforms[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(1): 5-18.
DOI：

Zeyu WANG, Yaohua SUN, Shuo YUAN. Intelligent radio access networks: architectures, key techniques, and experimental platforms[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(1): 5-18. DOI： 10.1631/FITEE.2100305.

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。

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.

关键词

Keywords

references

3GPP , 2019a . TR23.791 V16.2.0: Study of Enablers for Network Automation for 5G .

3GPP 2019b . TR28.805 V1.1.0: Study on Management Aspects of Communication Services .

3GPP , 2020 . TR28.809 V0.3.0: Study on Enhancement of Management Data Analytics (MDA) .

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 . doi: 10.1109/COMST.2018.2825786 http://doi.org/10.1109/COMST.2018.2825786

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 . doi: 10.1109/MCOM.001.1900653 http://doi.org/10.1109/MCOM.001.1900653

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 https://arxiv.org/abs/2012.01263 .

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 . doi: 10.1109/JIOT.2019.2950730 http://doi.org/10.1109/JIOT.2019.2950730

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 . doi: 10.1109/JIOT.2018.2876279 http://doi.org/10.1109/JIOT.2018.2876279

China Unicom , 2019 . Computing Power Network . White Paper .

Ding ZG , Poor HV , 2020 . A simple design of IRS-NOMA transmission . IEEE Commun Lett , 24 ( 5 ): 1119 - 1123 . doi: 10.1109/LCOMM.2020.2974196 http://doi.org/10.1109/LCOMM.2020.2974196

El Azzaoui A , Singh SK , Pan Y , et al. , 2020 . Block5GIntell: blockchain for AI-enabled 5G networks . IEEE Access , 8 : 145918 - 145935 . doi: 10.1109/ACCESS.2020.3014356 http://doi.org/10.1109/ACCESS.2020.3014356

ETSI , 2017 . Improved Operator Experience Through Experiential Networked Intelligence (ENI) .

He HT , Jin S , Wen CK , et al. , 2019 . Model-driven deep learning for physical layer communications . IEEE Wirel Commun , 26 ( 5 ): 77 - 83 . doi: 10.1109/MWC.2019.1800447 http://doi.org/10.1109/MWC.2019.1800447

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) . doi: 10.3969/j.issn.1006-1010.2020.06.020 http://doi.org/10.3969/j.issn.1006-1010.2020.06.020

Huawei , 2020 . Autonomous Driving Network (ADN) Solution . White Paper .

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 . doi: 10.1109/ACTEA.2019.8851107 http://doi.org/10.1109/ACTEA.2019.8851107

ITU-T , 2020 . Framework for Evaluating Intelligence Levels of Future Networks Including IMT .

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 . doi: 10.1109/JIOT.2020.2977402 http://doi.org/10.1109/JIOT.2020.2977402

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 . doi: 10.1109/JIOT.2020.3004500 http://doi.org/10.1109/JIOT.2020.3004500

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 http://doi.org/10.1109/TII.2019.2942179

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 http://doi.org/10.1109/COMST.2018.2846401

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 http://doi.org/10.1109/COMST.2020.3024783

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 http://doi.org/10.1109/ACCESS.2019.2902984

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 http://doi.org/10.1109/MNET.2016.7513863

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 http://doi.org/10.13190/j.jbupt.2020-079

RAN Alliance , 2018 . O-RAN: Towards an Open and Smart RAN . White Paper . https://www.coursehero.com/file/93485199/O-RANWPFInal181017pdf/ https://www.coursehero.com/file/93485199/O-RANWPFInal181017pdf/

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 http://doi.org/10.1109/TII.2020.3021024

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 http://doi.org/10.1109/JIOT.2019.2908019

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 http://doi.org/10.1109/COMST.2019.2924243

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 http://doi.org/10.1109/JIOT.2018.2871020

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 http://doi.org/10.1109/TVT.2019.2935098

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 http://doi.org/10.16157/j.issn.0258-7998.211392

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 http://doi.org/10.1109/JIOT.2018.2848295

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 http://doi.org/10.1109/JIOT.2020.2997187

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 http://doi.org/10.23919/JCC.2020.08.001

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 http://doi.org/10.11959/j.issn.1000-0801.2017200

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 http://doi.org/10.1109/TWC.2020.2965927

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 http://doi.org/10.1109/TVT.2019.2897134

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 http://doi.org/10.1109/JSYST.2015.2445919

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 http://doi.org/10.11871/jfdc.issn.2096-742X.2020.03.003

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 http://doi.org/10.11959/j.issn.1000-0801.2021123

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 http://doi.org/10.1109/MCOM.2017.1600940

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 http://doi.org/10.1631/FITEE.2210000

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 http://doi.org/10.1109/MWC.001.1900370

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 http://doi.org/10.11959/j.issn.2096-3750.2020.00146

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