Green Optical Networks Based on Machine Learning Techniques

Abstract

Presently the demand from users for communication networks with high bandwidth, high data rate, high security, and reliable connections has suddenly surged due to increased internet applications over the last five years. This is making communication networks complex. Further use of optical fiber cables as the backbone because of their high bandwidth and capacity, which can carry Tera bits of information, has further increased the complexity of accessing and analyzing the optical networks due to their non-linear behavior. In this regard, the availability of advanced machine learning algorithms based on mathematical tools, which are in a mature stage, has been looked upon. Machine learning is becoming an important tool because of the unexpected growth of network complexity. In the optical network, resource efficiency and capacity of the network is determined by routing and resource allocation. Since optical networks are the backbone, making them energy efficient will reduce carbon footprints and hence can reduce global warming. Hence, mathematical tools of machine learning algorithms are explored for optical communication systems and networks. Applications such as nonlinear equalization, optical performance monitoring, signal detection, mitigating various noise effects etc., and corresponding to their machine learning tools are discussed in detail. Researchers can explore further using machine learning tools in an optical domain and hence can propose and enhance flexibility and reliability.