Extracting Cell Patterns From High-Dimensional Radio Network Performance Datasets Using Self-Organizing Maps and K-Means Clustering

Wang, Shaoxuan; Ferrús, R.

doi:10.1109/access.2021.3065820

Cited by 9 publications

(2 citation statements)

References 25 publications

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“…The use of a local threshold to identify abnormalities distinguishes this study from similar ones, because local approaches improve diagnosis accuracy and decrease the production of false alarms. The distribution of deviations in the network can be used with the adaptive threshold in this investigation [18].…”

Section: Fault Detectionmentioning

confidence: 99%

Automatic Fault Detection and Diagnosis in Cellular Networks and Beyond 5G: Intelligent Network Management

et al. 2022

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Handling faults in a running cellular network can impair the performance and dissatisfy the end users. It is important to design an automatic self-healing procedure to not only detect the active faults, but also to diagnosis them automatically. Although fault detection has been well studied in the literature, fewer studies have targeted the more complicated task of diagnosing. Our presented method aims to tackle fault detection and diagnosis using two sets of data collected by the network: performance support system data and drive test data. Although performance support system data is collected automatically by the network, drive test data are collected manually in three mode call scenarios: short, long and idle. The short call can identify faults in a call setup, the long call is designed to identify handover failures and call interruption, and, finally, the idle mode is designed to understand the characteristics of the standard signal in the network. We have applied unsupervised learning, along with various classified algorithms, on performance support system data. Congestion and failures in TCH assignments are a few examples of the detected and diagnosed faults with our method. In addition, we present a framework to identify the need for handovers. The Silhouette coefficient is used to evaluate the quality of the unsupervised learning approach. We achieved an accuracy of 96.86% with the dynamic neural network method.

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Section: Fault Detectionmentioning

confidence: 99%

Automatic Fault Detection and Diagnosis in Cellular Networks and Beyond 5G: Intelligent Network Management

et al. 2022

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“…Lu W, Yan X [10] combines DMF with self-organizing map (DMF-SOM) to extract discriminative features from the original data and visualize them to distinguish normal state and fault state. The most common is to combine SOM neural network with various algorithms [11][12][13][14][15] to solve problems in various fields, but most of them are only simple combinations.…”

Section: Introductionmentioning

confidence: 99%

Adaptive SOM neural network algorithm

Wang

Yan

2023

3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023)

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In order to improve the performance of traditional SOM model, an adaptive SOM neural network algorithm is proposed. By introducing the error square sum solution formula, the clustering error of the model sub-data set is calculated by using the method of cutting the data set, and the change trend of the clustering error is quickly obtained. Then the learning rate of the SOM network is adaptively adjusted according to this, and the convergence speed is accelerated on the basis of ensuring that the network is fully trained, thereby improving the performance of the SOM network.

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Improving the performance of self-organizing map using reweighted zero-attracting method

Hameed,

Jamil,

Alazzawi

et al. 2024

Alexandria Engineering Journal

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Extracting Cell Patterns From High-Dimensional Radio Network Performance Datasets Using Self-Organizing Maps and K-Means Clustering

Cited by 9 publications

References 25 publications

Automatic Fault Detection and Diagnosis in Cellular Networks and Beyond 5G: Intelligent Network Management

Automatic Fault Detection and Diagnosis in Cellular Networks and Beyond 5G: Intelligent Network Management

Adaptive SOM neural network algorithm

Improving the performance of self-organizing map using reweighted zero-attracting method

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