Multiplexed orbital angular momentum beams demultiplexing using hybrid optical-electronic convolutional neural network
Jiachi Ye, Haoyan Kang, Qian Cai, Zibo Hu, Maria Solyanik‐Gorgone, Hao Wang, Elham Heidari, Chandraman Patil, Mohammad‐Ali Miri, Navid Asadizanjani, Volker J. Sorger, Hamed Dalir
Abstract
Abstract Advancements in optical communications have increasingly focused on leveraging spatial-structured beams such as orbital angular momentum (OAM) beams for high-capacity data transmission. Conventional electronic convolutional neural networks exhibit constraints in efficiently demultiplexing OAM signals. Here, we introduce a hybrid optical-electronic convolutional neural network that is capable of completing Fourier optics convolution and realizing intensity-recognition-based demultiplexing of multiplexed OAM beams under variable simulated atmospheric turbulent conditions. The core part of our demultiplexing system includes a 4F optics system employing a Fourier optics convolution layer. This optical spatial-filtering-based convolutional neural network is utilized to realize the training and demultiplexing of the 4-bit OAM-coded signals under simulated atmospheric turbulent conditions. The current system shows a demultiplexing accuracy of 72.84% under strong turbulence scenarios with 3.2 times faster training time than all electronic convolutional neural networks.