Machine Learning Based Soft Failure Detection by Exploiting Optical Channel Forward Error Correction Data
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Date
2023-10
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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.
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Keywords
Quality of transmission, soft failure detection, support vector machine (SVM), artificial neural network (multilayer perception), and random forest (RF),