Research On Analysis System Of Drive-Test Of High-speed Railway CDMA Network Based On Machine Learning

With the development of national economy, the development of high-speed rail achieves a high speed since1999. With the open of several high-speed railways, the mileage of high-speed railway in China has ranked first in the world and China has entered the era of high-speed railway. Because of the rapid popularization of high speed railway and the rapid increase of the number of passengers, the requirements for the quality of the communication of high-speed railway increases which attracts the attention of many communication organizations all around the world. The world’s major carriers has set off a race in the high-speed railway communication service market, in which China telecom got a dominant position. China telecom takes the lead to complete the network coverage of CDMA for high-speed railway. Since the complexity of the network structure of the network coverage of CDMA for high-speed railway, the network optimization of CDMA network of high-speed railway is of great significance with the help of the analysis of DT data.Machine learning is introduced in DT data analyzing to approach a local path loss model which is helped by the analyzing and modeling of the DT data. In addition, machine learning is used to analyze the DT data of CDMA network of high-speed railway and come up with a analysis method. First, this paper makes use of a modified K-means algorithm to cluster the DT data and determine the scope of fault. Then this paper makes use of SVM to complete the fault location which implements of the whole process of data analysis. This paper develops a system of DT data analysis of CDMA network of high-speed railway which achieves good analyze effect in DT data analysis which is of great significance for the network optimization of CDMA network of high-speed railway.