Aspects of the present disclosure describe systems, methods and structures for classification of higher-order spatial modes using machine learning systems and methods in which the classification of high-order spatial modes emitted from a multimode optical fiber does not require indirect measurement of the complex amplitude of a light beam's electric field using interferometry or, holographic techniques via unconventional optical devices/elements, which have prohibitive cost and efficacy; classification of high-order spatial modes emitted from a multimode optical fiber is not dependent on a light beam's alignment, size, wave front (e.g. curvature, etc.), polarization, or wavelength, which has prohibitive cost and efficacy; classification of higher-order spatial modes from a multimode optical fiber does not require a prohibitive amount of experimentally generated training examples, which, in turn, has prohibitive efficacy; and the light beam from a multimode optical fiber can be advantageously separated into two orthogonal polarization components, such that, the different linear combination of higher order spatial modes comprising each polarization component can be classified.
Machine learning based classification of higher-order spatial modes