User Experience Estimation in Multi-Service Scenario of Cellular Network

The estimation of user experience in a wireless network has always been a research hotspot, especially for the realization of network automation. In order to solve the problem of user experience estimation in wireless networks, we propose a two-step optimization method for the selection of the kernel function and bandwidth in a naive Bayesian classifier based on kernel density estimation. This optimization method can effectively improve the accuracy of estimation. At present, research on user experience estimation in wireless networks does not include an in-depth analysis of the reasons for the decline of user experience. We established a scheme integrating user experience prediction and network fault diagnosis. Key performance indicator (KPI) data collected from an actual network were divided into five categories, which were used to estimate user experience. The results of these five estimates were counted through the voting mechanism, and the final estimation results could be obtained. At the same time, this voting mechanism can also feed back to us which KPIs lead to the reduction of user experience. In addition, this paper also puts forward the evaluation standard of the multi-service perception capability of cell-level wireless networks. We summarize the user experience estimation for three main services in a cell to obtain a cell-level user experience evaluation. The results showed that the proposed method can accurately estimate user experience and diagnosis abnormal values in a timely manner. This method can improve the efficiency of network management.

Research on Fault Diagnosis Model of Generative Adss Based on Improved Semisupervised Diagnosis Algorithm

With the advent of the era of big data and the rapid development of deep learning and other technologies, people can use complex neural network models to mine and extract key information in massive data with the support of powerful computing power. However, it also increases the complexity of heterogeneous network and greatly increases the difficulty of network maintenance and management. In order to solve the problem of network fault diagnosis, this paper first proposes an improved semisupervised inverse network fault diagnosis algorithm; the proposed algorithm effectively guarantees the convergence of generated against network model, makes full use of a large amount of trouble-free tag data, and obtains a good accuracy of fault diagnosis. Then, the diagnosis model is further optimized and the fault classification task is completed by the convolutional neural network, the discriminant function of the network is simplified, and the generation pair network is only responsible for generating fault samples. The simulation results also show that the fault diagnosis algorithm based on network generation and convolutional neural network achieves good fault diagnosis accuracy and saves the overhead of manually labeling a large number of data samples.

Design Specification for Network Fault Diagnosis System

As the network becomes more and more complex and heterogeneous, the problem of network management becomes more and more prominent. The research and application of network management is of great significance for ensuring the normal operation of network and improving the reliability and availability of network system. The main problems of network management are network configuration, failure, performance, security, planning and scaling, etc. On the basis of in-depth research and discussion on network management and fault management, this paper designs the system requirements of network fault diagnosis system.

Fault location method of active distribution network based on graph theory and matrix algorithm

In view of the large scale distributed power distribution network, distribution network from the traditional single main transformer power supply system becomes more complex broken power supply network, the trend of the distribution network flow and network frame produced change, failure fault feature information of great change, the traditional distribution network fault location method can not accurately obtain the location of the fault zone, the low accuracy of fault location, the error bigger problem a new fault location method of active distribution network combining graph theory and matrix was proposed. According to the knowledge of graph theory, the distribution network is simplified to topology diagram and described in the form of matrix, and then the fault judgment matrix is obtained through a series of matrix addition operation, and the type of fault interval can be identified accurately and quickly by combining the fault criterion table. The simulation results show that this method is simple in principle, fast and effective in criterion, suitable for the flexible operation mode of active distribution network structure, can accurately and quickly determine the fault segment, and can meet the requirements of complex distribution network fault location.