Surrogate Model for Adaptive Control of Optical Amplifier Operating Point based on Machine Learning

The Adaptive Control of optical amplifier Operating Point (ACOP) is one of the problems presented in the challenge of Dynamic operation in optical communication and networks. The ACOP approaches aim to define the gains of the optical amplifiers dynamically to increase the transmission quality after a cascade of amplifiers. The most recent ACOP approach uses a multi-objective evolutionary optimization algorithm to define the gains of the amplifiers to maximize the Optical Signal to Noise Ratio (OSNR) and minimize OSNR ripple. Despite the promising results regarding Quality of Transmission, relying on an evolutionary algorithm to make decisions in real-time is not desirable because its iterative method usually implies high execution time. Therefore, this work proposes a surrogate model that can obtain solutions as good as the multi-objective evolutionary algorithm but in less time. We considered five Machine Learning (ML) regression techniques, trained with the optimization algorithm solutions. Results showed that for all cases, the regression median error is less than 1 dB and that one regressor can be used to define amplifiers’ gains and variable optical attenuators’ losses of an entire optical link. It also showed that the most straightforward regressor is 120 times faster than the evolutionary optimization approach.