5G ultra dense network switching method based on precise point positioning

Abstract

The invention discloses a 5G ultra dense network switching method based on precise point positioning. By means of the precise point positioning technology, the current user location in an ultra densenetwork is measured, the location of the next moment is predicted, decision switching is conducted according to the signal to interference ratio of the current moment and the predicted signal to interference ratio of the next moment, a target micro cell can prepare resources for a possibly-generated access request in advance, the performance of the ultra dense network is improved, the ping-pong effect in the network is reduced, the average delay of the network is shortened, the generality and the validity are considered simultaneously, and the system performance is improved.

Description

【Technical field】 [0001] The invention belongs to the technical field of cell management, and in particular relates to a 5G ultra-dense network switching method based on precise single point positioning. 【Background technique】 [0002] At present, research institutions and scholars around the world have basically reached a consensus on the performance requirements of 5G, hoping to have higher data traffic, a large number of terminal connections, and smaller delays. In order to realize the development requirements of the above-mentioned wireless communication network, ultra-dense network technology is a research hotspot at present. [0003] In the past wireless communication systems, the cell radius was generally reduced by cell splitting, but as far as the current situation is concerned, the cell radius has become very small, and it is difficult to continue to rely on cell splitting. Another effective solution is to increase the capacity of the system by increasing the numb...

AI Technical Summary

Problems solved by technology

[0010] 2. At present, there is no reliable and effective theoretical model for reasonable modeling of user behavior

The mainstream solutions use machine learning-related models to model this problem, but complex modeling and large-scale data model training are difficult to apply to the computing environment of mobile networks

[0011] For option 2, the main problem at present is that the accuracy of the base station and satellite positioning to determine the user's location is not enough, resulting in a large prediction error, which is especially obvious in the scenario where the radius of ultra-dense network cells is constantly shrinking

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