Adaptive demodulation by deep-learning-based identification of fractional orbital angular momentum modes with structural distortion due to atmospheric turbulence

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.

Defect prediction of big data computer network based on deep learning model

“…For the first research question RQ1, the experiment uses the six datasets in Table 1, the basic model refers to the method of direct classification without feature extraction, it is compared with the software defect prediction model based on deep self coding computer network (DA) proposed by the author in the case of using three different classifiers Softmax, SVM and LR. The experimental results are shown in Table 3 to Table 5, the bold numbers represent the better results [7][8]. From the experimental results in Table 3 to Table 5, we can see that for NASA dataset kel and pel, the performance of the method proposed by the author on various evaluation indicators under the three classifiers has not improved significantly, and the results of many indicators are not even as good as the benchmark model.…”