人工智能在移动通信中的应用：挑战与实践

doi:10.11959/j.issn.1000-436x.2020167

Abstract

Abstract:

The research and application progress in mobile communication,and points out its obstacle for improving the performance of mobile communication system were summarized.A new approach for reducing the gap between the practical and the ideal network performance was put forward creatively,which included the artificial intelligence reconstruction for performance outer loop part,the traditional adaptation or optimization for performance inner loop part,and the optimal cooperation between the two parts.The effectiveness of this approach was proved by the several successful applications.Finally,it pointed out that in order to meet the severe demands of mobile communication system for the “stable” “accurate” and “fast” artificial intelligence solution,and make the mobile network automatic,intelligent and wise,in addition to the excellent performance of the artificial intelligence reconstruction scheme itself,there must also be a feedback closed-loop system architecture based on the big data analysis and analog system,which is the key path to achieve this target.

Key words: mobile communication, artificial intelligence, machine learning, network performance

CLC Number:

TN929.5

Yusun FU,Genke YANG. Application of artificial intelligence in mobile communication:challenge and practice[J]. Journal on Communications, 2020, 41(9): 190-201.

Figures/Tables 16

References 63

[1]	3GPP.Service requirements for the 5G system:TS 22.261[S].(2018-03)[2020-06-14].
[2]	3GPP.Telefónica 5G vision:RWS-150005[S].(2015-09)[202006-14].
[3]	胡晓峰, 贺筱媛, 徐旭林 . 大数据时代对建模仿真的挑战与思考—中国科协第 81 期新观点新学说学术沙龙综述[J]. 中国科学:信息科学, 2014,44(5): 676-692.
	HU X F , HE X Y , XU X L . Simulation in the big data era \| review of new ideas and new theories in the 81st academic salon of China association for science and technology[J]. SCIENTIA SINICA Informationis, 2014,44(5): 676-692.
[4]	何清, 李宁, 罗文娟 ,等. 大数据下的机器学习算法综述[J]. 模式识别与人工智能, 2014,27(4): 327-336.
	HE Q , LI N , LUO W J ,et al. A survey of machine learning algorithms for big data[J]. Pattern Recognition and Artificial Intelligence, 2014,27(4): 327-336.
[5]	段艳杰, 吕宜生, 张杰 ,等. 深度学习在控制领域的研究现状与展望[J]. 自动化学报, 2016,42(5): 643-653.
	DUAN Y J , LYU Y S , ZHANG J ,et al. Deep learning for control:the state of the art and prospects[J]. Acta Automatica Sinica, 2016,42(5): 643-653.
[6]	张长水 . 机器学习面临的挑战[J]. 中国科学:信息科学, 2013,43(12): 1612-1623.
	ZHANG C S . Challenges in machine learning[J]. SCIENTIA SINICA Informationis, 2013,43(12): 1612-1623.
[7]	梁吉业, 冯晨娇, 宋鹏 . 大数据相关分析综述[J]. 计算机学报, 2016,39(1): 1-18.
	LIANG J Y , FENG C J , SONG P . A survey on correlation analysis of big data[J]. Chinese Journal of Computers, 2016,39(1): 1-18.
[8]	李建东, 张琰, 盛敏 ,等. 信息通信网络的内涵及发展趋势[J]. 中国科学:信息科学, 2019,49(8): 949-962.
	LI J D , ZHANG Y , SHENG M ,et al. Concepts and trends of information communication networks[J]. SCIENTIA SINICA Informationis, 2019,49(8): 949-962.
[9]	赵亚军, 郁光辉, 徐汉青 . 6G移动通信网络:远景、挑战与关键技术[J]. 中国科学:信息科学, 2019,49(8): 963-987.
