Load balancing based on clustering analysis and deep learning for multi-frequency and multi-mode network

Yaxing QIU; Xidong WANG; Sen BIAN; Lei YUE

doi:10.11959/j.issn.1000-0801.2020159

您当前的位置：

首页 >

文章列表页 >

Load balancing based on clustering analysis and deep learning for multi-frequency and multi-mode network

Operation Technology | 更新时间：2024-06-05

- Load balancing based on clustering analysis and deep learning for multi-frequency and multi-mode network
- Telecommunications Science Vol. 36, Issue 7, Pages: 156-162(2020)
- 作者机构：
  
  1. 中国移动通信有限公司研究院，北京 100053
  2. 中国移动通信集团广西有限公司，广西南宁 530022
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2020159
  CLC： TN929.53
- Published Online：2020-07，
  
  Published：20 July 2020
- 稿件说明：
移动端阅览
Yaxing QIU, Xidong WANG, Sen BIAN, et al. Load balancing based on clustering analysis and deep learning for multi-frequency and multi-mode network[J]. Telecommunications science, 2020, 36(7): 156-162.
DOI：

Yaxing QIU, Xidong WANG, Sen BIAN, et al. Load balancing based on clustering analysis and deep learning for multi-frequency and multi-mode network[J]. Telecommunications science, 2020, 36(7): 156-162. DOI： 10.11959/j.issn.1000-0801.2020159.

摘要

负载均衡问题是LTE多频多模网络要解决的重大问题。多频多模网络结构复杂，负载均衡涉及的参数达数百个，仅依靠人工经验很难进行精细化配置。为解决多频多模网络的负载均衡问题，解决现网运维的难点与痛点，提出一种基于机器学习的多频多模网络负载均衡方案。首先选取关键指标对网络场景进行划分，然后利用机器学习技术挖掘出不同场景下的最佳参数配置建议。经验证，机器学习技术可以大大提高参数配置的质量和效率，做到精细化参数配置。

Abstract

Load balancing is a huge challenge for LTE multi-frequency and multi-mode network.Hundreds of parameters are involved in load balancing for the complex network structure.Therefore

it is difficult to perform precise and meticulous configuration only relying on human experience.In order to cope with the challenge

a load balancing scheme based on clustering analysis and deep learning was proposed.Firstly

the key indicators were selected to identify the network scenes

and then big data and deep learning technologies were used to mine the relationship between data.Finally

the optimum system parameters for different network scenes were found.It has been proved that machine learning technology can greatly improve the accuracy and the efficiency of parameter configuration.

关键词

Keywords

references

3GPP.Evolved universal terrestrial access network (E-UTRAN);layer 2 measurements:TS36.314 [S ] . 2010 .

郭佳睿 , 魏进武 , 张云勇 . 大数据助力运营商提升规模化运营核心力策略 [J ] . 电信科学 , 2018 , 34 ( 1 ): 120 - 125 .

GUO J R , WEI J W , ZHANG Y Y . Strategies for enhancing core capability of large-scalely operation for national telecom operators assisted by big data [J ] . Telecommunications Science , 2018 , 34 ( 1 ): 120 - 125 .

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 ] . Telecommunications Science , 2018 , 34 ( 3 ): 1 - 7 .

王志宏 , 杨震 . 人工智能技术研究及未来智能化信息服务体系的思考 [J ] . 电信科学 , 2017 , 33 ( 5 ): 1 - 11 .

WANG Z H , YANG Z . Research on artificial intelligence technology and the future intelligent information service architecture [J ] . Telecommunications Science , 2017 , 33 ( 5 ): 1 - 11 .

许波 , 付雷 , 张强 . LTE多载波负荷均衡优化浅析 [J ] . 邮电设计技术 , 2018 ( 7 ): 31 - 35 .

XU B , FU L , ZHANG Q . Analysis of LTE multi-carrier load balancing optimization [J ] . Posts and Telecommunications Design Technology , 2018 ( 7 ): 31 - 35 .

TIAN S , LI X , JI H . Mobility prediction scheme for optimized load balance in heterogeneous networks [C ] // Proceedings of 2018 IEEE Globecom Workshops . Piscataway:IEEE Press , 2018 : 1 - 6 .

XU Y , XU W , WANG Z , et al . Deep reinforcement learning based mobility load balancing under multiple behavior policies [C ] // Proceedings of 2019 IEEE International Conference on Communications (ICC) . Piscataway:IEEE Press , 2019 : 1 - 6 .

Views

516

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Research on telecom industry customer churn prediction based on explainable machine learning models

A single flow detection enabled method for DDoS attacks in IoT based on traffic feature reconstruction and mapping

Research and application of adaptive algorithm for 5G voice quality evaluation

A spatial diversity cooperative spectrum sensing system based on blockchain and deep learning

Gesture recognition based on flexible solar cells and ultrathin hydrogel film

Related Author

WANG Shengjie

ZHANG Qinghong

Lixia XIE

Bingdi YUAN

Hongyu YANG

Ze HU

Xiang CHENG

Liang ZHANG

Related Institution

College of Statistics and Data Science, Xinjiang University of Finance and Economics

School of Safety Science and Engineering, Civil Aviation University of China

School of Information, The University of Arizona

School of Information Engineering, Yangzhou University

School of Computer Science and Technology, Civil Aviation University of China

⁰