1024-ary composite OAM shift keying for free-space optical communication system decoded by a two-step neural network

The demand for high-dimensional encoding techniques for communication systems is increasing. Vortex beams carry-ing orbital angular momentum (OAM) provide new degrees of freedom for optical communication. In this study, we propose an approach for increasing the channel capacity of free-space optical communication systems by integrat-ing superimposed orbital angular momentum (OAM) states and deep learning techniques. We generate composite vor-tex beams with topological charges ranging from -4 to 8 and radial coefficients ranging from 0 to 3. A phase difference among each OAM state is introduced to significantly increase the number of available superimposed states, achieving up to 1024-ary codes with distinct features. To accurately decode the high-dimensional codes, we propose a two-step convo-lutional neural network (CNN). The first step is to make a coarse classification of the codes, while the second step is to finely identify the code and achieve decoding. Our pro-posed method demonstrates 100% accuracy achieved for the coarse classification after 7 epochs, 100% accuracy achieved for the fine identification after 12 epochs, and 99.84% accu-racy achieved for testing, which is much faster and more accurate than one-step decoding. To demonstrate the fea-sibility of our method, we successfully transmitted a 24-bit true-color Peppers image once with a resolution of 64 x 64 in the laboratory, yielding a bit error rate of 0. (c) 2023 Optica Publishing Group

Automated summary

This study proposes an approach to increase the channel capacity of free-space optical communication systems by integrating superimposed OAM states and deep learning techniques. A two-step CNN method is proposed to accurately decode the high-dimensional codes. The feasibility of the method is demonstrated through successful transmission of a high-resolution image with low bit error rate.