Designing of the Submarine Cable Backhaul Optical Transport Network and the Prediction of OSNR using Artificial Neural Network

Author(s)	Mohammad Rakibur Rahman, Partha Mandal, Abdullah Al Mahbub, Mazbah Kabir Mridul
Country	Bangladesh
Abstract	In the era of the fast exponential increase of internet traffic, thereby, widespread deployment of IP over Optical Transport Network (OTN) necessitates the designing of a robust and stable backhaul network, so that the Services/Clients don’t experience any blackouts and outages from the International Bandwidth. Because of being a terrestrial backhaul transport network, the performance parameter is mostly the Optical Signal to Noise Ratio(OSNR). In this paper, in the 1st phase, motivated by a live real network, it’s been fully designed the Submarine Cable Backhaul Transport Network from Cable Landing Station(CLS) to Destination with working, protection, and restoration path prioritizing the fidelity of the network. In the 2nd phase, collecting the real-time OSNR time series data from the live circuit, an Artificial Neural Network (ANN) Model has been proposed to predict the OSNR to monitor the performance quality. The Model ANN result shows the network capability for better OSNR assessment and forecasting.
Keywords	Backhaul Optical Transport Network, Optical Signal to Noise Ratio (OSNR) Prediction, GMPLS, Artificial Neural Network (ANN)
Field	Computer > Network / Security
Published In	Volume 4, Issue 5, September-October 2022
Published On	2022-10-01
DOI	https://doi.org/10.36948/ijfmr.2022.v04i05.838
Short DOI	https://doi.org/gq9xb9

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