Prediction of Channel Excess Attenuation for Satellite Communication Systems at Q-band Using Artificial Neural Network

Lu Bai, Cheng-Xiang Wang, Qian Xu, Spiros Ventouras, George Goussetis

Research output: Contribution to journalArticle

Abstract

This paper proposes the use of an artificial neural network (ANN) for estimating the fading of a Q-band (39.402 GHz) satellite channel exploiting knowledge of its previous state as well as the present weather conditions. The ANN is trained using weather data and propagation measurements at Q-band obtained during a period of nine months by the Aldo Paraboni receivers of RAL Space at Chilbolton. Subsequently, the estimation obtained by the ANN is compared with actual
propagation measurements on data obtained over a period of three months. Statistical analysis demonstrates agreement between the ANN estimation and the measurement within a 1 dB range with a probability exceeding 98.8%. The significance of this work lies with the opportunities it raises to deliver real-time
fading estimations using low-cost weather sensors combined with feedback on the channel state from the return link, which can be used in the deployment of propagation impairment mitigation techniques (PIMTs).
Original languageEnglish
JournalIEEE Antennas and Wireless Propagation Letters
Publication statusAccepted/In press - 1 Jul 2019

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Satellite communication systems
Neural networks
Statistical methods
Satellites
Feedback
Sensors
Costs

Cite this

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title = "Prediction of Channel Excess Attenuation for Satellite Communication Systems at Q-band Using Artificial Neural Network",
abstract = "This paper proposes the use of an artificial neural network (ANN) for estimating the fading of a Q-band (39.402 GHz) satellite channel exploiting knowledge of its previous state as well as the present weather conditions. The ANN is trained using weather data and propagation measurements at Q-band obtained during a period of nine months by the Aldo Paraboni receivers of RAL Space at Chilbolton. Subsequently, the estimation obtained by the ANN is compared with actualpropagation measurements on data obtained over a period of three months. Statistical analysis demonstrates agreement between the ANN estimation and the measurement within a 1 dB range with a probability exceeding 98.8{\%}. The significance of this work lies with the opportunities it raises to deliver real-timefading estimations using low-cost weather sensors combined with feedback on the channel state from the return link, which can be used in the deployment of propagation impairment mitigation techniques (PIMTs).",
author = "Lu Bai and Cheng-Xiang Wang and Qian Xu and Spiros Ventouras and George Goussetis",
year = "2019",
month = "7",
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language = "English",
journal = "IEEE Antennas and Wireless Propagation Letters",
issn = "1536-1225",
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T1 - Prediction of Channel Excess Attenuation for Satellite Communication Systems at Q-band Using Artificial Neural Network

AU - Bai, Lu

AU - Wang, Cheng-Xiang

AU - Xu, Qian

AU - Ventouras, Spiros

AU - Goussetis, George

PY - 2019/7/1

Y1 - 2019/7/1

N2 - This paper proposes the use of an artificial neural network (ANN) for estimating the fading of a Q-band (39.402 GHz) satellite channel exploiting knowledge of its previous state as well as the present weather conditions. The ANN is trained using weather data and propagation measurements at Q-band obtained during a period of nine months by the Aldo Paraboni receivers of RAL Space at Chilbolton. Subsequently, the estimation obtained by the ANN is compared with actualpropagation measurements on data obtained over a period of three months. Statistical analysis demonstrates agreement between the ANN estimation and the measurement within a 1 dB range with a probability exceeding 98.8%. The significance of this work lies with the opportunities it raises to deliver real-timefading estimations using low-cost weather sensors combined with feedback on the channel state from the return link, which can be used in the deployment of propagation impairment mitigation techniques (PIMTs).

AB - This paper proposes the use of an artificial neural network (ANN) for estimating the fading of a Q-band (39.402 GHz) satellite channel exploiting knowledge of its previous state as well as the present weather conditions. The ANN is trained using weather data and propagation measurements at Q-band obtained during a period of nine months by the Aldo Paraboni receivers of RAL Space at Chilbolton. Subsequently, the estimation obtained by the ANN is compared with actualpropagation measurements on data obtained over a period of three months. Statistical analysis demonstrates agreement between the ANN estimation and the measurement within a 1 dB range with a probability exceeding 98.8%. The significance of this work lies with the opportunities it raises to deliver real-timefading estimations using low-cost weather sensors combined with feedback on the channel state from the return link, which can be used in the deployment of propagation impairment mitigation techniques (PIMTs).

M3 - Article

JO - IEEE Antennas and Wireless Propagation Letters

JF - IEEE Antennas and Wireless Propagation Letters

SN - 1536-1225

ER -