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[ "伏玉笋(1972- ),男,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" ]
网络出版日期:2020-11,
纸质出版日期:2020-11-20
移动端阅览
伏玉笋. 移动通信网络评价准则与解决方案[J]. 电信科学, 2020,36(11):28-38.
Yusun FU. Evaluation criteria and solution of mobile communication network[J]. Telecommunications science, 2020, 36(11): 28-38.
伏玉笋. 移动通信网络评价准则与解决方案[J]. 电信科学, 2020,36(11):28-38. DOI: 10.11959/j.issn.1000-0801.2020275.
Yusun FU. Evaluation criteria and solution of mobile communication network[J]. Telecommunications science, 2020, 36(11): 28-38. DOI: 10.11959/j.issn.1000-0801.2020275.
面向未来巨大差异化的业务、多样的个性化体验要求,论述了关键性能指标(KPI)、关键质量指标(KQI)、业务质量(QoS)、业务体验(QoE)及关键经营指标(KBI)的内在关系以及从KPI、KQI和KBI出发各自可能的最优设计方法及潜在的挑战和技术研究方向,这是一个层层递进的统一整体。最后指出,目前的公平性指标是一种后验的“经验”指导,没有产生具体的反馈闭环机制以形成对设计准则的明确指导。而从KBI角度的牵引,可能是可选的终结性、归一化的评价准则。需要指出的是,要从深度和广度上做好这些工作,需要跨学科、跨领域的深入研究,需要跨网络不同节点间的密切协同、跨层设计。
Facing the huge difference of business and various personalized experience requirements in the future
KPI (key performance indicator)
KQI (key quality indicator)
QoS
QoE and KBI (key business indicator) was discussed
as well as the possible optimal design methods
potential challenges and technical research directions based on KPI
KQI and KBI
which was a progressive and unified whole.Finally
it was pointed out that the current fairness index was a kind of “experience” guidance
and there was no specific feedback closed-loop mechanism to form a clear guidance for the design criteria.The traction from the perspective of KBI may be an optional final and normalized evaluation criterion.It was pointed out that to do these works well in depth and breadth
it needed interdisciplinary in-depth research
close collaboration and cross layer design between different nodes of the network.
3GPP.Service requirements for the 5G system:TS 22.261 [S ] . 2018 .
3GPP.Telefónica 5G vision:RWS-150005 [S ] . 2015 .
肖子玉 , 韩研 , 马洪源 , 等 . 5G网络面向垂直行业业务模型 [J ] . 电信科学 , 2019 , 35 ( 6 ): 132 - 140 .
XIAO Z Y , HAN Y , MA H Y , et al . Business service model of 5G network for vertical industry [J ] . Telecommunications Science , 2019 , 35 ( 6 ): 132 - 140 .
ITU-T . QoS aspects for popular services in mobile networks [R ] . 2014 .
杨燕 . 浅谈移动通信网中的 QoE [J ] . 电信科学 , 2007 , 23 ( 8 ): 34 - 38 .
YANG Y . Simple analysis of QoE in mobile communication network [J ] . Telecommunications Science , 2007 , 23 ( 8 ): 34 - 38 .
3GPP.Key performance indicators (KPI) for evolved universal terrestrial radio access network:TS32.450 [S ] . 2018 .
3GPP.Study on key quality indicator (KQI) for service experience:TR32.862 [S ] . 2016 .
潘思宇 , 张云勇 , 张溶芳 , 等 . 5G时代,人工智能为运营商赋能 [J ] . 电信科学 , 2019 , 35 ( 4 ): 95 - 102 .
PAN S Y , ZHANG Y Y , ZHANG R F , et al . Empowering MNO with AI in 5G era [J ] . Telecommunications Science , 2019 , 35 ( 4 ): 95 - 102 .
陈森 , 陈超 , 张小勇 , 等 . 基于大数据分析的移动互联网用户感知评估系统 [J ] . 电信科学 , 2015 , 31 ( 4 ): 147 - 154 .
CHEN S , CHEN C , ZHANG X Y , et al . Evaluation system of mobile internet user experience based on big data analysis [J ] . Telecommunications Science , 2015 , 31 ( 4 ): 147 - 154 .
林浩凌 , 吴奕生 , 郑伟旭 , 等 . 基于移动互联网应用的感知评估体系研究 [J ] . 电信科学 , 2014 , 30 ( Z1 ): 1 - 5 .