	ZHAO Y J , YU G H , XU H Q . 6G mobile communication networks:vision,challenges,and key technologies[J]. SCIENTIA SINICA Informationis, 2019,49(8): 963-987.
[10]	尤肖虎, 张川, 谈晓思 ,等. 基于AI的5G技术—研究方向与范例[J]. 中国科学:信息科学, 2018,48(12): 1589-1602.
	YOU X H , ZHANG C , TAN X S ,et al. AI for 5G:research directions and paradigms[J]. SCIENTIA SINICA Informationis, 2018,48(12): 1589-1602.
[11]	张平, 崔琪楣, 侯延昭 ,等. 移动大数据时代:无线网络的挑战与机遇[J]. 科学通报, 2015,60(5-6): 433-438.
	ZHANG P , CUI Q M , HOU Y Z.et al . Opportunities and challenges of wireless networks in the era of mobile big data[J]. Chinese Science Bulletin, 2015,60(5-6): 433-438.
[12]	尤肖虎 . 网络通信融合发展与技术革命[J]. 中国科学:信息科学, 2017,47(1): 144-148.
	XOU X H . Communication network convergence and technique revolution[J]. SCIENTIA SINICA Informationis, 2017,47(1): 144-1148.
[13]	HUAWEI. Machine learning for wireless networks:challenges and opportunities[R].(2018-06-08)[2020-06-14].
[14]	HUAWEI. The future of wireless network–AI inside[R].(2018-04-25)[2020-06-14].
[15]	CHIH-LIN I . From ‘green ＆ soft’ to ‘open ＆ smart’[R].(2018-07)[2020-06-14].
[16]	中兴通讯股份有限公司. 5G 网络智能化白皮书[R].(2018-08-01)[2020-06-14].
	ZTE Corporation. 5G network intelligence white paper[R].(201808-01)[2020-06-14].
[17]	中国人工智能产业发展联盟. 电信网络人工智能应用白皮书[R].(2018-09-30)[2020-06-14].
	Artificial Intelligence Industry Alliance. White paper on application of artificial intelligence in telecommunication network[R].(201808-01)[2020-06-14].
[18]	黄宇红 . 5G 智慧网络白皮书[R]. 北京:中国移动研究院,(2018-1128)[2020-06-14].
	HUANG Y H . 5G smart network white paper[R]. Beijing:China Mobile Research Institute,(2018-11-28)[2020-06-14].
[19]	CALABRESE F D , WANG L , GHADIMI E ,et al. Learning radio resource management in RANs:framework,opportunities and challenges[J]. IEEE Communications Magazine, 2018(9): 138-145.
[20]	KLAINE P V , IMRAN M A , ONIRETI O ,et al. A survey of machinelearning techniques applied to self organizing cellular networks[J]. IEEE Communications Survey ＆ Tutorials, 2017,19(4): 2392-2431.
[21]	IMRAN A , ZAHA A , ABU-DAYYA A . Challenges in 5G:how to empower SON with big data for enabling 5G[J]. IEEE Network, 2014,28(6): 27-33.
[22]	SAMEK W , STANCZAK S , WIEGAD T . The convergence of machine learning and communication[J]. arXiv Preprint,arXiv:1708.08299, 2017
[23]	ZHENG K , YANG Z , ZHANG K ,et al. Big data-driven optimization for mobile networks towards 5G[J]. IEEE Network, 2016,30(1): 44-51.
[24]	HAN S F , CHIH L I , LI G ,et al. Big data enabled mobile network design for 5G and beyond[J]. IEEE Communications Magazine, 2017,55(9): 150-157.
[25]	HE Y , YU F R , ZHAO N ,et al. Big data analytics in mobile cellular networks[J]. IEEE Access, 2016(4): 1985-1996.
[26]	ZAPPONE A , RENZO M D , DEBBAH M . Wireless networks design in the era of deep learning:model-based,AI-based,or both?[J]. IEEE Transactions on Communications, 2019,67(10): 7331-7376.
[27]	ALIU O G , IMRAN A , IMRAN M A ,et al. A survey of self organization in future cellular networks[J]. IEEE Communications Surveys ＆Tutorials, 2013,15(1): 336-361.
