Enhancing optical communication with deep neural networks

The spatial profile of optical modes may be altered such that they contain nonzero orbital angular momentum (OAM). Laguerre-Gauss (LG) states of light have a helical wavefront and well-defined OAM, and have recently been shown to allow for larger information transfer rates in optical communications as compared to using only Gaussian modes. A primary difficulty, however, is the accurate classification of different OAM optical states, which contain different values of OAM, in the detection stage. The difficulty in this differentiation increases as larger degrees of OAM are used. Here we show the performance of deep neural networks in the simultaneous classification of numerically generated, noisy, Laguerre-Gauss states with OAM value up to 100 can reach near 100{\%} accuracy. This method relies only on the intensity profile of the detected OAM states, avoiding bulky and difficult-to-implement methods that are required to measure the phase profile of the modes in the receiver of the communication platform. This allows for a simplification in the network design and an increase in performance when using states with large degrees of OAM. We anticipate that this approach will allow for significant advances in the development of optical communication technologies.