Machine Learning Techniques Applied to Improve High-Speed Spectrum Management in Fiber Optic Communications

High velocity spectrum management in fiber optic conversation systems is critical for device overall performance optimization. system getting to know methods are more and more hired in this regard, as they can make the most substantial information sets to better understand the underlying dependencies between spectral features and device overall performance. In this newsletter, we review cutting-edge gadget studying techniques and their packages in improving high-velocity spectrum control in optical networks. We gift a scientific overview of the literature on the way to perceive how machine getting to know approaches have been applied to diverse duties associated with spectrum management in optical networks including optical signal-to-noise ratio (OSNR) estimation, modulation layout identification, dispersion estimation, fiber configurations optimization, and course control. We additionally talk potential trends and open demanding situations for destiny research. It's far expected that the application of device learning strategies in fiber optic communique structures will open up a wholly new subject of research, and that it's going to offer similarly insights into spectrum management, leading to advanced overall performance and more suitable efficiency.