Cellular network fault diagnosis method

Abstract

The invention discloses a cellular network fault diagnosis method. The method comprises the following steps: the step 1, determining a network fault data set; the step 2, obtaining a network fault data set after dimension reduction; the step 3, representing the network fault data set after dimension reduction in the step 2 in the form of a feature matrix, expressing label information of the network fault data set after dimension reduction in the step 2 in a label matrix form, and converting the introduced weight matrix into an adjacent matrix with matrix elements only being 0 and 1; and the step 4, carrying out fault diagnosis based on the graph convolutional neural network. According to the novel cellular network fault diagnosis method, intelligent fault diagnosis of a heterogeneous wireless network is deeply studied, similar characteristics among samples are analyzed in combination with a big data processing method, an existing network fault parameter data set is converted into graph structure data, and features are extracted from the graph structure data by using a graph convolutional neural network, so that the classification task of the sample nodes is completed, and the fault type of the cell is predicted.

Description

technical field [0001] The invention relates to the technical field of communication, in particular to a cellular network fault diagnosis method. Background technique [0002] In recent years, facing the surge of mobile data traffic and the different needs of various services, heterogeneous cellular networks have gradually become one of the important methods to improve system capacity. However, with the expansion of the scale and complexity of the cellular network, the tasks of operation and maintenance of the cellular network also become complicated and cumbersome. While the end-to-end user experience has improved significantly in terms of throughput and latency, cellular networks have also become more prone to failure. When faults occur or will occur, how to predict and locate them has become a great challenge. [0003] In the past, traditional network fault detection and diagnosis were mainly done manually. It was difficult to accurately obtain the mapping relationship ...

AI Technical Summary

Problems solved by technology

However, in actual situations, it is very difficult to obtain labeling information. Labeling samples is often very time-consuming, and the cost of manual category labeling is too high. Moreover, there are too few historical data sets, and the acquisition cost is also very expensive.

Most of these fault diagnosis methods are based on supervised learning fine-tuning and classification, a large number of unlabeled sample information cannot be fully utilized, and unlabeled sample data is wasted

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