The vortex beam carrying orbital angular momentum (OAM) has attracted great attentions in optical communication field, which can extend the channel capacity of communication system due to the orthogonality between different OAM modes. These results indicate that the CNN model can well compensate the atmospheric turbulence induced distortion in VBs, and may open new avenues for improving the performance of OAM communications. Moreover, the constellations converge obviously at the signal-to-noise ratio of 20dB, and the error-vector-magnitude decreases from 0.3337 to 0.1622.
By constructing an OAM multiplexing communication link with the bit-rate of 100Īnd employing the CNN model to equalize the OAM channels, the bit-error-rates are decreased by three orders of magnitude, and the measured crosstalk is reduced from -23.15dB to -29.46dB.
, the mode purity of the distorted VB improves from 26.91% to 93.12% through the compensation. Under the influence of the turbulencewith After supervisory training, the CNN model possesses a strong generalization ability and can efficiently predict the equivalent turbulence phase screen. Taking the advantage of signal processing, we design a CNN model that can automatically extract the characteristic parameters from the distorted intensity distribution of VBs. Here, we propose and experimentally investigate a convolutional neural network (CNN) based atmospheric turbulence compensation method for OAM multiplexing communication. As a result, our study helps to build a deep connection between turbulence distortion and imaging effects through a standard perceptron neural network (NN), where mutual inference between turbulence levels and target recognition rates can be achieved.Ītmospheric turbulence in free-space will distort the helical phase front of vortex beams (VBs) and cause mode diffusion, seriously hindering the practical application of optical orbital angular momentum (OAM) communications. Retrospectively, we also apply the recognition outcomes to evaluate the turbulence strength through regression techniques. In this work, we propose a straightforward approach that treats images with turbulence distortion as a data augmentation in the training set, and investigate the effectiveness of the ML-assisted recognition outcomes under different turbulence strengths.
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Meanwhile, machine learning (ML)-based algorithms have been proposed and studied using both hardware and software approaches to alleviate turbulence effects.
As the aggregated turbulence distortion inevitably degrades remote targets and makes them less recognizable, both adaptive optics approaches and image correction methods will become less effective in retrieving correct attributes of the target. Imaging and target recognition through strong turbulence is regarded as one of the most challenging problems in modern turbulence research.
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