Beam Prediction Based on Large Language Models

Beam Prediction Based on Large Language Models aims to develop a smart system that predicts the optimal beam for satellite antenna steering based on user input data. This system integrates machine learning models, such as Random Forest (RF), XGBoost, SVM, and hybrid deep learning models like CNN-Hybrid and LSTM + Dense Hybrid Neural Network, to analyze historical user location and beam data, providing accurate predictions on the most suitable beam direction. The CNN-Hybrid model extracts high-level features, while the LSTM + Dense Hybrid Neural Network captures sequential dependencies, enhancing the model's ability to make predictions over time. A Flask-based web application allows users to upload data, select algorithms, and receive real-time feedback. By leveraging large language models (LLMs) and probabilistic descriptions, the system generates intuitive explanations of beam predictions, offering users clear insights into the likelihood of optimal performance. This project serves as a proof of concept for applying LLMs in beam prediction, providing a scalable and efficient solution for satellite management and optimization, with an accessible user interface for satellite communications.

Keywords: Beam Prediction, Large Language Models, Satellite Antenna, Machine Learning, Random Forest, XGBoost, SVM, CNN-Hybrid Model, LSTM + Dense Hybrid Neural Network, Flask, Data Upload, Prediction Accuracy, Probabilistic Description, User Interaction, Satellite Communications, System Optimization.