中国移动通信集团设计院有限公司
Published:2018
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[1]刘玮,董江波,任冶冰,刘娜.大数据分析与网络弱覆盖优化方案[J].电信工程技术与标准化,2018,31(01):10-13.
刘玮, 董江波, 任冶冰, et al. Large data analysis and poor coverage discovery[J]. 2018, 31(1): 10-13.
[1]刘玮,董江波,任冶冰,刘娜.大数据分析与网络弱覆盖优化方案[J].电信工程技术与标准化,2018,31(01):10-13. DOI: 10.13992/j.cnki.tetas.2018.01.004.
刘玮, 董江波, 任冶冰, et al. Large data analysis and poor coverage discovery[J]. 2018, 31(1): 10-13. DOI: 10.13992/j.cnki.tetas.2018.01.004.
网络弱覆盖严重影响用户感知
是运营商最为关注并需首要解决的问题之一。网络弱覆盖的发现来源主要有路测、用户投诉等
近年来
随着大数据采集与分析能力的提升
也成为了弱覆盖发现的重要手段之一。本文以某密集城区为例
介绍从基于Hadoop的大数据解析、大数据定位、到网络弱覆盖发现与优化建议的整个流程
给实际网络规划优化工作提供经验。
Because poor coverage is a serious impact on user perception
it is one of the most important issues for operators and one of the most important issues to be solved. In the past
the main sources of poor coverage were road test
user complaint and so on. In recent years
with the enhancement of large data acquisition and analysis ability
MR data analysis has also become one of the most important means of poor coverage discovery. Taking a dense urban area as an example
this paper introduces the whole process of large data parsing
large data location
poor coverage discovery and optimization suggestions. The method of this paper provides experience for the actual network planning optimization.
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