中国移动通信集团设计院有限公司
Published:2018
移动端阅览
[1]王磊.基于机器学习技术的LTE网络智能优化系统设计[J].电信工程技术与标准化,2018,31(01):39-42.
王磊. Intelligent LTE configuration optimization system based on machine learning technology[J]. 2018, 31(1): 39-42.
[1]王磊.基于机器学习技术的LTE网络智能优化系统设计[J].电信工程技术与标准化,2018,31(01):39-42. DOI: 10.13992/j.cnki.tetas.2018.01.011.
王磊. Intelligent LTE configuration optimization system based on machine learning technology[J]. 2018, 31(1): 39-42. DOI: 10.13992/j.cnki.tetas.2018.01.011.
本文描述的工具
通过使用机器学习技术中成熟的个性化推荐算法
让计算机能够自动进行不同无线场景的关键特征提取和分析
从而对LTE小区结构进行特征画像
并在各种精细化的特征场景内进行网络质量多维度评价
实现自动化挖掘和学习各个场景下的局部参数最优配置
并进行平台固化和网优经验共享。同时
基于协同过滤算法
还可按照精细场景特征进行小区粒度参数经验值的自动化学习、推荐和自动设置。
In this paper
by means of the mature personalized recommendation algorithm in the machine learning technology
the computer can automatically extract and analyze the key features of different wireless scenarios.In a variety of scenarios
we can evaluate the quality of the network in order to realize the automatic mining and learning the local optimal parameters value.At the same time
based on the collaborative filtering algorithm
the automatic learning
recommendation and automatic setting of the cell configuration parameters can also be carried out in accordance with the fine scene features.
机器学习[M]. 清华大学出版社 , 周志华, 2016
推荐系统实践[M]. 人民邮电出版社 , 项亮, 2012
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