浏览全部资源
扫码关注微信
人工智能在移动通信中的应用：挑战与实践
Application of artificial intelligence in mobile communication:challenge and practice
- 通信学报 2020年41卷第9期 页码：190-201
- 作者机构：
1. 上海交通大学电子信息与电气工程学院，上海 200240
2. 上海交通大学宁波人工智能研究院，浙江 宁波 315000
- 作者简介：
[ "伏玉笋（1972- ），男，甘肃天水人，博士，上海交通大学助理研究员，主要研究方向为无线通信与系统、无线网联智能系统、工业互联网与安全、智能制造等" ]
[ "杨根科（1963- ），男，山西原平人，博士，上海交通大学教授，主要研究方向为离散事件系统和混杂系统的建模、优化与控制" ]
- 基金信息：国家重点研发计划基金资助项目(2019YFB1705703);国家重点研发计划基金资助项目(2017YFA60700602)
DOI：10.11959/j.issn.1000-436x.2020167
中图分类号： TN929.5-
网络出版日期：2020-09，
纸质出版日期：2020-09-25
- 稿件说明：
移动端阅览
伏玉笋, 杨根科. 人工智能在移动通信中的应用：挑战与实践[J]. 通信学报, 2020,41(9):190-201.
Yusun FU, Genke YANG. Application of artificial intelligence in mobile communication:challenge and practice[J]. Journal on communications, 2020, 41(9): 190-201.
伏玉笋, 杨根科. 人工智能在移动通信中的应用：挑战与实践[J]. 通信学报, 2020,41(9):190-201. DOI： 10.11959/j.issn.1000-436x.2020167.
Yusun FU, Genke YANG. Application of artificial intelligence in mobile communication:challenge and practice[J]. Journal on communications, 2020, 41(9): 190-201. DOI： 10.11959/j.issn.1000-436x.2020167.
摘要
对人工智能在移动通信领域学术界和产业界的研究与应用现状进行了总结，指出了人工智能在提升移动通信系统性能方面的挑战和瓶颈。创造性地提出性能外环与性能内环协同减小实际网络性能与理想网络性能间距离的新思路和新方法：对性能外环部分进行人工智能重构，对性能内环部分进行传统自适应或最优化，形成与性能外环部分的最佳协同。若干成功应用的实例证明了该思路和方法的有效性。最后指出，为了满足移动通信系统对人工智能解决方案“稳”“准”“快”的严苛需求，使能移动网络的自动化、智能化、智慧化，除了使人工智能重构的方案本身具有优异的性能外，还必须有基于大数据分析和模拟系统的反馈闭环系统架构，而架构中模拟器的构建——模拟系统是实现“稳”“准”“快”严苛需求的关键路径。
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.
关键词
Keywords
references
3GPP.Service requirements for the 5G system:TS 22.261 [S ] .(2018-03)[2020-06-14 ] .
3GPP.Telefónica 5G vision:RWS-150005 [S ] .(2015-09)[202006-14 ] .
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 .
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 .
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 .
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 .
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 .
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 .
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 .
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 .
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 .
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 .
HUAWEI . Machine learning for wireless networks:challenges and opportunities [R ] .(2018-06-08)[2020-06-14 ] .
HUAWEI . The future of wireless network–AI inside [R ] .(2018-0 4-25)[2020-06-14 ] .
CHIH-LIN I . From ‘green ＆ soft’ to ‘open ＆ smart’ [R ] .(2018-07)[2020-06-14 ] .
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 ] .
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 ] .
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 ] .
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 .
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 .
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 .
SAMEK W , STANCZAK S , WIEGAD T . The convergence of machine learning and communication [J ] . arXiv Preprint,arXiv:1708.08299 , 2017
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 .
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 .
HE Y , YU F R , ZHAO N , et al . Big data analytics in mobile cellular networks [J ] . IEEE Access , 2016 ( 4 ): 1985 - 1996 .
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 .
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 .
5G Americas.Management,orchestration and automation [R ] .(2019-11)[2020-06-14 ] .
3GPP.Study on the Self-organizing networks (SON) for 5G networks:TR 28.861 [S ] .(2020-04)[2020-06-14 ] .
3GPP.Self-organizing networks (SON) for 5G networks:TS 28.313 [S ] .(2020-05)[2020-06-14 ] .
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 .
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 .
ITU-T . Framework for evaluating intelligence levels of future networks including IMT-2020 [R ] .(2020-02)[2020-06-14 ] .
3GPP.Study on concept,requirements and solutions for levels of autonomous network:TR 28.810 [S ] .(2020-04)[2020-06-14 ] .
3GPP.System Architecture for the 5G system:TS 23.501 [S ] .(2018-03)[2020-06-14 ] .
3GPP.Architecture enhancements for 5G system(5GS) to support network data analytics services:TS 23.288 [S ] .(2020-03)[2020-06-14 ] .
3GPP.Policy and charging control framework for the 5G system:TS 23.503 [S ] .(2018-03)[2020-06-14 ] .
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 .
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 ] .
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 .
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 .
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 .
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 .
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 .
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 .
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 .
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 .
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 .
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 .
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 .
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 .
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 .
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 .
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 .
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 .
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 .
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 .
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 .
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 .
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 .
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 .
3GPP.Study of enablers for network automation for 5G:TR 23.791 [S ] .(2019-06)[2020-06-14 ] .
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 .
0
浏览量
2350
下载量
0
CSCD
- 网络PDF下载
- XML下载
- Meta-XML下载
- BibTeX下载
- Endnote下载
- RefMan 下载
- NoteExpress下载
- NoteFirst下载
关联资源
相关文章
相关作者
相关机构
AI问答
鸿云智能
中文
- 技术支持由北京北大方正电子有限公司提供 京ICP备09064830号-19
京公网安备11010802024621 - 本系统建议在Chrome、 IE9+ 以上版本浏览器阅读本站内容，360浏览器请切换至极速模式
- Cookies帮助我们提供服务并提供个性化体验。使用本网站，即表示您同意我们使用Cookies
- 总访问量：151733 今日访问量：2