Unsupervised Anomaly Detection for Rural Fixed Wireless LTE Networks

被引：0

作者：

Colpitts, Alexander G. B. ^{[1
]}

Petersen, Brent R. ^{[1
]}

机构：

[1] Univ New Brunswick, Dept Elect & Comp Engn, Fredericton, NB E3B 5A3, Canada

来源：

IEEE CANADIAN JOURNAL OF ELECTRICAL AND COMPUTER ENGINEERING | 2023年 / 46卷 / 04期

关键词：

Intelligent networks; network fault diagnosis; rural areas; unsupervised learning;

D O I：

10.1109/ICJECE.2023.3275975

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

This article presents an anomaly detection (AD) algorithm, robust AD for rural fixed wireless LTE (RAINFOREST), to address the difficulty of fault detection in LTE networks, specifically those that are rural and fixed wireless. We propose a hybrid AD method that uses network key performance indicators (KPIs), historical KPI forecasts, density-based spatial clustering of applications with noise (DBSCAN), and statistical analysis to detect anomalies. RAINFOREST outperformed benchmark AD methods and was able to detect faults in a rural commercial fixed wireless network earlier than existing LTE threshold-based alarms.

引用

页码：256 / 261

页数：6

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