LIN H L , WU Y S , ZHENG W X , et al . Research on perception evaluation system based on mobile internet application [J ] . Telecommunications Science , 2014 , 30 ( Z1 ): 1 - 5 .
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 .
3GPP.System architecture for the 5G system:TS23.501 [S ] . 2018 .
3GPP.Study on architecture for next generation system:TR23.799 [S ] . 2016 .
3GPP.Policy and charging control framework for the 5G system:TS23.503 [S ] . 2018 .
3GPP.NR and NG-RAN overall description:TS38.300 [S ] . 2018 .
EKSTROM H . QoS control in the 3GPP evolved packet system [J ] . IEEE Communications Magazine , 2009 , 47 ( 2 ): 76 - 83 .
AMEIGEIRAS P , WANG Y Y , NAVARRO-ORTIZ J , et al . Traffic models impact on OFDMA scheduling design [J ] . EURASIP Journal on Wireless Communications and Networking , 2012
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 .
SUN H R , CHEN X Y , SHI Q J , et al . Learning to optimize:training deep neural networks for wireless resource management [C ] // Proceedings of IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications . Piscataway:IEEE Press , 2017 : 1 - 6 .
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 .
HAN S F , CHIHL I , LI G , et al . Big data enabled mobile network design for 5G and beyond [J ] . IEEE Communications Magazine , 2017 , 55 ( 9 ): 150 - 157 .
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 .
KLAINE P V , IMRAN M A , ONIRETI O , et al . A survey of machine learning techniques applied to self organizing cellular networks [J ] . IEEE Communications Survey & Tutorials , 2017 , 19 ( 4 ): 2392 - 2431 .
HUAWEI . The future of wireless network–AI inside [R ] . 2018 .
3GPP.Study on server and network-assisted dynamic adaptive streaming over HTTP (DASH) (SAND) for 3GPP multimedia services:TR26.957 [S ] . 2017 .
3GPP.Study on context aware service delivery in RAN for LTE:TR36.933 [S ] . 2017 .
周一青 , 李国杰 . 未来移动通信系统中的通信与计算融合 [J ] . 电信科学 , 2018 , 34 ( 3 ): 1 - 7 .
ZHOU Y Q , LI G J . Convergence of communication and computing in future mobile communication systems [J ] . Telecommunication Science , 2018 , 34 ( 3 ): 1 - 7 .
ETSI . Mobile edge computing (MEC); service scenarios [R ] . 2015 .
马洪源 , 肖子玉 , 卜忠贵 , 等 . 5G边缘计算技术及应用展望 [J ] . 电信科学 , 2019 , 35 ( 6 ): 114 - 123 .
MA H Y , XIAO Z Y , BU Z G , et al . 5G edge computing technology and application prospects [J ] . Telecommunication Science , 2019 , 35 ( 6 ): 114 - 123 .
OYMAN O , SINGH S . Quality of experience for HTTP adaptive streaming services [J ] . IEEE Communications Magazine , 2012 , 50 ( 4 ): 20 - 27 .
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 .
SHI H Z , PRASADR V , ONUR E , et al . Fairness in wireless networks:issues,measures and challenges [J ] . IEEE Communications Surveys & Tutorials , 2014 , 16 ( 1 ): 5 - 22 .
BADIA L , LINDSTROM M , ZANDER J , et al . Demand and pricing effects on the radio resource allocation of multimedia communication systems [C ] // Proceedings of 2003 IEEE Global Telecommunications Conference . Piscataway:IEEE Press , 2004 : 4116 - 4121
BADIA L , SATURNI C , BRUNETTA L , et al . An optimization framework for radio resource management based on utility vs.price tradeoff in WCDMA systems [C ] // Proceedings of Third International Symposium on Modeling and Optimization in Mobile,Ad Hoc,and Wireless Networks . Piscataway:IEEE Press , 2005 : 404 - 412 .
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 .
LUONG N C , WANG , NIYATO D , et al . Resource management in cloud networking using economic analysis and pricing models:a survey [J ] . IEEE Communications Surveys & Tutorials , 2017 , 19 ( 2 ): 954 - 1001 .
ZHANG Y , LEE C , NIYATO D , et al . Auction approaches for resource allocation in wireless systems:a survey [J ] . IEEE Communications Surveys & Tutorials , 2013 , 15 ( 3 ): 1020 - 1041 .
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