一种基于机器学习的LTE高铁故障识别方法与实例

Machine learning-based method for malfunction identification of high-speed railway LTE network

为保证高铁LTE网络质量,提升客户感知,及时识别并解决高铁LTE网络故障至关重要。现有技术识别高铁故障主要依靠道路测试与网管后台数据,不仅耗时耗力,而且由于高铁专网多采用拉远与小区合并建设方式,故障具体点位和隐性故障难以定位。在此背景下,本文提出了一种基于大数据和机器学习,针对高铁网络特性的天馈故障识别方法,可有效提升高铁专网故障识别与定位的准确性,同时节省大量人力物力。

In order to ensure the quality of high-speed railway LTE network and improve customer perception,it is crucial to timely identify and solve the malfunction of high-speed rail LTE network.Existing technologies mainly rely on road test result and network management background data to identify highspeed railway LTE breakdown,which not only takes much time and energy,but also makes it diffi cult to locate specifi c points and hidden faults due to the fact that the high-speed rail LTE network is often constructed by combining remote and cell consolidation.In this context,we propose a machine learning based method for malfunction identification of high-speed railway LTE network,which can both effectively improve the accuracy of malfunction identification and positioning of high-speed railway appropriative network and save a lot of manpower and material resources.