Abstract:
Analytical QoT models require safety margins to account for uncertain knowledge of input parameters. We propose and evaluate a design procedure that gradually decreases t...Show MoreMetadata
Abstract:
Analytical QoT models require safety margins to account for uncertain knowledge of input parameters. We propose and evaluate a design procedure that gradually decreases these margins in presence of multiple physical-layer uncertainties, by leveraging monitoring data to build a ML-based QoT regressor.
Published in: 2022 European Conference on Optical Communication (ECOC)
Date of Conference: 18-22 September 2022
Date Added to IEEE Xplore: 20 December 2022
ISBN Information:
Conference Location: Basel, Switzerland
Politecnico di Milano, Milan
Paris Research Center, Huawei Technologies France
Politecnico di Milano, Milan
Paris Research Center, Huawei Technologies France
Politecnico di Milano, Milan
Politecnico di Milano, Milan
Paris Research Center, Huawei Technologies France
Politecnico di Milano, Milan
Paris Research Center, Huawei Technologies France
Politecnico di Milano, Milan