Optical network physical layer parameter optimization for digital backpropagation using Gaussian processes

Abstract

We present a novel methodology for optimizing fiber optic network performance by determining the ideal values for attenuation, nonlinearity, and dispersion parameters in terms of achieved signal-to-noise ratio (SNR) gain from digital backpropagation (DBP). Our approach uses Gaussian process regression, a probabilistic machine learning technique, to create a computationally efficient model for mapping these parameters to the resulting SNR after applying DBP. We then use simplicial homology global optimization to find the parameter values that yield maximum SNR for the Gaussian process model within a set of a priori bounds. This approach optimizes the parameters in terms of the DBP gain at the receiver. We demonstrate the effectiveness of our method through simulation and experimental testing, achieving optimal estimates of the dispersion, nonlinearity, and attenuation parameters. Our approach also highlights the limitations of traditional one-at-a-time grid search methods and emphasizes the interpretability of the technique. This methodology has broad applications in engineering and can be used to optimize performance in various systems beyond optical networks.

Keywords

• digital backpropagation
• Gaussian processes
• machine learning
• optical networks