[28]	5G Americas.Management,orchestration and automation[R].(2019-11)[2020-06-14].
[29]	3GPP.Study on the Self-organizing networks (SON) for 5G networks:TR 28.861[S].(2020-04)[2020-06-14].
[30]	3GPP.Self-organizing networks (SON) for 5G networks:TS 28.313[S].(2020-05)[2020-06-14].
[31]	GOMEZ-ANDRADES A , BARCO R , SERRANO I ,et al. Automatic root cause analysis based on traces for LTE self organizing networks[J]. IEEE Wireless Communications, 2016,23(3): 20-28.
[32]	MWANJE S S , MITSCHELE-THIEL A , . A Q-learning strategy for LTE mobility load balancing[C]// IEEE 24th International Symposium on Personal,Indoor and Mobile Radio Communications. Piscataway:IEEE Press, 2013: 2154-2158.
[33]	ITU-T. Framework for evaluating intelligence levels of future networks including IMT-2020[R].(2020-02)[2020-06-14].
[34]	3GPP.Study on concept,requirements and solutions for levels of autonomous network:TR 28.810[S].(2020-04)[2020-06-14].
[35]	3GPP.System Architecture for the 5G system:TS 23.501[S].(2018-03)[2020-06-14].
[36]	3GPP.Architecture enhancements for 5G system(5GS) to support network data analytics services:TS 23.288[S].(2020-03)[2020-06-14].
[37]	3GPP.Policy and charging control framework for the 5G system:TS 23.503[S].(2018-03)[2020-06-14].
[38]	NASIR Y S , GUO D N . Multi-agent deep reinforcement learning for dynamic power allocation in wireless networks[J]. IEEE Journal on Selected Areas in Communications, 2019,37(10): 2239-2250.
[39]	BRUNO R , MASARACCHIA A , PASSARELLA A . Robust adaptive modulation and coding (AMC) selection in LTE systems using reinforcement learning[R].(2015-05-06)[2020-06-14].
[40]	CUI W , SHEN K M , YU W . Spatial deep learning for wireless scheduling[J]. IEEE Journal on Selected Areas in Communications, 2019,37(6): 1248-1261.
[41]	YANG H H , LIU Z Z , QUEK T Q S ,et al. Scheduling policies for federated learning in wireless networks[J]. IEEE Transactions on Communications, 2020,68(1): 317-333.
[42]	WEN C K , SHIH W T , JIN S . Deep learning for massive MIMO CSI feedback[J]. IEEE Wireless Communications Letters, 2018,7(5): 748-751.
[43]	WANG T Q , WEN C K , WANG H Q ,et al. Deep learning for wireless physical layer:opportunities and challenges[J]. China Communications, 2017,14(11): 92-111.
[44]	AIT AOUDIA F , HOYDIS J . Model-free training of end-to-end communication systems[J]. IEEE Journal on Selected Areas in Communications, 2019,37(11): 2503-2516.
[45]	GUNDUZ D , KERRET P D , NICHOLAS D ,et al. Machine learning in the air[J]. IEEE Journal on Selected Areas in Communications, 2019,37(10): 2184-2199.
[46]	QIN Z J , YE H , YE LI G Y ,et al. Deep learning in physical layer communications[J]. IEEE Wireless Communications, 2019,26(2): 93-99.
[47]	桂冠, 王禹, 黄浩 . 基于深度学习的物理层无线通信技术：机遇与挑战[J]. 通信学报, 2019,40(2): 19-23.
	GUI G , WANG Y , HUANG H . Deep learning based physical layer wireless communication techniques:opportunities and challenges[J]. Journal on Communications, 2019,40(2): 19-23.
[48]	张静, 金石, 温朝凯 ,等. 基于人工智能的无线传输技术最新研究进展[J]. 电信科学, 2018,34(8): 46-55.
