# Artificial Intelligence-Empowered Doppler Weather Profile for Low-Earth-Orbit Satellites  
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1 Artificial Intelligence Applications Laboratory, School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, QLD 4300, Australia; [EMAIL_ADDRESS]. space monitoring applications. The compact tracking RADAR system used to monitor celestial bodies in Low Earth Orbit was developed individually [3]. LEO satellites have shown great promise as a foundational infrastructure for advanced global wireless networks, offering increased data rates and improved connectivity. Given their reduced transmission delay and adaptable service options, LEO satellites are typically favoured for enhancing and expanding the capabilities of future terrestrial communication networks [4]. Nevertheless, the obstacles presented by prompt transmission rates, negligible latency, and widespread connectivity persist [5]. Furthermore, as a result of advancements in diverse applications, these criteria must be upheld even in challenging conditions, such as environments with high levels of mobility. There are often substantial channel fluctuations in these settings, which result in decreased communication efficiency [6]. LEO satellites maintain a velocity significantly higher than the rotation speed of the Earth to prevent reentry into its atmosphere due to gravitational forces. The observed phenomenon leads to a significant change in the frequency offset of the signal being transmitted, commonly referred to as the Doppler shift, which varies periodically within the satellite's timeframe. The Doppler rate symbolises the velocity at which these fluctuations take place, determined by the rate of Doppler shift changes over a specified period of time. Regrettably, this presents significant obstacles to the functionality of LoRa receivers at the physical layer [7]. LoRa modulation is a modulation scheme derived from the chirp-spread-spectrum (CSS) modulation technique [8]. Traditional methods for Doppler shift estimation have been proposed in previous literature, such as the channel autocorrelation coefficient function (ACF) [9], phase difference tracking [10], cyclic prefix [11], and maximum-likelihood estimation [12]. In [13], a linear frequency modulation (LFM) preamble-based Doppler estimation method was introduced. While these methods are effective, they require extensive sampling and can be challenging to implement packet-by-packet. Additionally, they are sensitive to additive white Gaussian noise (AWGN), reducing accuracy in low SNR environments. Authors in [14] have presented a novel Doppler detection framework designed for blind opportunistic navigation with the utilisation of signals from LEO satellites. Artificial intelligence (AI) offers us strategies to address these challenges, thanks to its advancements and use in communications. There has been a growing interest in deep learning (DL) algorithms for addressing intricate challenges across various communication sectors. However, limited research has been conducted on the estimation of Doppler effects for LEO satellites using deep learning techniques. The use of deep learning algorithms for predictive channel modelling and advanced multibeam precoding techniques in complex satellite communication (SatCom) environments was performed by [15]. The analysis of channel forecasting and the hybrid beamforming in an LEO satellite's massive Multiple-Input Multiple-Output (MIMO) system was researched by [16]. Authors in [17] used advanced deep learning techniques to detect overlaid control signals in LEO Multiple-Input Multiple-Output (LEO-MIMO) systems. In artificial intelligence technology, the deep neural network (DNN) stands out for processing vast amounts of data and tackling intricate nonlinear challenges, thanks to its data-driven nature. Specifically in the time-series issues, the gated recurrent unit (GRU), or recurrent neural network (RNN), demonstrates impressive predictive abilities.