Deep Learning-Based Multi-Fault Diagnosis for Self-Organizing Networks

Kuan Fu Chen, Chia Hung Lin, Ming-Chun Lee, Ta-Sung Lee

研究成果: Conference contribution同行評審

5 引文 斯高帕斯(Scopus)

摘要

Having self-organizing ability is regarded as one of the vital features for modern wireless communication networks. Such self-organizing networks (SONs) thus draw significant attention in past years. As fault diagnosis is one of the essential functionalities for SONs, in this paper, we investigate the multi-fault and fault severity level diagnosis. Specifically, we propose deep learning-based approaches that can determine the faults and their corresponding levels by utilizing the network key performance indicators (KPIs). Furthermore, to enhance recall, we propose a loss function design that can effectively trade false alarm rate against recall. We conduct simulations adopting a practical setup to evaluate the performance. Results show that our proposed approaches can accurately diagnose multiple faults and determine their severity levels.

原文English
主出版物標題ICC 2021 - IEEE International Conference on Communications, Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
頁數6
ISBN(電子)9781728171227
DOIs
出版狀態Published - 14 6月 2021
事件2021 IEEE International Conference on Communications, ICC 2021 - Virtual, Online, Canada
持續時間: 14 6月 202123 6月 2021

出版系列

名字IEEE International Conference on Communications
ISSN(列印)1550-3607

Conference

Conference2021 IEEE International Conference on Communications, ICC 2021
國家/地區Canada
城市Virtual, Online
期間14/06/2123/06/21

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