# Artificial Intelligence-Empowered Doppler Weather Profile for Low-Earth-Orbit Satellites
## 4 Conclusions  
Efficient protocols are essential for ensuring reliability, smooth communication, and energy efficiency. Through cutting-edge AI architectures and emerging technologies, robust satellite communication networks are feasible. This study comprehensively examines the operational efficiency of low-Earth-orbit satellites concerning the Doppler effect, utilising state-of-the-art artificial intelligence techniques. The performance of LoRa and ground satellite connectivity has been thoroughly investigated, particularly under the influence of a significant Doppler weather effect. The hybrid AI model developed prioritises extended transmission distances, even at the cost of bandwidth. This study estimates frequency offset, uses AI to project the Doppler profile for the LEO satellite and weather attenuation, coordinates the project collectively for the uplink and downlink, enhances computational efficiency and overall performance, and assesses against other prominent modulation schemes found in prevalent Internet of Things (IoT) standards such as Doppler effect in long-range modulation. In LEO satellite communications, the implementation of effective frequency Doppler shift (FDS) compensation techniques is vital to ensure optimal data transmission rates and minimal error occurrences. The analysis has focused on several key factors that differentiate the impact of the static Doppler from the dynamic Doppler. The challenges posed by the Doppler effect when establishing LoRa ground satellite connectivity have also been addressed. The meticulous evaluation of the traditional systems with the current industry benchmarks helps us to refine modulation schemes, especially in the long range (LoRa), leading to a significantly improved and more effectively operated AI model. With training and optimisation of the model developed in conjunction with SNR values, we can enhance estimation accuracy and reduce computational complexity. It has been observed that various factors, such as spread factor, frequency offset, and bandwidth have a significant effect on the uplink. Furthermore, the satellite's height of orbit and packet payload also have a moderate effect on performance. These conclusions can help in determining the most suitable parameters for SatCom to understand the loss of packets. Our study highlights the benefits, challenges, and delicate equilibrium in adjusting key parameters related to communication. The presence of both Doppler rate and shift effects could interfere with the SatCom link, potentially resulting in the loss of packets. Static Doppler is challenging at elevation angles that are lower. However, dynamic Doppler has a more significant effect at elevation angles that are higher, especially near the end device. These effects can result in coverage gaps within the impression of the satellite, decreasing the area of coverage, where it is impactful and reduces the available time for a stable connection. As a result, even if a satellite is within sight, other systems may encounter difficulties establishing communication due to the influence of the Doppler effect. The integrated AI model, known as the Iterative Input Selection-Long Short-Term Memory Network (IIS-LSTM), forecasts Doppler weather profiles for LEO satellites. The IIS algorithm is utilised for selecting pertinent input variables within the model, whereas the LSTM algorithm is responsible for acquiring knowledge and making predictive assessments of patterns. The feature extractor is trained using the normalised mean squared error criterion to accommodate the wide range of Doppler shift values present in LEO communication channels. The feature extractor has been trained utilising the normalised mean squared error criterion in order to adapt to the diverse range of values of Doppler shift encountered within LEO. The machine learning model utilising XG Boost attains an impressive accuracy rate of 87%, with the 55 th trial emerging as the most optimal out of a total of 100 trials. Additionally, the learning rate employed in this analysis is 0.102.