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Next decade of telecommunications artificial intelligence
- Telecommunications Science Vol. 37, Issue 3, Pages: 1-36(2021)
- 作者机构：
1. 亚信科技（中国）有限公司，北京 100193
2. 美国威瑞森电信公司，美国 纽约 10036
3. 美国电话电报公司，美国 得克萨斯 达拉斯75202
4. 美国斯蒂文斯理工学院，美国 新泽西 霍博肯 07030
5. 中国移动通信集团公司，北京 100032
6. 中国电信集团公司，北京 100033
7. 清华大学，北京100084
- 作者简介：
- 基金信息：
DOI：10.11959/j.issn.1000-0801.2021055
CLC： TN929.5；TP18-
Published Online：2021-03，
Published：20 March 2021
- 稿件说明：
移动端阅览
Ye OUYANG, Lilei WANG, Aidong YANG, et al. Next decade of telecommunications artificial intelligence[J]. Telecommunications science, 2021, 37(3): 1-36.
Ye OUYANG, Lilei WANG, Aidong YANG, et al. Next decade of telecommunications artificial intelligence[J]. Telecommunications science, 2021, 37(3): 1-36. DOI： 10.11959/j.issn.1000-0801.2021055.
摘要
移动通信技术走过了37年的发展历程，人工智能技术也已走过了64年的发展历程。从早期的各自独立演进，到5G与人工智能开始深度融合发展，“5G与人工智能”已被业界视为一组最新的通用目的技术组合，对垂直行业的发展起到提振生产力与赋能的作用。首先介绍了早期移动通信和人工智能各自的发展路线，并重点回顾了人工智能与通信技术在3G到5G阶段开始融合发展。针对通信人工智能，详细阐述了当前人工智能技术在移动通信生态系统中各领域的发展情况，包括通信网络基础设施、网络管理与运营、电信业务管理、跨领域融合智能化、垂直行业与专网等，并总结了通信国际标准组织对人工智能技术在移动通信系统中的分级定义与演进路线。面向下一个十年，展望了通信人工智能未来的发展路线与演进趋势，并结合 3GPP与ITU-R的5G/6G时间表，前瞻性探索了基于3GPP和O-RAN路线的网络智能化、基于体验感知与意图的网络管理与运营系统的发展、网络AI信令体系、面向智慧中台演进的电信业务与支撑体系、跨领域融合的智能化体验管理与策略管理、从SLA向ELA的演进以及面向垂直行业的智能专网等。最后建议行业达成共识，在下一个十年中全面加速推进人工智能在通信生态领域的发展。
Abstract
It has been an exciting journey since the mobile communications and AI were conceived 37 years and 64 years ago.While both fields evolved independently and profoundly changed communications and computing industries
the rapid convergence of 5G and AI is beginning to significantly transform the core communication infrastructure
network management and vertical applications.The individual roadmaps of mobile communications and artificial intelligence in the early stage were firstly outlined
with a concentration to review the era from 3G to 5G when AI and mobile communications started to converge.With regard to telecommunications artificial intelligence
the progress of AI in the ecosystem of mobile communications was further introduced in detail
including network infrastructure
network operation and management
business operation and management
intelligent applications towards BSS ＆amp; OSS convergence
verticals and private networks etc.Then the classifications of AI in telecom ecosystems were summarized along with its evolution paths specified by various international telecommunications standardization bodies.Towards the next decade
the prospective roadmap of telecommunications artificial intelligence was forecasted.In line with 3GPP and ITU-R’s timeline of 5G ＆amp; 6G
the network intelligence following 3GPP and O-RAN routes
experience and intention driven network management and operation
network AI signaling system
intelligent middle-office based BSS
intelligent customer experience management and policy control driven by BSS＆amp; OSS convergence
evolution from SLA to ELA
and intelligent private network for verticals were further explored.It concludes that with the vision AI will reshape the future B5G/6G landscape
and we need fully take the unprecedented opportunities.
关键词
Keywords
references
LI R P , ZHAO Z F , ZHOU X , et al . Intelligent 5G:when cellular networks meet artificial intelligence [J ] . IEEE Wireless Communications , 2017 , 24 ( 5 ): 175 - 183 .
