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Methods of signal processing in atmospheric optical communication systems based on machine learning technologies

Field of Science:Artificial IntelligenceComputational Theory and MathematicsComputer Graphics and Computer-Aided DesignComputer Networks and CommunicationsComputer Science (miscellaneous)Information SystemsSignal ProcessingSoftware
National field of science (HAC):05.01.01 — Engineering geometry and computer graphics. Audio and video technologies05.01.02 — Systems analysis, control and information processing05.01.03 — Theoretical foundations of informatics05.01.04 — Mathematical and software support of computers, complexes and computer networks05.01.05 — Information protection methods and systems. Information security05.02.03 — Technological machines. Robots, mechatronics and robotic systems05.01.08 — Automation and control of technological processes and productions05.01.09 — Document studies. Archival science. Library science05.01.10 — Information retrieval systems and processes05.01.11 — Digital technologies and artificial intelligence05.04.01 — Telecommunication and computer systems, telecommunication networks and devices. Information distribution05.04.02 — Radio engineering, radionavigation, radiolocation and television systems and devices. Mobile and fiber-optic communication systems08.00.14 — Information systems and technologies in economics08.00.16 — Digital economy and international digital integration11.00.07 — Geoinformatics21.01.09 — Military command and communication systems21.02.12 — Military cybernetics, systems analysis, operations research, modeling of combat operations and military systems (including Armed Forces branches, arms of service and special units)05.01.06 — Elements and devices of computer engineering and control systems05.01.07 — Mathematical modeling. Numerical methods and software packages10.00.11 — Language theory. Applied and computational linguistics13.00.06 — Theory and methods of e-learning (by education fields and levels)21.02.13 — Informatics and computer technologies in military affairs
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Scientific electronic journal "Digital Economy"

Scientific electronic journal "Digital Economy"

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ARTICLE ANNOTATION

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This article analyzes the role of machine learning methods in transmitting and receiving signals in atmospheric optical communication systems (AOAT). The advantages of machine learning models in reducing the negative impact of atmospheric turbulence, meteorological conditions and various noise on signal quality are presented.

AUTHORS

M.Sultonova

"MUHAMMAD AL-XORAZMIY NOMIDAGI TOSHKENT AXBOROT TEXNOLOGIYALARI UNIVERSITETI" DAVLAT MUASSASASI

Tags

# efficiency# communication# signal# networks# neural networks# machine learning# optical# telecommunications# atmosphere# weather

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Article Identifiers

ROI:

https://eroi.uz/11.142/A026-372113

DOI:

Not Available

References

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Mohamed-Slim Aloui "Beam Wander Prediction with Recurrent Neural Networks for Free-Space Optical Communications" IEEE Global Communications Conference (GLOBECOM 2020). IEEE Xplore Digital Library. 6 bet

Steve Hranilovic "End-to-End Learning for Block-Coded Free-Space Optical Communications" Journal of Lightwave Technology (Volume: 38, Issue: 15) 1 август, 2020 йил. 3955 – 3965-бетлар.

X. Liu, L. Zhang etc."Deep Learning-Based Signal Detection for Free-Space Optical Communications Under Strong Atmospheric Turbulence" IEEE Photonics Journal, August 2024.

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A. Sharma, S. Chaudhary. "Machine Learning Assisted Mitigation of Atmospheric Turbulence in FSO Systems: A Comparative Study". Optics Communications (Elsevier), February 2025.

K. Singh, J. Malhotra. "Intelligent Signal Processing in FSO Communication Using Recurrent Neural Networks". Sensors (MDPI), December 2024.

X. Khasanov, A. Abduazizov. "Application of Neural Networks for Noise Reduction in FSO Systems under Foggy Conditions". International Conference on Information Science and Communications Technologies (ICISCT), Tashkent, November 2024.

M. Djurayev, R. Ganiyev. "Optimizing Optical Signal Power in FSO Using Random Forest Regressor". Journal of Physics: Conference Series, 2025 (Conference Proceedings).