S1-MME data-based method of determining mobile user rapidly and accurately

Abstract

The invention discloses an S1-MME data-based method of determining mobile user rapidly and accurately, belonging to the technical field of data analysis. According to the method, a pre-determined useris screened out by collecting S1-MME interface data in an LTE mobile communication network, and a practical traffic route and a travel distance of the user are simulated and calculated by combining an algorithm calculating a projection of a base station cell set covering the traffic route on a geographic traffic route and a cell coverage track segmentation algorithm. The method replaces a mannerof calculating a user movement speed by using cell space in the prior art, so that the movement distance and speed are determined more accurately; and the user is determined by using both a maximum span determining method and a reverse slide window determining method, so that the determining accuracy is improved, manual intervention factors are reduced, and false negative and false positive problems resulting from the fact that fewer cells cover the traffic route or traffic routes of different mobile attributes are adjacent are solved.

Description

technical field [0001] The invention relates to the technical field of wireless communication data analysis, in particular to a high-precision discrimination method for fast mobile users based on S1-MME data in an LTE network. Background technique [0002] With the development of mobile communication network technology, especially the popularization and application of LTE network technology, terminal users have higher and higher requirements for network quality and service experience. The focus of network optimization has also shifted from the perspective of traditional network equipment and parameter optimization, focusing on the optimization of user group experience; and the identification of user groups with different mobility characteristics has become the basis for user perception analysis and improvement. [0003] The traditional drive test optimization method not only has high cost and long optimization period, but also cannot effectively reflect the service quality a...

AI Technical Summary

Problems solved by technology

This type of method introduces big data analysis and processing and machine learning evaluation, which has a high demand for resources; and the positioning accuracy of current algorithms such as AGPS is insufficient, and in areas with high density of cells, or where there are many ping-pong access areas, large errors occur

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