Convolutional neural network - long short-term memory model combined with Dammann vortex grating for decoding orbital angular momentum shift keying

This Letter presents a new artificial intelligence (AI)-based technique for decoding 16/32-ary orbital angular momentum shift keying (OAM-SK). Departing from the conventional CNN-based method that involves directly receiving vortex light at the terminal and decoding it with a convolutional neural network, our approach employs a 5×5 Dammann vortex grating (DVG) to generate an OAM-SK light array (from -12th to +12th diffraction order) in the receiving plane. Using DVG, the OAM-SK beam's light pattern evolves systematically across diffraction orders, transforming the optical array into a sequential signal for recognition by a convolutional neural network-long short-term memory (CNN-LSTM) model. To our knowledge, the DVG-CNN-LSTM method presents the first successful integration of deep learning model with DVG to improve OAM-SK decoding performance. The simulation results indicate that our innovative method markedly enhances recognition accuracy with the compact architecture with low computational overhead, an improvement attributed to the advanced acquisition of OAM-SK light pattern.