Abstract
Since the great success of optical communications utilizing orbital angular momentum (OAM), increasing the number of addressable spatial modes in the given physical resources has always been an important yet challenging problem. The recent improvement in measurement resolution through deep-learning techniques has demonstrated the possibility of high-capacity free-space optical communications based on fractional OAM modes. However, due to a tiny gap between adjacent modes, such systems are highly susceptible to external perturbations such as atmospheric turbulence (AT). Here, we propose an AT adaptive neural network (ATANN) and study high-resolution recognition of fractional OAM modes in the presence of turbulence. We perform simulations of fractional OAM beams propagating through a 1-km optical turbulence channel and analyze the effects of turbulence strength, OAM mode interval, and signal noise on the recognition performance of the ATANN. The recognition of multiplexed fractional modes is also investigated to demonstrate the feasibility of high-dimensional data transmission in the proposed deep-learning-based system. Our results show that the proposed model can predict transmitted modes with high accuracy and high resolution despite the collapse of structured fields due to AT and provide stable performance over a wide SNR range.
Highlights
Since the great success of optical communications utilizing orbital angular momentum (OAM), increasing the number of addressable spatial modes in the given physical resources has always been an important yet challenging problem
We run the training with batch size 20 for 50 epochs, and the weights of the AT adaptive neural network (ATANN) are updated automatically with the Adam optimizer43, a stochastic optimization method
We proposed and demonstrated deep-learning-based adaptive demodulation of fractional OAM modes distorted by atmospheric turbulence (AT)
Summary
Since the great success of optical communications utilizing orbital angular momentum (OAM), increasing the number of addressable spatial modes in the given physical resources has always been an important yet challenging problem. The recent improvement in measurement resolution through deep-learning techniques has demonstrated the possibility of high-capacity free-space optical communications based on fractional OAM modes. We propose an AT adaptive neural network (ATANN) and study high-resolution recognition of fractional OAM modes in the presence of turbulence. The researchers have experimentally demonstrated that a deep-learning model based on a convolutional neural network can classify fractional OAM modes with a resolution of up to 0.0117 and process two independent spatial degrees of freedom simultaneously. Deep-learning-based detection of hybrid beams carrying fractional topological charge and the fractional angular ratio was investigated, which showed accurate recognition of fractional OAM with broad bandwidth in atmospheric environments. For the practical application of such OAM modes, it is necessary to develop an optical system capable of performing high-resolution recognition regardless of distorting factors
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