在海洋动荡中对束的实验识别,结合了Gerchberg-Saxton算法和卷积神经网络
Applied optics
|March 4, 2024
概括
这项研究引入了一种新的Gerchberg-Saxton (GS) 算法和卷积神经网络 (CNN) 混合方法 (GS-CNN),通过增强在海洋动荡 (OT) 中的束识别来改善水下无线光通信 (UWOC). 该GS-CNN方法显著提高了精度,并减少了传输数字图像的错误.
科学领域:
- 光学通信是指光学通信.
- 信号处理 信号处理
- 降低流的缓解方式
背景情况:
- 水下无线光通信 (UWOC) 使用带有轨道角动量 (OAM) 的束来增强通道容量.
- 海洋流 (OT) 显著降低了OAM波束的性能,导致识别错误.
研究的目的:
- 开发一种强大的方法,以减轻UWOC系统中海洋流的影响.
- 为了提高识别精度和减少基于OAM的UWOC中的比特错误比率 (BER).
主要方法:
- 提出了一种基于Gerchberg-Saxton (GS) 算法的复原和基于卷积神经网络 (CNN) 的识别 (GS-CNN) 的混合方法.
- 使用叠加的拉盖尔-高斯 (Laguerre-Gaussian,LG) 束,以小的拓电荷作为信息载体.
- 在高海洋流强度下实验测试了GS-CNN方法 (Cn2高达10^-11 K^2 m^-2/3).
主要成果:
- 具有小拓电荷的叠加LG束被确定为最佳信息载体.
- 即使在强烈的海洋动荡下,GS-CNN方法也证明了它的有效性.
- 在使用GS-CNN时,图像识别精度从0.75增加到0.93,并且BER从3.98x10^-2降低到6.52x10^-3在使用GS-CNN时相比仅使用CNN时.
结论:
- 拟议的GS-CNN方法显著提高了基于OAM的UWOC系统在动荡的水下环境中的性能.
- 混合方法为可靠的水下光通信提供了一个有希望的解决方案.
- 该研究强调了特定的LG光束配置和GS-CNN对于强大的OAM信号恢复和识别的有效性.
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