Free-Space Optical Channel Turbulence Prediction: A Machine Learning Approach

IF 3.7 3区计算机科学 Q2 TELECOMMUNICATIONS

IEEE Communications Letters Pub Date : 2025-03-26 DOI:10.1109/LCOMM.2025.3555162

Md Zobaer Islam;Ethan Abele;Fahim Ferdous Hossain;Arsalan Ahmad;Sabit Ekin;John F. O’Hara

引用次数: 0

Abstract

Channel turbulence is a formidable obstacle for free-space optical (FSO) communication. Anticipation of turbulence levels is highly important for mitigating disruptions but has not been demonstrated without dedicated, auxiliary hardware. We show that machine learning (ML) can be applied to raw FSO data streams to rapidly predict channel turbulence levels with no additional sensing hardware. FSO was conducted through a controlled channel in the lab under six distinct turbulence levels, and the efficacy of using ML to classify turbulence levels was examined. ML-based turbulence level classification was found to be >98% accurate with multiple ML training parameters. Classification effectiveness was found to depend on the timescale of changes between turbulence levels but converges when turbulence stabilizes over about a one minute timescale.

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自由空间光通道湍流预测：一种机器学习方法

信道湍流是自由空间光通信的一个巨大障碍。湍流水平的预测对于减少干扰非常重要，但如果没有专用的辅助硬件，就无法证明这一点。我们表明，机器学习（ML）可以应用于原始FSO数据流，以快速预测信道湍流水平，而无需额外的传感硬件。FSO通过实验室的受控通道在六种不同的湍流水平下进行，并检验了使用ML对湍流水平进行分类的有效性。在多个ML训练参数的情况下，基于ML的湍流等级分类准确率达到bb0 98%。发现分类有效性取决于湍流水平变化的时间尺度，但当湍流稳定超过约一分钟的时间尺度时，分类有效性收敛。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Communications Letters 工程技术-电信学

CiteScore

8.10

自引率

7.30%

发文量

590

审稿时长

2.8 months

期刊介绍： The IEEE Communications Letters publishes short papers in a rapid publication cycle on advances in the state-of-the-art of communication over different media and channels including wire, underground, waveguide, optical fiber, and storage channels. Both theoretical contributions (including new techniques, concepts, and analyses) and practical contributions (including system experiments and prototypes, and new applications) are encouraged. This journal focuses on the physical layer and the link layer of communication systems.