	ZHANG J , JIN S , WEN C K ,et al. An overview of wireless transmission technology utilizing artificial intelligence[J]. Telecommunications Science, 2018,34(8): 46-55.
[49]	尹思源, 刘太君, 叶焱 ,等. 广义记忆型神经网络射频功放数字预失真器[J]. 微波学报, 2018,34(2): 47-50.
	YIN S Y , LIU T J , YE Y ,et al. Digital predistorters based on generalized memory neural networks RF power amplifiers[J]. Journal of Microwaves, 2018,34(2): 47-50.
[50]	林文韬, 刘太君, 叶焱 ,等. 基于改进型径向基函数神经网络的功放线性化[J]. 微波学报, 2015,31(5): 46-50.
	LIN W T , LIU T J , YE Y ,et al. Power amplifier linearization with modified radical basis function neural networks[J]. Journal of Microwaves, 2015,31(5): 46-50.
[51]	JIANG C X , ZHANG H J , REN Y ,et al. Machine learning paradigms for next-generation wireless networks[J]. IEEE Wireless Communications, 2017,24(2): 98-105.
[52]	SUN H R , CHEN X Y , SHI Q J ,et al. Learning to optimize:training deep neural networks for wireless resource management[C]// IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications. Piscataway:IEEE Press, 2017: 1-6.
[53]	CHEN M Z , CHALLITAX U , SAAD W ,et al. Artificial neural networks-based machine learning for wireless networks:a tutorial[J]. IEEE Communications Surveys ＆ Tutorials, 2019,21(4): 3039-3071.
[54]	SUN Y H , PENG M G , ZHOU Y C ,et al. Application of machine learning in wireless networks:key techniques and open issues[J]. IEEE Communications Surveys ＆ Tutorials, 2019,21(4): 3072-3108.
[55]	MAO Q , HU F , HAO Q . Deep learning for intelligent wireless networks:a comprehensive survey[J]. IEEE Communications Surveys ＆Tutorials, 2018,20(4): 2595-2621.
[56]	STRODTHOFF N , GOKTEPE B , SCHIERL T ,et al. Enhanced machine learning techniques for early HARQ feedback prediction in 5G[J]. IEEE Journal on Selected Areas in Communications, 2019,37(11): 2573-2587.
[57]	XU Z Y , TANG J , YIN C X ,et al. Experience-driven congestion control:when multi-path TCP meets deep reinforcement learning[J]. IEEE Journal on Selected Areas in Communications, 2019,37(6): 1325-1335.
[58]	HE H T , JIN S , WEN C K ,et al. Model-driven deep learning for physical layer communications[J]. IEEE Wireless Communications, 2019,26(5): 77-83.
[59]	LEE H , LEE S H , QUEK T Q S . Deep learning for distributed optimization:applications to wireless resource management[J]. IEEE Journal on Selected Areas in Communications, 2019,37(10): 2251-2266.
[60]	李建东, 刘磊, 盛敏 ,等. 面向5G 无线网络的智能干扰管理技术[J]. 电信科学, 2016,32(6): 3-14.
	LI J D , LIU L , SHENG M ,et al. Intelligent interference management in 5G wireless networks[J]. Telecommunications Science, 2016,32(6): 3-14.
[61]	王睿, 张克落 . 5G 网络切片综述[J]. 南京邮电大学学报, 2018,38(5): 19-27.
	WANG R , ZHANG K L . Survey of 5G network slicing[J]. Journal of Nanjing University of Posts and Telecommunications, 2018,38(5): 19-27.
[62]	3GPP.Study of enablers for network automation for 5G:TR 23.791[S].(2019-06)[2020-06-14].
[63]	刘秋妍, 钱颖, 伏玉笋 ,等. LTE 网络控制信道和业务信道联合调度策略[J]. 北京大学学报(自然科学版), 2015,51(3): 391-397.
	LIU Q Y , QIAN Y , FU Y S ,et al. Joint control channel and service channel allocation strategy in LTE network[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2015,51(3): 391-397.

