Abstract
Machine learning methods were used to construct a demultiplexer for helical wave front separation into orthogonal modes. The accuracy of wave front demultiplexing into eight modes at a signal-to-noise ratio of –3 dB is about 95% in a broad range of signal carrier frequencies. For nonstationary parameters of signals, the proposed demultiplexer accuracy exceeds that of the classical correlation method.
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