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Abstract
The demand for high-dimensional encoding techniques for communication systems is increasing. Vortex beams carrying 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 integrating superimposed orbital angular momentum (OAM) states and deep learning techniques. We generate composite vortex 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 convolutional 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 proposed 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% accuracy achieved for testing, which is much faster and more accurate than one-step decoding. To demonstrate the feasibility of our method, we successfully transmitted a 24-bit true-color Peppers image once with a resolution of 64 × 64 in the laboratory, yielding a bit error rate of 0.
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