Method for realizing wireless network fault detection through neural network

A neural network and fault detection technology, applied in the field of mobile communication, can solve the problem that the fault detection method is no longer applicable, and achieve the effects of reducing communication overhead, good performance, and high detection accuracy.

Active Publication Date: 2018-08-03
SOUTHEAST UNIV
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Problems solved by technology

Due to the high-density distribution characteristics of small cells and the sparse nature of user statistics, fault detection methods in homogeneous networks are mostly no longer applicable

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  • Method for realizing wireless network fault detection through neural network
  • Method for realizing wireless network fault detection through neural network
  • Method for realizing wireless network fault detection through neural network

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Embodiment Construction

[0036] The present invention will be further illustrated below in conjunction with the accompanying drawings and specific embodiments, and it should be understood that these embodiments are only for illustrating the present invention and are not intended to limit the scope of the present invention.

[0037] Such as figure 1 As shown, the present invention provides a method of using an improved radial basis function neural network to realize wireless network fault detection, which specifically includes the following steps:

[0038] 1) First consider the triggering of the detection process. The parallel operation of each neuron in the neural network greatly improves the operation speed of the test process, so the tolerance for triggering errors is also improved. We trigger the power attenuation fault of the base station through the network A2 and A3 events What needs to be explained here is the network A2 and A3 events, that is, the serving cell is worse than the absolute thresh...

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Abstract

The invention discloses a method for realizing wireless network fault detection through an improved radial basis function neural network so as to realize better classification for nonlinear separableuser data. In the method provided by the invention, a decision tree based learner and a bagging method are used for performing feature selection from user data of space and time dimensions, an artificial bee colony algorithm combined with mutation operations is used for realizing global optimization of neural network parameters, thus, performance of a neural network classifier is improved, a monitoring function of neighboring base stations is introduced in a distributed cooperative detection method, detection accuracy rate is improved and data transmission consumption is reduced, and ideal performance is realized in a densely distributed sparse-user small base station fault detection problem.

Description

technical field [0001] The invention belongs to the technical field of mobile communication, and in particular relates to a method for realizing wireless network fault detection by using an improved radial basis function neural network. Background technique [0002] In order to meet higher user service quality requirements and achieve greater communication bandwidth, the decentralized self-organizing network information collection and management method will achieve better network performance in 5G. Cell fault detection is an important part of the self-healing function in self-organizing networks, and fault detection in heterogeneous networks has been extensively studied. Due to the high-density distribution characteristics of small cells and the sparse nature of user statistics, fault detection methods in homogeneous networks are mostly no longer applicable. Contents of the invention [0003] Purpose of the invention: In order to overcome the deficiencies in the prior art...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04W24/04G06N3/04
CPCG06N3/04H04W24/04
Inventor 刘楠王玉婷潘志文尤肖虎
Owner SOUTHEAST UNIV
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