Direction-of-Arrival (DOA) Estimation Based on Real Field Measurements and Modified Linear Regression
Luis Antonio Flores, Ismael Lomas, Lenin Guachalá, Pablo Lupera, Robin Álvarez, Ricardo Llugsi · 2024
This study applied modified linear regression in machine learning (ML) to predict the direction of arrival (DoA) in cellular networks using field measurements and radiofrequency parameters. Models were developed from base station data, with preprocessing for pattern identification and formula adjustments to improve the accuracy across angle ranges. Machine learning, tested here as an additional method to traditional techniques, achieved a root mean square error (RMSE) of 3.63 to 17.93, demonstrating enhanced adaptability. While requiring substantial data and computational resources, this approach highlights machine learning’s potential as a valuable tool for DoA estimation in cellular networks.