HINTON G E , OSINDERO S , TEH Y W . A fast-learning algorithm for deep belief nets [J ] . Neural Computation , 2006 , 18 ( 7 ): 1527 - 1554 .
LANDES D S . The unbound prometheus:technological change and industrial development in western Europe 1750 to the present [M ] . Cambridge : Cambridge University Press , 1976 .
ROSENBERG N . Inside the black box:technology and economics [M ] . Cambridge : Cambridge University Press , 1990 .
BRESNAHAN T F , TRAJTENBERG M . General purpose technologies ‘Engines of growth’? [J ] . Journal of Econometrics , 1995 , 65 ( 1 ): 83 - 108 .
LIPSEY R G , CARLAW K I , BEKAR C T . Economic transformations:general purpose technologies and long term economic growth [M ] . New York : Oxford University Press , 2005 .
The Bureau of Communication and Arts Research,Australian Government . Impacts of 5G on productivity and economic growth [Z ] . 2018 .
Ministry of Internal Affairs and Communications,Japan Government . Information and communications in Japan [R ] . 2019 .
PRIEGER J E . An economic analysis of 5G wireless deployment:impact on the U.S.and local economies [R ] . 2020 .
TEECE1 D J , DASGUPTA K . 5G mobile broadband:the economic potential and policies that maximize it [Z ] . 2017 .
Qualcomm . 5G+AI:the ingredients fueling tomorrow’s technology innovations [Z ] . 2020 .
SHAFIN R , LIU L J , CHANDRASEKHAR V , et al . Artificial intelligence-enabled cellular networks:a critical path to beyond-5G and 6G [J ] . IEEE Wireless Communications , 2020 , 27 ( 2 ): 212 - 217 .
MCCARTHY J , MINSKY M , ROCHESTER N , et al . A proposal for the Dartmouth summer research project on artificial intelligence,August 31,1955 [J ] . AI Magazine , 2006 , 27 ( 4 ): 12 - 14 .
RUMELHART D E , HINTON G E , WILLIAMS R J . Learning representations by back-propagating errors [J ] . Nature , 1986 , 323 ( 6088 ).
YASNITSKY L N , . Whether be new “winter” of artificial intelligence [C ] // Proceedings of International Conference on Integrated Science . Cham:Springer , 2019 .
KRIZHEVSKY A , SUTSKEVER I , HINTON G E . ImageNet classification with deep convolutional neural networks [C ] // Proceedings of the 25th International Conference on Neural Information Processing Systems , 2006 , 1 ( 11 ): 1097 - 1105 .
GOODFELLOW I J , POUGET-ABADIE J , MIRZA M , et al . Generative adversarial nets [C ] // Proceedings of the 27th International Conference on Neural Information Processing Systems . New York:ACM Press , 2014 .
MNIH V , KAVUKCUOGLU K , SILVER D , et al . Human-level control through deep reinforcement learning [J ] . Nature , 2015 , 518 ( 7540 ): 529 - 533 .
DEVLIN J , CHANG M W , LEE K , et al . Bert:pre-training of deep bidirectional transformers for language understanding [J ] . arXiv:1810.04805 , 2018
BROWN T B , MANN B , RYDER N , et al . Language models are few-shot learners [J ] . arXiv:2005.141654 , 2020
KONEN J , MCMAHAN H B , YU F X , et al . Federated learning:Strategies for improving communication efficiency [J ] . arXiv:1610.05492 , 2016
MCMAHAN H B , MOORE E , RAMAGE D , et al . Communication efficiency learning of deep networks from decentralized data [Z ] . 2016 .
OKUMURA Y , OHMORI E , KAWANO T , et al . Field strength and its variability in the VHF and UHF land mobile radio service [J ] . Rev.Elec.Commun.Lab. , 1968 , 16 ( 9/10 ): 825 - 73 .
HATA M . Empirical formula for propagation loss in land mobile radio services [J ] . IEEE Transactions on Vehicular Technology , 1980 , 29 ( 3 ): 317 - 325 .
3GPP . RF system scenarios:TS 25.942 V1.0.0 [S ] . 1999 .
YOUNG D J , BEAULIEU N C . The generation of correlated Rayleigh random variates by inverse discrete Fourier transform [J ] . IEEE Transactions on Communications , 2000 , 48 ( 7 ): 1114 - 1127 .
