User movement state estimation method for LTE-A heterogeneous network

Abstract

The invention provides a user movement state estimation method for a LTE-A heterogeneous network. The user movement state estimation method comprises the steps (A) statistics is conducted on the weighting switchover frequency of a user to be tested within a time period of a preset first length according a current switchover weighting coefficient; (B) an estimated movement state of the user to be tested is estimated according to the weighting switchover frequency obtained through statistics; (C) current switchover triggering time of the user to be tested is set according to the estimated movement state of the user to be tested; (D) switchover failure events of the user to be tested within a time period of a preset second length are recorded, and the switchover failure rate of the user to be tested is calculated; (E), when the switchover failure rate of the user to be tested meets preset conditions, the step (G) is executed, and the step (F) is executed otherwise; (F) the switchover weighting coefficient of the user to be tested is adjusted according to the proportion of the recorded switchover failure events of the user to be tested to total switchover failure events, and the step (A) is executed; (G) the currently estimated movement state serves as the current movement state of the user to be tested. By the adoption of the user movement state estimation method, the movement state of the user can be better estimated, and switchover performance can be effectively improved.

Description

technical field [0001] The invention relates to the technical field of mobile communication, in particular to a method for estimating a user's mobile state used in an LTE-A heterogeneous network. Background technique [0002] In my country, 3GPP Long Term Evolution (LTE) licenses have been officially issued, and various operators have begun extensive commercial use. Judging from the development experience of 3G networks and the statistical results of countries that have deployed LTE networks around the world, mobile data services still reflect the characteristics of commercial activity centers, homes and office buildings and other crowd gathering places as the main business hotspots and network capacity and performance bottlenecks. . In view of this feature, the most important practical countermeasures at present are to provide wide coverage through large macro base stations, and introduce and deploy small and ultra-small base stations in hot spots to realize load distribut...

AI Technical Summary

Problems solved by technology

[0004] At present, there are some mobile state estimation algorithms for heterogeneous networks, such as only counting the number of handovers from macro base stations to macro base stations; counting all handover times and directly accumulating; or weighting and summing handover times of different handover types according to fixed weights, etc., but , these methods are not well adapted to different network topologies

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