Abstract
Optical performance monitoring (OPM) aims to estimate the amount of distortion in optical networks. Its importance relies on building robust and efficient networks with self and dynamic diagnosis. In this work, channel impairment monitoring is investigated for optical wireless communication. The monitoring is achieved using a support vector machine (SVM) regressor. A cost-effective and straightforward acquisition system is used to build the training features, which are asynchronous amplitude histograms. Three different parameters related to three common channel impairments are monitored using these features:optical signal-to-noise ratio (OSNR), visibility range, and $\xi$ parameter related to pointing error. First, each parameter is monitored when there is only one isolated channel impairment. Then, each parameter is monitored when two and three jointly channel impairments occur. The results showed that using this low complex machine learning (ML) technique, the achieved prediction accuracy was very high ( $>$ 0.98) for most channel conditions except for the monitoring of the OSNR parameter, where the prediction accuracy dropped to 0.86 under harsh channel conditions. Moreover, the superiority of ML-based techniques is compared with non-ML-based techniques for the OSNR parameter monitoring. The results indicated that the ML-based technique achieved high prediction accuracy than the non-ML-based technique, especially for harsh channel conditions.
Highlights
The increasing demand for bandwidth due to the emergent services such as online gaming, Internet Protocol television (IPTV), etc., is putting pressure on the network operators and service providers to upgrade their networks to support such services
This study focused on using a convolutional neural network (CNN) algorithm with asynchronous delay-tap sampling (ADTS) features for developing a prediction model
We investigate the performance of the support vector machine (SVM) as a regressor to predict every isolated parameter, i.e., optical signal-to-noise ratio (OSNR) parameter under amplified spontaneous emission (ASE) noise impairment, ξ parameter under pointing error impairment, and V parameter under atmospheric loss due to fog
Summary
The increasing demand for bandwidth due to the emergent services such as online gaming, Internet Protocol television (IPTV), etc., is putting pressure on the network operators and service providers to upgrade their networks to support such services. Future optical networks are expected to be heterogeneous, supporting different modulation formats and different baud rates depending on the channel status and the end customer demand [1] To manage such heterogeneous networks, the acquisition of real time information about the quality of the physical link in addition to the transmission speed is important. The lack of enough studies for monitoring the different impairments in FSO channel motivated the authors to investigate using ML for predicting different types of impairments and identifying the transmission baud rate in FSO systems. This includes monitoring the optical signal-to-noise ratio (OSNR), visibility range, and ξ parameters.
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