Research On The Application Of Deep Learning To Free-space Vortex Optical Communication System

In recent years,OAM communication has attracted the research interests of many scholars at home and abroad because of its remarkable advantages in communication capacity and spectral efficiency.As a kind of free space optical communication,OAM communication not only has larger communication capacity,but also can quickly build communication links compared with RF wireless communication and optical fiber communication.The orbital angular momentum theoretically has an infinite number of modes and is orthogonal to each other,which provides a new dimension for optical communication,and extends the carrier of information in optical communication from the amplitude,polarization,frequency and phase of optical signal to orbital angular momentum.At the same time,because the free space optical communication takes the free space as the channel,the vortex beam will be affected by the atmospheric turbulence distortion in the transmission process,and the OAM mode will disperse,which seriously affects the communication performance.Therefore,it has important research significance and application value for OAM communication,especially its transmission mode distortion,correction and recognition.This paper focuses on the application of deep learning in OAM communication.Based on the framework of traditional OAM communication system and the shortcomings of existing methods,OAM mode recognition technology and turbulence distortion compensation technology based on deep learning are further studied and improved.The feasibility and superiority of the scheme proposed in this paper are verified by theoretical analysis and simulation experiments.The main work of this paper mainly includes the following three parts:(1)An OAM mode recognition technology based on attention pyramid convolution neural network is proposed.Through the simulation data,it is found that although the light intensity distribution of conjugate-mode vortex beam presents petal shape,which is conducive to recognize,the light intensity distributions of some multi-mode vortex beams are very similar;and after plane wave interference,the interference fringes of single-mode vortex beams with large topological charge are dense and difficult to recognize.After atmospheric turbulence,the map received by CCD camera is distorted,which makes the similar map more difficult to distinguish.To solve the above problems,this paper adds a dual path structure combined with the attention pyramid after Resnet network.Simulation experiments are carried out under different transmission conditions(turbulence intensity and transmission distance).By comparing the recognition accuracy and bit error rate,it shows that the proposed scheme can further improve the accuracy of OAM mode recognition.(2)An OAM mode recognition technology based on deep mutual learning is proposed.In order to ensure the accuracy of the OAM mode recognition at the receiving end,the recognition network has high complexity,which conflicts with the requirements of optical communication system deployment at the mobile end.For the large-scale application of OAM communication,combined with the needs of industrialization,in this paper,deep mutual learning is introduced to make each recognition network train and learn from each other at the same time.Based on the analysis of simulation data,it shows that the proposed recognition scheme can greatly reduce the amount of parameters and calculations under the premise of little loss of recognition accuracy.At the same time,the OAM mode recognition accuracy can be further improved by increasing the number of networks in the mutual learning queue.(3)An end-to-end turbulence distortion compensation technology based on generative adversarial network is proposed.Most of the existing wavefront correction techniques based on deep learning are based on wavefront reconstruction algorithm.The wavefront is reconstructed by training the mapping relationship between the light intensity distribution of the probe beam and the turbulent Zernike coefficient.Compared with the existing schemes,in this paper,only the light intensity distribution of OAM beam captured by CCD camera is used to directly output the light intensity distribution after compensation,which does not need to separate the probe beam to reconstruct the wavefront and control the optical devices for compensation,making full use of the characteristics of OAM beam as spatial structured light.At the same time,the loss function of SSIM is added on the basis of Pix2pix and CycleGAN.Simulation experiments are designed to evaluate the compensation performance from two aspects:subjective indicators and objective indicators.The experimental result shows that the proposed scheme can ensure excellent compensation performance without reconstructing wavefront and using optical hardware.