用反复的神经网络进行光束漫游预测
Optics express
|September 15, 2023
概括
这项研究引入了一种循环神经网络 (RNN),用于预测自由空间光通信 (FSO) 中的光束漫游. RNN方法显著提高了预测准确性,优于稳定光学链路的传统方法.
科学领域:
- 光学工程是指光学工程.
- 人工智能的人工智能
- 电信 电信服务 电信服务 电信服务
背景情况:
- 波束流浪是自由空间光通信 (FSO) 系统面临的主要挑战.
- 在FSO系统中结构光束的错位导致接收器交叉声波的增加.
- 流引起的色会影响FSO链路的可靠性.
研究的目的:
- 开发和评估一个循环神经网络 (RNN),用于预测FSO系统中的光束流浪.
- 调查RNN方法在不同光束类型和数据采样场景中的性能.
- 评估基于RNN的预测对缓解FSO链路损伤的潜力.
主要方法:
- 一个循环神经网络 (RNN) 模型被用来预测未来的光束流浪基于历史的光束中心的质量位置.
- 使用低样本实验数据 (260m链接) 和超样本模拟数据,测试了RNN方法.
- 这项研究分析了高斯,赫米特-高斯和拉格尔-高斯光束的光束流浪.
主要成果:
- 与天真和线性方法相比,基于RNN的预测方法在预测错误中显示出20-40%的改善.
- 该方法在各种场景中表现出强的表现,包括预测未来的多个样本.
- 在所有调查的案例中,RNN模型与其他预测方法的性能相匹配或超过.
结论:
- 拟议的RNN解决方案有效地预测了FSO系统中的光束流浪,优于现有的方法.
- 这种预测能力可以帮助减轻流引起的色,并提高FSO系统的可靠性.
- 潜在的应用包括智能转发,服务质量改进和预测适应光学.
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