Abstract
Rain attenuation is an important factor affecting wireless communication systems throughout the world. Rain attenuation and rain rate data are collected using multichannel radiometer and laser precipitation monitor at a tropical location. The dataset obtained is used to initially propose an empirical model for prediction of rain attenuation from rain rate data. An alternative model using linear spline regression-based machine learning is also used to predict rain attenuation. The machine learning-based model is found to be more accurate by an appreciable degree compared to the empirical model proposed in the previous instance.
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The authors acknowledge the Department of Electronics and Communication Engineering, Techno International New Town, for providing the necessary support and resources.
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Islam, M.A., Maiti, M., Ghosh, P.K., Sanyal, J. (2021). Machine Learning-Based Rain Attenuation Prediction Model. In: Das, N.R., Sarkar, S. (eds) Computers and Devices for Communication. CODEC 2019. Lecture Notes in Networks and Systems, vol 147. Springer, Singapore. https://doi.org/10.1007/978-981-15-8366-7_3
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DOI: https://doi.org/10.1007/978-981-15-8366-7_3
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