Machine Learning Based Soft Failure Detection by Exploiting Optical Channel Forward Error Correction Data
No Thumbnail Available
Date
2023-10
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Addis Ababa University
Abstract
The performance of optical channel degrades because of soft failures, such as filter failures, laser drift, and system aging. If such degradation is handled correctly and promptly, soft failures will not affect services. A crucial element in the protection against failures in optical channels is soft failure detection. Traditional approaches, however, find it difficult to complete this task because of their limitations in adapting the dynamic behavior of soft failure, requirements for professional manual intervention, and vastly increased optical performance data. The aim of this thesis is to detect soft failures based on machine learning by exploiting optical channel forward error correction data. Soft failures detection is explored using three ML algorithms: support vector machines (SVM), artificial neural networks (multilayer perception), and random forests (RF).The input of ML algorithms is Pre forward error correction bit error rate (Pre-FE-BER) that captured from forward error correction data on real optical channels. We implemented feature labelling and extraction based on the behavior of a time series window. We use stratified shuffle split method in cross-validation approaches to optimize and validate algorithm performance in terms of confession matrix, accuracy, and building time. As a result, RF with significant features, which has a validation accuracy of 99.2% and a standard deviation of 0.49%, is the best method. Beside, a lower computational complexity of 12 features and a building time of 17.5ms were determined.
Description
Keywords
Quality of transmission, soft failure detection, support vector machine (SVM), artificial neural network (multilayer perception), and random forest (RF),