Orbital angular momentum beams demultiplexing using hybrid Fourier phase shift neural network

The exponential growth in data traffic has driven significant research into maximizing the capacity of free-space optical (FSO) communication systems. Orbital angular momentum (OAM) multiplexing offers a promising approach by using spatially structured beams with helical wavefronts to achieve higher data transmission rates. However, conventional electronic convolutional neural network-based OAM demultiplexing schemes exhibit substantial computational and energy efficiency limitations. In this paper, we introduce a hybrid optical-electronic Fourier phase shift neural network that implements phase-only feature extraction of the input multiplexed OAM beams in the Fourier domain. The proposed hybrid neural network uses phase spatial frequency kernels with the spatial light modulator to perform additive phase modulation of the Fourier-transformed input beams. Experimental results show that the proposed phase shift neural network has 6.5 times faster training time and three orders of magnitude higher energy efficiency compared to the designed conventional all-electronic convolutional neural network with one single convolution layer. The proposed system represents an idea towards energy-efficient, high-throughput optical neural networks for OAM-based FSO communication systems.