TY - GEN
T1 - Deep Learning based Scintillation Prediction for Satellite Link using Measured Data
AU - Kumar, Rajnish
AU - Arnon, Shlomi
N1 - Funding Information: ACKNOWLEDGEMENT The research was carried out with the technological support and funding from the Genesis Consortium, the Israel Innovation Authority, and the Israel Space Agency. The authors would like to thank Advantech Wireless for providing the measurement data at the site of Petah Tikva, Israel. They would also like to thank Kreitman School of advanced Graduate Studies and Ben-Gurion University of the Negev, Israel for providing fellowships to continue the research. The authors would also like to thank the anonymous reviewers for providing valuable suggestions to improve the article. Publisher Copyright: © 2022 IEEE.
PY - 2022/8/18
Y1 - 2022/8/18
N2 - The satellite communication link is affected by the inhomogenities of the refractive index over the atmospheric channel that causes rapid variations in the amplitude of the received signal at the ground station i.e. scintillation fading. Such scintillation fading show significant enhanced activity depending on certain time during a typical day and seasonal variation that could act as a potential degrading source especially for a low-margin satellite link under clear weather condition. In this work, we apply a recurrent neural network (RNN) for the prediction of scintillation fading at a future observation time that is 240 milliseconds ahead (around the round trip delay of a Geostationary satellite). We show that the performance metric of the predicted scintillation- root mean square error and mean absolute error are observed to be very low. The error histogram reveals that the error mostly lies within the range of 0.02 dB on a typical clear weather day and thus making it extremely useful for adapting to fade mitigation techniques in real time for a satellite link.
AB - The satellite communication link is affected by the inhomogenities of the refractive index over the atmospheric channel that causes rapid variations in the amplitude of the received signal at the ground station i.e. scintillation fading. Such scintillation fading show significant enhanced activity depending on certain time during a typical day and seasonal variation that could act as a potential degrading source especially for a low-margin satellite link under clear weather condition. In this work, we apply a recurrent neural network (RNN) for the prediction of scintillation fading at a future observation time that is 240 milliseconds ahead (around the round trip delay of a Geostationary satellite). We show that the performance metric of the predicted scintillation- root mean square error and mean absolute error are observed to be very low. The error histogram reveals that the error mostly lies within the range of 0.02 dB on a typical clear weather day and thus making it extremely useful for adapting to fade mitigation techniques in real time for a satellite link.
KW - Ku bands
KW - Recurrent neural network
KW - Satellite communication
KW - Scintillation fading
UR - http://www.scopus.com/inward/record.url?scp=85138032199&partnerID=8YFLogxK
U2 - https://doi.org/10.1109/TSP55681.2022.9851250
DO - https://doi.org/10.1109/TSP55681.2022.9851250
M3 - Conference contribution
T3 - 2022 45th International Conference on Telecommunications and Signal Processing, TSP 2022
SP - 246
EP - 249
BT - 45th International Conference on Telecommunications and Signal Processing, TSP 2022
A2 - Herencsar, Norbert
T2 - 45th International Conference on Telecommunications and Signal Processing, TSP 2022
Y2 - 13 July 2022 through 15 July 2022
ER -