Small base station switch control method based on deep neural network

Abstract

The invention provides a small base station switch control method based on a deep neural network. The method comprises the following steps of acquiring user information in a base station; integratingall user data into a path data sample set capable of being trained by a model; establishing a neural network model; inputting data and training the model; collecting to-be-predicted user data, and predicting a position of a user at the next moment; and computing the number of future service users of the base station, and controlling switch of the base station. According to the method, by predicting the number of people to be serviced in the base station, the switch of the small base station in an ultra-dense network is controlled, so that the purposes of reducing the power consumption of the base station, reducing the interference between the base stations and optimizing resource distribution in the ultra-dense network are achieved; the method combines data mining and machine learning in aprocess of establishing a mathematical model, so that the prediction accuracy and the system practicability are improved.

Description

technical field [0001] The invention belongs to the technical field of wireless resource management in mobile communications, and relates to a base station switch control method, more specifically, to a small base station switch control method based on a deep neural network. Background technique [0002] An ultra-dense heterogeneous network in which low-power small cells are densely deployed on the same frequency within the coverage area of ​​macro cells is an effective method to improve the spectrum utilization and network capacity of wireless networks. However, on the one hand, the terminals to be served are unevenly distributed in space, some small base stations in a region operate at full capacity, and some small base stations are idle, resulting in a waste of processing resources. On the other hand, the terminals to be served are unevenly distributed in time, and there is a tidal effect in the distribution of users in the cell, which also causes waste of resources. Co...

AI Technical Summary

Problems solved by technology

However, on the one hand, the terminals to be served are unevenly distributed in space, some small base stations in a region are operating at full capacity, and some small base stations are idle, resulting in a waste of processing resources

On the other hand, the terminals to be served are unevenly distributed in time, and there is a tidal effect in the distribution of users in the cell, which will also cause a waste of resources

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