Abstract
Optical networks generate a vast amount of diagnostic, control, and performance monitoring data. When information is extracted from these data, reconfigurable network elements and reconfigurable transceivers allow the network to adapt not only to changes in the physical infrastructure but also to changing traffic conditions. Machine learning is emerging as a disruptive technology for extracting useful information from these raw data to enable enhanced planning, monitoring, and dynamic control. We provide a survey of the recent literature and highlight numerous promising avenues for machine learning applied to optical networks, including explainable machine learning, digital twins, and approaches in which we embed our knowledge into machine learning such as physics-informed machine learning for the physical layer and graph-based machine learning for the networking layer.
Highlights
Machine learning (ML) is the study of computer algorithms that can learn to achieve a given task via experience and data without being explicitly programmed.[1]
We provide a survey of the recent literature and highlight numerous promising avenues for machine learning applied to optical networks, including explainable machine learning, digital twins, and approaches in which we embed our knowledge into machine learning such as physics-informed machine learning for the physical layer and graph-based machine learning for the networking layer
Variations of NN approaches appearing in the state-of-the-art of traffic prediction include Recurrent NNs (RNNs), such as Gated Recurrent Units (GRU) and LSTM owing to their capability of adaptively capturing dependencies on different time scales
Summary
Machine learning (ML) is the study of computer algorithms that can learn to achieve a given task via experience and data without being explicitly programmed.[1]. Certain ML applications have recently begun to increase in popularity for optical networks problems, which we address in this Tutorial In this Tutorial, we introduce the reader to ML, highlight the key ML techniques being deployed within optical fiber communication systems presently, and outline recent impactful works within each application sub-domain. These nonlinear noise-like distortions due to channel interference are power-dependent, meaning that there exists a trade-off between the optical power of the signal and the strength of these nonlinear interactions.[14] This introduces a level of complexity that makes physics-based modeling challenging in practical systems, making ML approaches look promising. ML approaches have shown potential in helping to deal with such effects, which may facilitate the use of wide-band systems in future networks Another critical problem in optical fiber communications is the high complexity of optical networks, which poses a significant operational challenge.[22].
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