在流和窃听效应下基于机器学习的结构光模式的分类
Applied optics
|June 10, 2024
概括
本研究对可靠的自由空间光学 (FSO) 通信进行了多重结构光模式的分类. 机器学习模型达到92%以上的准确性,提高了数据传输速度,即使在动荡的条件下.
科学领域:
- 光学通信是指光学通信的应用.
- 机器学习 机器学习
- 自由空间光学是自由空间的.
背景情况:
- 多重结构光模式为增强数据传输提供了潜力.
- 自由空间光学 (FSO) 系统面临着流和窃听的挑战.
- 这些模式的分类对于可靠的FSO通信至关重要.
研究的目的:
- 在FSO系统中对多重结构光模式进行分类.
- 评估流和拦截威胁对模式分类的影响.
- 为此分类任务开发和比较机器/深度学习算法.
主要方法:
- 一个实验性的3米FSO系统被用来传输16种模式 (8-ary拉古埃尔高斯和8-ary叠加LG).
- 使用了四种机器/深度学习算法:人工神经网络,支持矢量机器,1D CNN和2D CNN.
- 采用了融合方法,将这些算法的输出结合起来.
主要成果:
- 分类准确度在弱动荡时超过92%,中度动荡时超过81%,强动荡时超过69%.
- 这项研究是第一个在结构光模式分类中同时解决流和拦截威胁的研究.
- 合并模型在不同的流水平上表现出强的性能.
结论:
- 多复合结构光模式显示出在FSO系统中可靠,高容量的数据传输的巨大潜力.
- 机器和深度学习算法在具有挑战性的条件下对这些模式进行分类是有效的.
- 拟议的分类方法提高了在动荡的FSO通道中的通信可靠性.
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