Comparative Analysis of Machine Learning Algorithms for Antenna Alignments

In the rapidly evolving field of radio frequency (RF) engineering, precise antenna alignment remains a critical challenge, directly influencing communication performance and reliability. This study presents a comprehensive comparative analysis of three advanced machine learning models—Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Multilayer Perceptron (MLP)—to predict antenna alignments based on S-parameter measurements. By addressing the complexities of both distance and angle predictions, our approach demonstrates the efficacy of these neural network architectures in automating alignment tasks traditionally reliant on manual intervention. The results reveal that the MLP and LSTM models excel in distance prediction, achieving a remarkable accuracy of 97%, while the CNN model outperforms in angle prediction with an accuracy of 86%. This is the first work to propose a data-driven antenna alignment method based solely on S-parameter analysis, offering a cost-effective and scalable alternative to complex imaging or optical systems. The approach significantly reduces setup complexity while maintaining high prediction accuracy, making it well-suited for deployment in practical RF systems. The study further highlights the strengths and limitations of each model, offering valuable insights into their applicability for RF engineering tasks. By providing a robust machine learning framework, this research contributes significantly to advancing automated alignment processes, reducing dependency on manual methods, and paving the way for future innovations in RF systems. These findings have far-reaching implications for the development of intelligent and scalable solutions in wireless communication systems.