Free-Space Optical Channel Turbulence Prediction: A Machine Learning Approach
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arXiv
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| Format: | Preprint |
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2024
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| author | Islam, Md Zobaer Abele, Ethan Hossain, Fahim Ferdous Ahmad, Arsalan Ekin, Sabit O'Hara, John F. |
| author_facet | Islam, Md Zobaer Abele, Ethan Hossain, Fahim Ferdous Ahmad, Arsalan Ekin, Sabit O'Hara, John F. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_16729 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Free-Space Optical Channel Turbulence Prediction: A Machine Learning Approach Islam, Md Zobaer Abele, Ethan Hossain, Fahim Ferdous Ahmad, Arsalan Ekin, Sabit O'Hara, John F. Systems and Control Machine Learning 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. |
| title | Free-Space Optical Channel Turbulence Prediction: A Machine Learning Approach |
| topic | Systems and Control Machine Learning |
| url | https://arxiv.org/abs/2405.16729 |