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Free-space 16-ary orbital angular momentum coded optical communication system based on chaotic interleaving and convolutional neural networks

机译:基于混沌交错和卷积神经网络的自由空间16- ary轨道角动量编码光通信系统

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Recently, orbital angular momentum (OAM) rays passing through free space have attracted the attention of researchers in the field of free-space optical communication systems. Throughout free space, the OAM states are subject to atmospheric turbulence (AT) distortion leading to crosstalk and power discrepancies between states. In this paper, a novel chaotic interleaver is used with low-density parity-check coded OAM-shift keying through an AT channel. Moreover, a convolutional neural network (CNN) is used as an adaptive demodulator to enhance the performance of the wireless optical communication system. The detection process with the conjugate light field method in the presence of chaotic interleaving has a better performance compared to that without chaotic interleaving for different values of propagation distance. Also, the viability of the proposed system is verified by conveying a digital image in the presence of distinctive turbulence conditions with different error correction codes. The impacts of turbulence strength, transmission distance, signal-to-noise ratio (SNR), and CNN parameters and hyperparameters are investigated and taken into consideration. The proposed CNN is chosen with the optimal parameter and hyperparameter values that yield the highest accuracy, utmost mean average precision (MAP), and the largest value of area under curve (AUC) for the different optimizers. The simulation results affirm that the proposed system can achieve better peak SNR values and lower mean square error values in the presence of different AT conditions. By computing accuracy, MAP, and AUC of the proposed system, we realize that the stochastic gradient descent with momentum and the adaptive moment estimation optimizers have better performance compared to the root mean square propagation optimizer. (C) 2020 Optical Society of America.
机译:最近,通过自由空间的轨道角动量(OAM)光线引起了自由空间光通信系统领域的研究人员的注意。在整个自由空间中,OAM状态受到大气湍流(AT)失真,导致状态之间的串扰和电源差异。在本文中,一种新的混沌交织器与沿AT通道键控的低密度奇偶校验编码的OAM转换使用。此外,卷积神经网络(CNN)用作自适应解调器,以增强无线光通信系统的性能。在混沌交织存在下,在混沌交织的存在下的检测过程具有更好的性能,而没有用于不同的传播距离的不同值的混沌交织。而且,通过在具有不同误差校正码的不同湍流条件存在下传送数字图像来验证所提出的系统的可行性。研究了湍流强度,传输距离,信噪比(SNR)和CNN参数和高参数的影响,并考虑到了。选择了CNN的最佳参数和超参数值,从而产生最高精度,最大平均平均精度(MAP)以及不同优化器的曲线(AUC)下的最大值。仿真结果确认所提出的系统可以在不同在条件下实现更好的峰值SNR值和低均线误差值。通过计算所提出的系统的精度,地图和AUC,我们意识到与势均方的动量和自适应力矩估计优化器的随机梯度下降具有更好的性能,与根均线广场传播优化器相比具有更好的性能。 (c)2020美国光学学会。

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