Small base station switch control method based on deep neural network

A technology of deep neural network and switch control, applied in neural learning methods, biological neural network models, services based on location information, etc., can solve problems such as uneven distribution of terminals, waste of processing resources, tidal effects, etc., and achieve optimal resource allocation , improve accuracy and reduce interference

Active Publication Date: 2018-06-08
白盒子(上海)微电子科技有限公司
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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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  • Small base station switch control method based on deep neural network
  • Small base station switch control method based on deep neural network
  • Small base station switch control method based on deep neural network

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[0037] The technical solutions provided by the present invention will be described in detail below in conjunction with specific examples. It should be understood that the following specific embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention.

[0038] The present invention uses a deep neural network model to establish a prediction model for the location of the crowd, and predicts the number of people to be served in the small base station in the future. Specifically, the small base station switch control method based on the deep neural network provided by the present invention includes the following steps:

[0039] Step 1: Collect user information in the base station. Sampling once every minute, recording the user number, access time, and user location information of accessing the base station, and putting them into the sample set L={(u i ,t i ,p i )}, where u i is the access user number, t i It is...

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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...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04W4/02H04W4/029H04W52/02G06N3/08
CPCG06N3/084H04W4/02H04W52/0206Y02D30/70
Inventor 潘志文杜鹏程尤肖虎刘楠
Owner 白盒子(上海)微电子科技有限公司
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