[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121203-en":3,"doc-seo-121203-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121203,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Free-Space Optical Channel Turbulence Prediction - A Machine Learning Approach","Channel turbulence poses a major obstacle to free-space optical (FSO) communication, because random atmospheric refractive-index fluctuations degrade intensity and phase, causing beam wander and scintillation. Reliable anticipation of turbulence levels is crucial for mitigating disruption, yet prior approaches often require dedicated auxiliary sensing hardware. This study applies machine learning to raw FSO data streams to rapidly predict turbulence levels without extra sensing hardware. Experiments used a controlled laboratory channel across six turbulence regimes, achieving over 98% classification accuracy and showing dependence on turbulence-change timescale, converging near a one-minute stabilization.","Free-Space Optical Channel Turbulence Prediction: A Machine Learning Approach  \nMd Zobaer Islam, Ethan Abele, Fahim Ferdous Hossain, Arsalan Ahmad, Senior Member, IEEE, Sabit Ekin, Senior Member, IEEE, and John F. O’Hara, Senior Member, IEEE  \narXiv :2405 . 16729v2 [ ee ss . SY] 25 Mar 2025  \nAbstract—Channel turbulence is a formidable obstacle for freespace 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 ef􀀂cacy of using ML to classify turbulence levels was examined. ML-based turbulence level classi􀀂cation was found to be > 98% accurate with multiple ML training parameters. Classi􀀂cation effectiveness was found to depend on the timescale of changes between turbulence levels but converges when turbulence stabilizes over about a one minute timescale.  \nIndex Terms—Free space optical communication, channel turbulence prediction.  \nI. INTRODUCTION  \nFree-space optical (FSO) communication is emerging asa critical technology for high-speed, wireless transmission of data in certain applications. It can bypass terrain where guided-wave communication systems are impractical, and it can be rapidly deployed in disaster areas. FSO also offers enhanced security, reduced size, weight, and power (SWaP), and increased bandwidth compared to radio frequency (RF) systems. These advantages have driven wide adoption of FSO in applications from satellite communications to terrestrial point-to-point links.  \nNevertheless, FSO still faces signi􀀂cant practical challenges, such as fog, pointing error, and atmospheric turbulence. The last of these is a major source of signal degradation, even in clear weather [1], [2] . Turbulence is a phenomenon that refers to the random 􀀃uctuations in the refractive index of the atmosphere [3] . These 􀀃uctuations cause variations in the intensity and phase of the transmitted optical signal, leading to beam wander and scintillation at the receiver [4] . The results are signal fading, distortion, increased bit-error rates, and generally worsened performance/reliability.  \nThis work was supported in part by the National Aeronautics and Space Administration under Grant 80NSSC20M0214 . (Corresponding author: Ethan Abele, John F. O’Hara.)  \nMd Zobaer Islam, Ethan Abele, Fahim Ferdous Hossain, and John F. O’Hara are with the School of Electrical and Computer Engineering, Oklahoma State University, Oklahoma, USA (e-mail: zobaer.islam, eabele, fferdou, [oharaj](oharaj{@okstate.edu})[{](oharaj{@okstate.edu})[@okstate.edu](oharaj{@okstate.edu})[}](oharaj{@okstate.edu}))  \nArsalan Ahmad is with the Department of Electrical and Computer Engineering, Iowa State University, Ames, Iowa, USA (e-mail: aah[mad@iastate.edu](mad@iastate.edu))  \nSabit Ekin is with the Departments of Engineering Technology, and Electrical & Computer Engineering, Texas A&M University, College Station, Texas, USA (e-mail: [sabitekin@tamu.edu](sabitekin@tamu.edu))  \nTurbulence is a major loss factor in most FSO systems [5],[6]and link budgets rely on accurate turbulence estimation for both Earth-to-space and terrestrial applications. Much effort has been focused on overcoming it by various means, including increased transmitted power, adaptive optics, and optimization of beam width [7] . However, many of these methods require or bene􀀂t from an accurate estimate of the turbulence levels. A common parameter used in quantifying turbulence level is the refractive index structure constant C2n , which can vary greatly in position and time [8], [9] . Orders of magnitude difference can be seen from morning to night, with large 􀀃uctuations also occurring on the scale of minutes. Sever","cbCaivrR4qTvPCvW","https://ap.wps.com/l/cbCaivrR4qTvPCvW","pdf",3662494,1,5,"English","en",105,"# Introduction\n## Free-space optical communication advantages and use cases\n## Atmospheric turbulence effects on signal quality\n## Need for turbulence estimation and existing challenges\n## Machine learning for turbulence prediction\n## Study approach and experimental context","[{\"question\":\"Why is channel turbulence a critical problem for free-space optical communication?\",\"answer\":\"Turbulence introduces random refractive-index fluctuations that alter optical intensity and phase, producing beam wander and scintillation. This leads to signal fading and increased bit-error rates, reducing performance and reliability.\"},{\"question\":\"What approach does the document propose for turbulence prediction?\",\"answer\":\"It proposes using machine learning on raw FSO data streams to classify turbulence levels quickly. The method avoids dedicated auxiliary sensing hardware.\"},{\"question\":\"How effective is the machine learning classification, and what factors influence it?\",\"answer\":\"ML-based turbulence level classification achieves greater than 98% accuracy under multiple training settings. Performance depends on the timescale of turbulence changes and converges when turbulence stabilizes over roughly one minute.\"}]","Free-Space Optical Channel Turbulence Prediction - A Machine Learning Approach | PDF",1785734336,13,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"free-space-optical-channel-turbulence-prediction-a-machine-learning-approach","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/free-space-optical-channel-turbulence-prediction-a-machine-learning-approach/121203/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is channel turbulence a critical problem for free-space optical communication?","Question",{"text":75,"@type":76},"Turbulence introduces random refractive-index fluctuations that alter optical intensity and phase, producing beam wander and scintillation. This leads to signal fading and increased bit-error rates, reducing performance and reliability.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What approach does the document propose for turbulence prediction?",{"text":80,"@type":76},"It proposes using machine learning on raw FSO data streams to classify turbulence levels quickly. The method avoids dedicated auxiliary sensing hardware.",{"name":82,"@type":73,"acceptedAnswer":83},"How effective is the machine learning classification, and what factors influence it?",{"text":84,"@type":76},"ML-based turbulence level classification achieves greater than 98% accuracy under multiple training settings. 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