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ANN: A Deep Learning Model for Prediction of Radio Wave Attenuation Due to Clouds

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Abstract

Extremely high data rates above 100 Gbps are expected in future communications technologies, which can be achieved by exploiting higher spectrum bands. Higher frequency bands, such as millimeter wave bands, are predicted to have far wider bandwidths hence 6G will need to promote R&D to use millimeter waves with frequencies ranging from 20 to 100 GHz. The use of these higher frequency bands is complicated by their sensitivity to external ambient conditions such as cloud, fog, dust, and rain. This paper presents a Machine Learning based cloud attenuation model with global applicability. With improved accuracy the Artificial Neural Network (ANN) based model is developed by using real time data set from the AMSER—2 satellite at Indian site. A comparative analysis is carried out in the results and discussion section.

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Funding

This work was not supported by the financial Grant from any organization.

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Authors and Affiliations

  1. Noida Institute of Engineering and Technology affiliated to AKT University Lucknow, Lucknow, India

    Hitesh Singh, Vivek Kumar & Kumud Saxena

  2. CTIF Aarhus University, Herning, Denmark

    Hitesh Singh, Vivek Kumar & Kumud Saxena

  3. Technical University of Sofia, Sofia, Bulgaria

    Boncho Bonev

  4. Future Technologies for Business Ecosystem Innovation, Aarhus University, Herning, Denmark

    Ramjee Prasad

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  1. Hitesh Singh
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  2. Vivek Kumar
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  3. Kumud Saxena
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  4. Boncho Bonev
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  5. Ramjee Prasad
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Contributions

Did work for machine learning model for cloud attenuation which will be helpful in future technology implementation. Add state of art for Cloud attenuation in last few years. Implement machine and AI based model for radio wave propagation particular for 5G signal range.

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Correspondence to Hitesh Singh.

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Singh, H., Kumar, V., Saxena, K. et al. ANN: A Deep Learning Model for Prediction of Radio Wave Attenuation Due to Clouds. Wireless Pers Commun 131, 1415–1435 (2023). https://doi.org/10.1007/s11277-023-10491-4

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