Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Satellite to Ground Station, Attenuation Prediction for 2.4-72GHz Using LTSM, An Artificial Recurrent Neural Network Technology

Version 1 : Received: 1 December 2021 / Approved: 2 December 2021 / Online: 2 December 2021 (11:18:57 CET)

How to cite: Domb, M.; Leshem, G. Satellite to Ground Station, Attenuation Prediction for 2.4-72GHz Using LTSM, An Artificial Recurrent Neural Network Technology. Preprints 2021, 2021120031 (doi: 10.20944/preprints202112.0031.v1). Domb, M.; Leshem, G. Satellite to Ground Station, Attenuation Prediction for 2.4-72GHz Using LTSM, An Artificial Recurrent Neural Network Technology. Preprints 2021, 2021120031 (doi: 10.20944/preprints202112.0031.v1).

Abstract

Free-space communication is a leading component in global communications. Its advantages relate to a broader signal spread, no wiring, and ease of engagement. Satellite communication services became recently attractive to mega-companies that foresee an excellent opportunity to connect disconnected remote regions, serve emerging machine-to-machine communication, Internet-of-things connectivity, and more. Satellite communication links suffer from arbitrary weather phenomena such as clouds, rain, snow, fog, and dust. In addition, when signals approach the ground station, it has to overcome buildings blocking the direct access to the ground station. Therefore, satellites commonly use redundant signal strength to ensure constant and continuous signal transmission, resulting in excess energy consumption, challenging the limited power capacity generated by solar energy or the fixed amount of fuel. This research proposes LTSM, an artificial recurrent neural network technology that provides a time-dependent prediction of the expected attenuation level due to rain and fog and the signal strength that remained after crossing physical obstacles surrounding the ground station. The satellite transmitter is calibrated accordingly. The satellite outgoing signal strength is based on the predicted signal strength to ensure it will remain strong enough for the ground station to process it. The instant calibration eliminates the excess use of energy resulting in energy savings.

Keywords

Satellite Communication; Signal Propagation; Rain Attenuation; Urban area ground station; SNR, ITU-R; LSTM, Neural network

Subject

MATHEMATICS & COMPUTER SCIENCE, Information Technology & Data Management

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