WANG C X , PATZOLD M . Methods of generating multiple uncorrelated Rayleigh fading processes [C ] // Proceedings of 57th IEEE Vehicular Technology Conference . Piscataway:IEEE Press , 2003 ( 1 ): 510 - 514 .
ARAGON-ZAVALA A , BELLOUL B , NIKOLOPOULOS V , et al . Accuracy evaluation analysis for indoor measurement-based radio-wave-propagation predictions [J ] . IEEE Proceedings on Microwaves,Antennas and Propagation , 2006 , 153 ( 1 ): 67 - 74 .
3GPP . Telecommunication management; self-organizing networks (SON); Concepts and requirements:TS32.523 [S ] . 2012 .
3GPP . Telecommunication management,self-configuration of network elements,concepts and requirements:TS32.501 [S ] . 2018 .
JOHNSONJM , RAHMAT-SAMII V . Genetic algorithms in engineering [J ] . IEEE Antennas and Propagation Magazine , 1997 ( 39 ): 7 - 21 .
PRODUCTION O B , SABUNCUOGLU I . Parameter selection in genetic algorithms [J ] . Journal of Systemics Cybernetics ＆Informatics , 2004 , 2 ( 4 ): 78 - 83 .
PÉREZ-ROMERO J , SALLENT O , FERRÚS R , et al . Knowledge-based 5G radio access network planning and optimization [C ] // Proceedings of International Symposium on Wireless Communication Systems (ISWCS) .[S.l.:s.n. ] , 2016 .
3GPP . Study of enablers for Network Automation for 5G:TS 23.791 [S ] . 2010 .
ETSI . Improved operator experience through experiential networked intelligence (ENI) [R ] . 2017 .
Study on applying AI in telecommunication network:CCSA TC1-WG1#58 meeting [R ] . 2017 .
O-RAN use cases and deployment scenarios [R ] . 2020 .
3GPP . RAN-centric data collection and utilization:RAN3 SI,2017.08/10 [S ] . 2018 .
3GPP . Study on the self-organizing networks(SON)for 5G Networks:TR28.861 [S ] . 2019
3GPP . Study on management aspects of communication services:TR28.805 V1.1.0 [S ] . 2019 .
ITU-T . Architectural framework for machine learning in future networks including IMT-2020:SG13 Y.317 [S ] . 2019 .
GSMA . AI in network use cases in China [R ] . 2019 .
3GPP . Study on concept,requirements and solutions for levels of autonomous network(Release 17):TR28.810 V17.0.0 [S ] . 2020 .
S2-2004541 . Federated Learning among multiple NWDAF instances,3GPP TSG-WG SA2 meeting #139E e-meeting [R ] . 2020 .
POOLE D , MACKWORTH A K , GOEBEL R . Computational intelligence - a logical approach [M ] // Computational intelligence:a logical approach . New York : Oxford University Press , 2011 .
RUSSELL S , NORVIG P . Artificial intelligence:a modern approach,3rd edition [M ] . Upper Saddle River : Prentice Hall , 1995 .
MOSLEH S S , LIU L J , SAHIN C , et al . Brain-inspired wireless communications:where reservoir computing meets MIMO-OFDM [J ] . IEEE Transactions on Neural Networks and Learning Systems , 2018 , 29 ( 10 ): 4694 - 4708 .
SAMUEL N , DISKIN T , WIESEL A . Deep MIMO detection [C ] // Proceedings of 2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications(SPAWC) . Piscataway:IEEE Press , 2017 .
CARPI F , HAGER C , MARTAL M , et al . Reinforcement learning for channel coding:learned bit-flipping decoding [J ] . arXiv:1906.04448 , 2019
LYU W , ZHANG Z , JIAO C , et al . Performance evaluation of channel decoding with deep neural networks [C ] // Proceedings of 2018 IEEE International Conference on Communications . Piscataway:IEEE Press , 2018 : 1 - 6 .
YASHASHWI K , ANAND D , PILLAI S R B , et al . MIST:a novel training strategy for low-latency scalable neural net decoders [J ] . arXiv:1905.08990 , 2019
LIANG F , SHEN C , WU F . An iterative BP-CNN architecture for channel decoding [J ] . IEEE Journal of Selected Topics in Signal Processing , 2018 , 12 ( 1 ): 144 - 159 .
