# Artificial Intelligence-Empowered Doppler Weather Profile for Low-Earth-Orbit Satellites
The study [18] focuses on using CNN-assisted techniques for enhancing channel and carrier frequency offset estimation in high-altitude platform station (HAPS)-LEO communication links. Initially, efforts are directed towards alleviating channel distortion and carrier frequency offset, including residual Doppler effects, through a novel estimator based on Convolutional Neural Networks. Subsequently, the data transmission rate is improved by enhancing the spectral efficiency with multiple access methods. The heightened focus on the DL benefit is attributed to their superior capability to extract several features that conventional methods struggle to achieve. This paper [19] introduces a CNN-based model for estimating Doppler shifts in LEO SatCom systems. The model uses deep learning to extract channel features and reduce errors in predicting Doppler shifts. Numerical resultsdemonstrate the model's accurate estimation capabilities. The authors' [20] architecture proposed a deep neural network to estimate timing deviation and frequency offset in high-mobility multicarrier transmission using CNN for extracting hidden signal features. To leverage the benefits of deep learning technology, we have developed a sophisticated model for low-Earth-orbit (LEO) constellation satellites. Our model evaluates the connectivity between satellites and ground stations, calculates latency, and incorporates a hybrid and computationally efficient model integrating Iterative Input Selection and Long Short-Term Memory Network (IIS-LSTM). This innovative model aims to forecast the Doppler profile for LEO satellites. Our comprehensive examination takes into account the Doppler shift and Doppler rate to evaluate their effect on packet loss. Doppler shift, otherwise referred to as static Doppler, adversely impacts communication effectiveness when at shallow elevation angles and the furthest reach of the link. The Doppler rate, or dynamic Doppler, can cause loss of packets at high angles of elevation, especially in the close proximity of the satellite to ground devices, which ultimately leads to a reduction in the link distance. Additionally, the involvement of deep learning (DL) is a rapidly evolving area of study. There has been a noticeable increase in interest and dedication toward exploring low-power and long-range communication networks, such as LoRa, for sensor networks and the Internet of Things. LoRa provides an economical and energy-conserving solution for communication. It forms the basis for terrestrial Low-Power Wide-Area Network (LPWAN) technology, LoRaWAN. The study conducted by authors [21] explores detecting LoRa signals from a LEO satellite, posing challenges due to Doppler effects and interference. Research by [22] showed the promising results of LoRa technology for satellite IoT, particularly in LEO. Testing demonstrated significant resistance to the Doppler effect, allowing for the utilisation of LoRa technology in orbits exceeding 550 km without any limitations. Below 550 km, the Doppler rate may disrupt communication sessions, with a minute reduction in LEO at 200 km. Therefore, we believe it is imperative to consider the impact of performance communication constraints. These can be the signal-to-noise ratio, bandwidth allocation, and the carrier frequency of utilising LoRa modulation within the framework of the Doppler effect. This can lead to potential sway over the frequency variations, packet loss, and the subsequent disruption of communication pathways. This will play a role in determining the receiver's susceptibility to this phenomenon. LoRa technology and LEO satellites have also been examined in previous studies, particularly in terms of link budget examination and scalability within networks for enabling uplink connectivity. Studies such as [23] find that link budget calculations suggest that communication up to 2302 km is possible.