Work

Direction-of-Arrival (DOA) Estimation Based on Real Field Measurements and Modified Linear Regression

Year: 2024

Type: conference-paper

Abstract: 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.

Source: Jornadas de Ingeniería Eléctrica y Electrónica (JIEE)

DOI: 10.3390/engproc2024077011

Authors: Luis Antonio Flores, Ismael Lomas, Lenin Guachalá, Pablo Lupera-Morillo, Robin Álvarez, Ricardo Llugsi

Institution: National Polytechnic School

Language: English

Topic: Direction-of-Arrival Estimation Techniques

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

OpenAlex ID: W4404488085

FWCI: 2.954

Cites: 21

Cited by: 10

Related to: 10