  Citation: Domb Alon, M.M.; Leshem, G. Satellite to Ground Station, Attenuation Prediction for 2.4–72 GHz Using LTSM, an Artificial Recurrent Neural Network Technology. Electronics 2022, 11, 541. https://doi.org/10.3390/ electronics11040541 Academic Editor: Manuel Arrebola Received: 30 November 2021 Accepted: 6 February 2022 Published: 11 February 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). electronics Article Satellite to Ground Station, Attenuation Prediction for 2.4–72 GHz Using LTSM, an Artificial Recurrent Neural Network Technology Menachem Manis Domb Alon * and Guy Leshem Computer Science Department, Ashkelon Academy College (AAC), Ashkelon 52653, Israel; assignments4md@gmail.com * Correspondence: dombmnc@edu.aac.ac.il Abstract: Satellite communication links suffer from arbitrary weather phenomena such as clouds, rain, snow, fog, and dust. Furthermore, when signals approach the ground station, they have to overcome buildings blocking the direct access to the ground station. This work proposes a model to predict the remaining signal strength for the next timeframe after deducting the attenuation and disruption impact caused during its propagation from the satellite to the ground station. The proposed model can be adjusted to comply with any geographic region and a broad spectrum of frequencies. We employ LTSM, an artificial recurrent neural network technology, providing a time- dependent prediction. We can instantly calibrate the satellite outgoing signal strength to overcome the predicted attenuation, resulting in satellite energy saving using this prediction. Keywords: satellite communication; signal propagation; rain attenuation; urban area ground station; SNR; ITU-R; LSTM; neural network 1. Introduction This paper extends our conference paper significantly [1]. Cellular wireless infrastruc- ture has served as the standard data transmission system in recent decades. However, the expected growing demand for internet services, high speed, wide bandwidth, and availabil- ity requires a significant change in communication infrastructure, deployment, technology, and management. Free-space, high-speed communications, employing numerous satellites and related ground stations, seem to reasonably fulfill this extreme demand. High frequencies are considerably affected by rainfall that attenuates the propagating signal at microwave and millimeter-wave frequencies. Therefore, mitigating rain attenua- tion is required to ensure the quality of microwave and millimeter-wave links. Dynamic attenuation mitigation methods can be implemented alongside attenuation prediction models. Calculating the impact of rain on satellite communication relies on attenuation data collected for each ground station and transmission frequency. These data enable us to estimate the expected attenuation per location with standard prediction methods. The availability of satellite beacon measurements has provided a database for validating and refining the prediction models. The predicting techniques recommended by the ITU-R assume that an equivalent cell of uniform rainfall rate can model the non-uniform rainfall along the propagation path. An identical, cylindrical cell of constant rain can intercept the link at any position with equal probability. A practical path length is calculated as the average length of the intersection between the cell and the propagation path. As a result, the effective path length is always smaller than the actual path length. In the slant path prediction method, horizontal and vertical reduction factors consider the spatial and temporal variability of the rain field. A satellite cruises in a specific orbit while receiving and sending signals to and from a ground station. The ground station is located in a fixed location. Figure 1 depicts the Electronics 2022, 11, 541. https://doi.org/10.3390/electronics11040541 https://www.mdpi.com/journal/electronics