3GPP . Evolved universal terrestrial radio access network(E-UTRAN)； Self-configuring and self-optimizing network(SON)use cases and solutions:TS36.902 [S ] . 2011 .
3GPP . Automatic neighbor relation(ANR)management; Concepts and requirements:TS32.511 [S ] . 2020 .
3GPP . Policy network resource model(NRM)integration reference point(IRP); Information service(IS):TS32.522 [S ] . 2013
3GPP . Self-organizing networks(SON); Self-healing concepts and requirements:TS32.541 [S ] . 2020 .
3GPP . Energy saving management(ESM); Concepts and requirements:TS32.551 [S ] . 2020 .
3GPP . Remote control of electrical tilting(RET)antennas;Requirements:TR32.804 [S ] . 2005
3GPP . Study on self-organizing networks(SON)for enhanced coordinated multi-point(eCoMP):TR36.742 [S ] . 2017 .
3GPP . Study on minimization of drive-tests:TR36.805 [S ] . 2009 .
3GPP . Potential solutions for energy saving for E-UTRAN:TR36.927 [S ] . 2020 .
LIN Z Y , OUYANG Y , SU L , et al . A machine learning assisted method of coverage and capacity optimization(CCO)in 4G LTE self organizing networks(SON) [C ] // Proceedings of 2019 Wireless Telecommunications Symposium(WTS) .[S.l.:s.n. ] , 2019 .
OUYANG Y , LI Z Y , SU L , et al . Application behaviors driven self-organizing network(SON)for 4G LTE networks [J ] . IEEE Transactions on Network Science and Engineering , 2020 , 7 ( 1 ): 3 - 14 .
OUYANG Y , LI Z Y , SU L , et al . APP-SON:Application characteristics-driven SON to optimize 4G/5G network performance and quality of experience [C ] // Proceedings of 2017 IEEE International Conference on Big Data(Big Data) . Piscataway:IEEE Press , 2017 : 1514 - 1523 .
QIN M , LI J , YANG Q , et al . Learning-aided multiple time-scale SON function coordination in ultra-dense small-cell networks [J ] . IEEE Transactions on Wireless Communications , 2019 , 18 ( 4 ): 2080 - 2092 .
ENGELS A , REYER M , XU X , et al . Autonomous self-optimization of coverage and capacity in LTE cellular networks [J ] . IEEE Transactions on Vehicular Technology , 2013 , 62 ( 5 ): 1989 - 2004 .
BERGER S , FEHSKE A , ZANIER P , et al . Online antenna tilt-based capacity and coverage optimization [J ] . IEEE Wireless Communications Letters , 2014 , 3 ( 4 ): 437 - 440 .
ROUZBEHR , SIEGFRIEDK , HOLGER C . A fuzzy reinforcement learning approach for self-optimization of coverage in LTE networks [J ] . Bell Labs Technical Journal , 2010 , 15 ( 3 ): 153 - 175 .
KARVOUNAS D , VLACHEAS P , GEORGAKOPOULOS A , et al . An opportunistic approach for coverage and capacity optimization in self-organizing networks [C ] // Proceedings of Future Network and Mobile Summit (Future Network Summit) . Piscataway:IEEE Press , 2013 .
COMBES R , ALTMAN Z , ALTMAN E . Self-organization in wireless networks:a flow-level perspective [J ] . Proceedings IEEE Infocom , 2012 : 2946 - 2950 .
LIANG H , ZHOU Y , HU J , et al . Coverage optimization for femtocell clusters using modified particle swarm optimization [C ] // Proceedings of IEEE International Conference on Communications . Piscataway:IEEE Press , 2012 : 611 - 615 .
PARTOV B , LEITH D J , RAZAVI R . Utility fair optimization of antenna tilt angles in LTE networks [J ] . IEEE/ACM Transactions on Networking , 2015 , 23 ( 1 ): 175 - 185 .
LEE D , ZHOU S , ZHONG X F , et al . Spatial modeling of the traffic density in cellular networks [J ] . Wireless Communications IEEE , 2014 , 21 ( 1 ): 80 - 88 .
AMIRIJOO M , JORGUSESKI L , LITJENS R , et al . Effectiveness of cell outage compensation in LTE networks [C ] // Proceedings of 2011 IEEE Consumer Communications and Networking Conference(CCNC) . Piscataway:IEEE Press , 2011 : 642 - 647 .
