Tropospheric Ducting: A Comprehensive Review and Machine Learning-Based Classification Advancements
Tropospheric ducts, characterized by anomalous positive vertical refractive index gradients, significantly influence radio wave propagation by enhancing signal strength and extending communication ranges. While beneficial in some contexts, these phenomena can disrupt communication systems, particula...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10858737/ |
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| author | Mohammed Banafaa Ali Hussein Muqaibel |
| author_facet | Mohammed Banafaa Ali Hussein Muqaibel |
| author_sort | Mohammed Banafaa |
| collection | DOAJ |
| description | Tropospheric ducts, characterized by anomalous positive vertical refractive index gradients, significantly influence radio wave propagation by enhancing signal strength and extending communication ranges. While beneficial in some contexts, these phenomena can disrupt communication systems, particularly in maritime and coastal regions where ducting conditions are prevalent. This paper addresses the challenge of accurately predicting and mitigating the effects of tropospheric ducts on radio propagation, which is critical for the reliability of next-generation communication systems. We present a comprehensive review of radio propagation in tropospheric ducts, encompassing fundamental concepts, theoretical models, and estimation techniques. To enhance the classification of anomalous propagation in the troposphere, including ducting, we develop machine learning models based on real-world data obtained from King Fahd University of Petroleum and Minerals (KFUPM). Our findings demonstrate that machine learning models, particularly support vector machines, can effectively classify ducting conditions, offering superior predictive performance compared to other models. The advancements presented in this paper address the challenges faced by next-generation radio systems and contribute to the development of effective mitigation strategies. The insights gained pave the way for further research and optimization of communication system performance in duct-prone environments. |
| format | Article |
| id | doaj-art-796e84b5ce204183a2b486a392a3d680 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-796e84b5ce204183a2b486a392a3d6802025-02-07T00:01:18ZengIEEEIEEE Access2169-35362025-01-0113225102253410.1109/ACCESS.2025.353716010858737Tropospheric Ducting: A Comprehensive Review and Machine Learning-Based Classification AdvancementsMohammed Banafaa0https://orcid.org/0000-0002-7239-2571Ali Hussein Muqaibel1https://orcid.org/0000-0001-7865-1987Electrical Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran, Saudi ArabiaElectrical Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran, Saudi ArabiaTropospheric ducts, characterized by anomalous positive vertical refractive index gradients, significantly influence radio wave propagation by enhancing signal strength and extending communication ranges. While beneficial in some contexts, these phenomena can disrupt communication systems, particularly in maritime and coastal regions where ducting conditions are prevalent. This paper addresses the challenge of accurately predicting and mitigating the effects of tropospheric ducts on radio propagation, which is critical for the reliability of next-generation communication systems. We present a comprehensive review of radio propagation in tropospheric ducts, encompassing fundamental concepts, theoretical models, and estimation techniques. To enhance the classification of anomalous propagation in the troposphere, including ducting, we develop machine learning models based on real-world data obtained from King Fahd University of Petroleum and Minerals (KFUPM). Our findings demonstrate that machine learning models, particularly support vector machines, can effectively classify ducting conditions, offering superior predictive performance compared to other models. The advancements presented in this paper address the challenges faced by next-generation radio systems and contribute to the development of effective mitigation strategies. The insights gained pave the way for further research and optimization of communication system performance in duct-prone environments.https://ieeexplore.ieee.org/document/10858737/Atmospheric ductanomalous propagationducting channelrefractivity indexremote sensingduct estimation |
| spellingShingle | Mohammed Banafaa Ali Hussein Muqaibel Tropospheric Ducting: A Comprehensive Review and Machine Learning-Based Classification Advancements IEEE Access Atmospheric duct anomalous propagation ducting channel refractivity index remote sensing duct estimation |
| title | Tropospheric Ducting: A Comprehensive Review and Machine Learning-Based Classification Advancements |
| title_full | Tropospheric Ducting: A Comprehensive Review and Machine Learning-Based Classification Advancements |
| title_fullStr | Tropospheric Ducting: A Comprehensive Review and Machine Learning-Based Classification Advancements |
| title_full_unstemmed | Tropospheric Ducting: A Comprehensive Review and Machine Learning-Based Classification Advancements |
| title_short | Tropospheric Ducting: A Comprehensive Review and Machine Learning-Based Classification Advancements |
| title_sort | tropospheric ducting a comprehensive review and machine learning based classification advancements |
| topic | Atmospheric duct anomalous propagation ducting channel refractivity index remote sensing duct estimation |
| url | https://ieeexplore.ieee.org/document/10858737/ |
| work_keys_str_mv | AT mohammedbanafaa troposphericductingacomprehensivereviewandmachinelearningbasedclassificationadvancements AT alihusseinmuqaibel troposphericductingacomprehensivereviewandmachinelearningbasedclassificationadvancements |