Metrics

Recommended 0

No Suggested Reading articles found!

维度	性能内环	性能外环
信息特征	无记忆	有记忆
处理粒度	小尺度（TTI+ 级）	大尺度（话统+ 级）
数据特征	时间序列特征	统计特征
处理对象	一般用户级	一般小区级或者簇级
结果	感知用户，无法感知网络	感知网络，无法感知用户
示例：链路自适应	基于ACK/NACK反馈的链路自适应	链路自适应的初始值

Application of artificial intelligence in mobile communication:challenge and practice

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 16

References 63

Related Articles 15

Metrics

Recommended 0

[1]	Qianyi DAI, Bin ZHANG, Song GUO, Kaiyong XU. Blockchain network layer anomaly traffic detection method based on multiple classifier integration [J]. Journal on Communications, 2023, 44(3): 66-80.
[2]	Feibo JIANG, Yubo PENG, Li DONG. Deep image semantic communication model for 6G [J]. Journal on Communications, 2023, 44(3): 198-208.
[3]	Caixia LIU, Xinsheng JI, Jiangxing WU. Endogenous security common problems and solutions of the mobile communication networks [J]. Journal on Communications, 2022, 43(9): 70-79.
[4]	Jingyu WANG, Zirui ZHUANG. Research on a knowledge-defined polymorphic network attainable service architecture [J]. Journal on Communications, 2022, 43(4): 71-82.
[5]	Zhisheng NIU, Sheng ZHOU, Yuxuan SUN. Green communication and networking for Carbon-peaking and Carbon-neutrality: challenges and solutions [J]. Journal on Communications, 2022, 43(2): 1-14.
[6]	Chong LI, Xiujuan DU, Lijuan WANG, Xiaojing TIAN. Rule and perception based MAC protocol for UAN [J]. Journal on Communications, 2022, 43(2): 65-75.
[7]	Gaofeng HE, Qianfeng WEI, Xiancai XIAO, Haiting ZHU, Bingfeng XU. Confirmation method for the detection of malicious encrypted traffic with data privacy protection [J]. Journal on Communications, 2022, 43(2): 156-170.
[8]	Zhibin FENG, Yuhua XU, Zhiyong DU, Xin LIU, Wen LI, Hao HAN, Xiaobo ZHANG. Active defense technology against intelligent jammer [J]. Journal on Communications, 2022, 43(10): 42-54.
[9]	Yanhui LU, Han LIU, Hang LI, Guangxu ZHU. Time series generation model based on multi-discriminator generative adversarial network [J]. Journal on Communications, 2022, 43(10): 167-176.
[10]	Kai MEI, Haitao ZHAO, Xiaoran LIU, Jun LIU, Jun XIONG, Baoquan REN, Jibo WEI. Efficient model-and-data based channel estimation algorithm [J]. Journal on Communications, 2022, 43(1): 59-70.
[11]	Changgen PENG, Ting GAO, Huilan LIU, Hongfa DING. PCA-based membership inference attack for machine learning models [J]. Journal on Communications, 2022, 43(1): 149-160.
[12]	Futai ZOU, Yue TAN, Lin WANG, Yongkang JIANG. Botnet detection based on generative adversarial network [J]. Journal on Communications, 2021, 42(7): 95-106.
[13]	Siya XU, Yifei XING, Shaoyong GUO, Chao YANG, Xuesong QIU, Luoming MENG. Deep reinforcement learning based task allocation mechanism for intelligent inspection in energy Internet [J]. Journal on Communications, 2021, 42(5): 191-204.
[14]	Yijun WANG, Youxu ZHANG, Dakun LIU, Guifen CHEN. Relay selection algorithm based on selfish behavior analysis in ultra-dense D2D [J]. Journal on Communications, 2021, 42(4): 119-126.
[15]	Liu LIU, Jianhua ZHANG, Yuanyuan FAN, Li YU, Jiachi ZHANG. Survey of application of machine learning in wireless channel modeling [J]. Journal on Communications, 2021, 42(2): 134-153.