FEHSKE A J , KLESSI G H , VOIGT J , et al . ConcurrentLoad aware adjustment of user association and antenna tilts in self-organizing radio networks [J ] . IEEE Transactions on Vehicular Technology , 2013 , 62 ( 5 ): 1974 - 1988 .
BERGER S , SIMSEK M , FEHSKE A , et al . Joint downlink and uplink tilt-based self-organization of coverage and capacity under sparse system knowledge [J ] . IEEE Transactions on Vehicular Technology , 2016 , 65 ( 4 ): 2259 - 2273 .
AWADA A , WEGMANN B , VIERING I , et al . A SON-based algorithm for the optimization of inter-RAT handover parameters [J ] . IEEE Transactions on Vehicular Technology , 2013 , 62 ( 5 ): 1906 - 1923 .
IACOBOAIEA O C , SAYRAC B , BEN JEMAA S , et al . SON coordination in heterogeneous networks:a reinforcement learning framework [J ] . IEEE Transactions on Wireless Communications , 2016 , 15 ( 9 ): 5835 - 5847 .
SANCHEZ-GONZALEZ J , PEREZ-ROMERO J , SALLENT J O . A rule-based solution search methodology for self-optimization in cellular networks [J ] . IEEE Communications Letters , 2014 , 18 ( 12 ): 2189 - 2192 .
ITU-T . G.703 :Physical/electrical characteristics of hierarchical digital interfaces [R ] . 2016 .
ITU-T . Network node interface for the synchronous digital hierarchy(SDH):Rec.G.707/Y.1322 [R ] . 2007 .
Cisco . Cisco ONS 15454 Multiservice Transport Platform [Z ] . 2021 .
ITU-T . G.694.2:WDM applications:CWDM wavelength grid [R ] . 2003 .
G.8080/Y.1304,Architecture for the automatically switched optical network(ASON) [Z ] . 2003 .
NTT , CMCC , ETRI . GPP [R ] . 2014 .
BHAUMIK P , ZHANG S Q , CHOWDHURY P , et al . Software-defined optical networks(SDONs):a survey [J ] . Photonic Network Communications , 2014 , 28 ( 1 ): 4 - 18 .
THOMAS R W , DASILVA L A , MACKENZIE A B . Cognitive networks [C ] // Proceedings of IEEE DySPAN . 2005 : 352 - 360 .
CABALLERO A , BORKOWSKI R , DEMIGUEL I , et al . Cognitive,heterogeneous and reconfigurable optical networks:the CHRON project [J ] . Journal of Lightwave Technology , 2014 , 32 ( 13 ): 2308 - 2323 .
CHRON . The CHRON Project [Z ] . 2014 .
MATA J , MIGUEL I , DURÁN R J , et al . Artificial intelligence (AI) methods in optical networks:A comprehensive survey [J ] . Optical Switching and Networking , 2018 , 28 ( 4 ): 43 - 57 .
Huawei Technologies Co.,Ltd . Intelligent IP solutions:SRv6/EVPN/BIERv6 [R ] . 2020 .
IETF . SRv6 network programming:draft-ietf-spring-srv6- networkprogramming-16 [R ] . 2020 .
5G mobile network architecture for diverse services,use cases,and applications in 5G and beyond,Deliverable D2.3,5G-MoNArch,Project No.761445 [Z ] . 2019 .
3GPP . Study on enhancement of management data analytics (MDA):TR 28.809 [S ] . 2020 .
ETSI . ENI mapping between ENI architecture and operational systems:GS ENI 011 [S ] . 2020 .
ETSI . ENI definition of data processing mechanisms:GR ENI 009 [S ] . 2020 .
ETSI . ENI intent aware network autonomicity:GS ENI 008 [S ] . 2020 .
ETSI . Ongoing PoCs [S ] . 2020 .
ETSI . ENI(19)000170:PoC#2 Final Report [R ] . 2019 .
ETSI . ENI(20)016_021,ENI ISG PoC Report:PoC#8 Intent-based user experience optimization [R ] . 2020 .
Wikipedia Contributors . Operations support system [S ] . 2021 .
