Review of the Optimization Machine Learning Inverse of view –Mode Fiber
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Abstract
The significance of optical fiber research in the digital realm is increasing because of its use in components, sensors, and high-speed data communication. The study of few-mode fiber (FMF) is experiencing a resurgence because of its capability to transmit data at high rates. This dissertation offers novel designs of FMFs with updated material composition and geometry to construct linkages using weakly coupled spatial division multiplexing (SDM) and mode division multiplexing (MDM). This study examines the necessary conditions for 5G networks and explores how they can be managed using spatial multiplexing and mode multiplexing techniques with a few-mode optical fiber. This method showcases the use of machine learning to simulate the profile of a few-mode fiber with a triangular-ring-core structure.It employs weak coupling optimisation to provide accurate predictions of refractive index differences and improved separation of spatial modes. Notably, this is accomplished using a data set that is six times smaller than that used in previous methods.
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