An Atmospheric Data-Driven Q-Band Satellite Channel Model With Feature Selection

Lu Bai; Qian Xu; Ziwei Huang; Shangbin Wu; Spiros Ventouras; George Goussetis; Xiang Cheng

doi:10.1109/TAP.2021.3137285

An Atmospheric Data-Driven Q-Band Satellite Channel Model With Feature Selection

Lu Bai, Qian Xu, Ziwei Huang, Shangbin Wu, Spiros Ventouras, George Goussetis, Xiang Cheng

Research output: Contribution to journal › Article › peer-review

17 Citations (Scopus)

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Abstract

This article proposes a novel atmospheric data-driven Q-band satellite channel model using two artificial neural networks, i.e., multilayer perceptron and long short-term memory (LSTM), to estimate real-time channel attenuation at Q-band via a set of atmospheric parameters. Seven atmospheric parameters for modeling satellite channel attenuation are selected by the least absolute shrinkage and selection operator (LASSO) algorithm from 14 commonly used atmospheric parameters. Simulation results demonstrate that the multilayer perceptron-based atmospheric data-driven Q-band satellite channel model via those seven atmospheric parameters is more accurate and less complex than that via the 14 atmospheric parameters. Meanwhile, the accuracy performance of multilayer perceptron- and LSTM-based atmospheric data-driven Q-band satellite channel models, such as absolute errors and mean-squared errors (MSEs), is discussed and analyzed. The complexity of multilayer perceptron and LSTM in this model, such as training time, loading time, and estimation time, is also investigated. It can be seen that the estimated channel attenuation can well align with the measured channel attenuation.

Original language	English
Pages (from-to)	4002-4013
Number of pages	12
Journal	IEEE Transactions on Antennas and Propagation
Volume	70
Issue number	6
Early online date	28 Dec 2021
DOIs	https://doi.org/10.1109/TAP.2021.3137285
Publication status	Published - Jun 2022

Keywords

Data driven
Q-band
feature selection
key atmospheric parameters
satellite communication channel attenuation

ASJC Scopus subject areas

Electrical and Electronic Engineering

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10.1109/TAP.2021.3137285

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title = "An Atmospheric Data-Driven Q-Band Satellite Channel Model With Feature Selection",

abstract = "This article proposes a novel atmospheric data-driven Q-band satellite channel model using two artificial neural networks, i.e., multilayer perceptron and long short-term memory (LSTM), to estimate real-time channel attenuation at Q-band via a set of atmospheric parameters. Seven atmospheric parameters for modeling satellite channel attenuation are selected by the least absolute shrinkage and selection operator (LASSO) algorithm from 14 commonly used atmospheric parameters. Simulation results demonstrate that the multilayer perceptron-based atmospheric data-driven Q-band satellite channel model via those seven atmospheric parameters is more accurate and less complex than that via the 14 atmospheric parameters. Meanwhile, the accuracy performance of multilayer perceptron- and LSTM-based atmospheric data-driven Q-band satellite channel models, such as absolute errors and mean-squared errors (MSEs), is discussed and analyzed. The complexity of multilayer perceptron and LSTM in this model, such as training time, loading time, and estimation time, is also investigated. It can be seen that the estimated channel attenuation can well align with the measured channel attenuation.",

keywords = "Data driven, Q-band, feature selection, key atmospheric parameters, satellite communication channel attenuation",

author = "Lu Bai and Qian Xu and Ziwei Huang and Shangbin Wu and Spiros Ventouras and George Goussetis and Xiang Cheng",

note = "Funding Information: This work was supported in part by the National Natural Science Foundation of China under Grant 62001018 and Grant 62125101. Publisher Copyright: {\textcopyright} 1963-2012 IEEE.",

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AU - Xu, Qian

AU - Huang, Ziwei

AU - Wu, Shangbin

AU - Ventouras, Spiros

AU - Goussetis, George

AU - Cheng, Xiang

PY - 2022/6

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AB - This article proposes a novel atmospheric data-driven Q-band satellite channel model using two artificial neural networks, i.e., multilayer perceptron and long short-term memory (LSTM), to estimate real-time channel attenuation at Q-band via a set of atmospheric parameters. Seven atmospheric parameters for modeling satellite channel attenuation are selected by the least absolute shrinkage and selection operator (LASSO) algorithm from 14 commonly used atmospheric parameters. Simulation results demonstrate that the multilayer perceptron-based atmospheric data-driven Q-band satellite channel model via those seven atmospheric parameters is more accurate and less complex than that via the 14 atmospheric parameters. Meanwhile, the accuracy performance of multilayer perceptron- and LSTM-based atmospheric data-driven Q-band satellite channel models, such as absolute errors and mean-squared errors (MSEs), is discussed and analyzed. The complexity of multilayer perceptron and LSTM in this model, such as training time, loading time, and estimation time, is also investigated. It can be seen that the estimated channel attenuation can well align with the measured channel attenuation.

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An Atmospheric Data-Driven Q-Band Satellite Channel Model With Feature Selection

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