LAV G , TARA S , MAEDE Z , et al . Fault and performance management in multi-cloud virtual network services using AI:A tutorial and a case study [J ] . Computer Networks , 2019 ( 10 ): 169 .
MCELLIGOTT T Future OSS:towards an open digital architecture [Z ] . 2019 .
AT＆T Inc . ECOMP(Enhanced control,orchestration,management ＆ policy)architecture,white paper [R ] . 2016 .
BARTOLI P D . Defining the ECOMP architecture [Z ] . 2016 .
TM Forum . GB921 business process framework(eTOM) suite [Z ] . 2020 .
LivePerson . Connecting with customers repor [R ] . 2013 .
MAGIDS S , ZORFAS A , LEEMON D . The new science of customer emotions [J ] . Harvard Business Review , 2015 ( 11 ).
AISWare CEM customer experience management [J ] . Asiainfo Technology News , 2020 ( 7 ).
REICHHELD F F . One Number You Need to Grow [J ] . Harvard Business Review , 2003 ( 11 ).
STAHLKOPF C . Where net promoter score goes wrong [J ] . Harvard Business Review , 2019 ( 10 ).
3GPP . Policy and charging control architecture:TS23.203 [S ] . 2019 .
ETSI . GR ENI 007 V1.1.1 [S ] . 2019 .
TM Forum.Autonomous networks:empowering digital transformation for the telecoms industry(Rel . 1) [R ] . 2019 .
DAVIES H . What is 6G:Nokia will lead EU research with project Hexa-X [Z ] . 2020 .
Next G Alliance . Building the foundation for North American leadership in 6G and beyorrd [S ] . 2021 .
Focus group on technologies for network 2030 [Z ] . 2020 .
ITU-T . A blueprint of technology,applications and market drivers towards the year 2030 and beyond:White paper,FG-NET-2030 [S ] . 2020 .
3GPP . Service and feature requirements applicable to mobile and fixed communications techmdogy:TSGSA [S ] . 2021 .
3GPP . New study on self-organizing networks(SON)for 5G:TR28.861 [S ] . 2018 .
3GPP . Study on RAN-centric data collection and study on RAN-centric data collection and utilization for LTE and NR,document RP-182105 [S ] . 2018 .
O-RAN . WG1.Use-cases-detailed-specification-v04.00 [S ] . 2020 .
3GPP . Study on enablers for network automation for the 5G System (5GS); Phase 2(Release 17),TR23.700-91 [S ] . 2020 .
VARELA M , ZWICKL P , REICHL P , et al . From service level agreements(SLA)to experience level agreements(ELA):the challenges of selling QoE to the user [C ] // Proceedings of 2015 IEEE International Conference on Communication Workshop(ICCW) . Piscataway:IEEE Press , 2015 : 1741 - 1746 .
3GPP . Telecommunication management;Study on scenarios for Intent driven management services for mobile networks:TR28.812 [S ] . 2020 .
ETSI . Network functions virtualisation(NFV); management and orchestration:GS NFV-MAN 001 [S ] . 2014 .
GUTIERREZ-ESTEVEZ D M , GRAMAGLIA M , DOMENICO A D , et al . Artificial intelligence for elastic management and orchestration of 5G networks [J ] . IEEE Wireless Communications , 2019 , 26 ( 5 ): 134 - 141 .
ITU-T . Recommendation E.800-definitions of terms related to quality of service [S ] . 2008 .
ISO . IEC 7498-1:open systems interconnection - basic reference model:the basic model [S ] . 1994 .
CROWCROFT J , ROSCOE T . QoS's downfall:at the bottom,or not at all [C ] // Proceedings of the ACM SIGCOMM 2003 Workshops . New York:ACM Press , 2003 : 109 - 114 .
VARELA M,SKORIN-KAPOV L , EBRAHIMI T . Quality of service vs.quality of experience [M ] . Quality of Experience-Advanced Concepts,Applications and Methods,1st ed. . Berlin : Springer , 2014 : 35 - 54 .
YANG Q , LIU Y , CHEN T , et al . Federated machine learning:concept and applications [J ] . ACM Transactions on Intelligent Systems and Technology , 2019 , 2 ( 12 ): 10 .
PAN S , YANG Q . A survey on transfer learning [J ] . IEEE Transactions on Knowledge and Data Engineering , 2019 ( 10 ): 1 - 5 .
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