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A Direction of Arrival Machine Learning Approach for Beamforming in 6G

Authors:
Anabel Reyes Carballeira
Abel Rodriguez Medel
Jose Marcos Camara Brito

Keywords: Beamforming; Direction of Arrival; Machine Learning.

Abstract:
Beamforming (BF) appropriately weights the amplitude and phase of individual antenna signals to create narrowly focused radiation. This makes it possible to provide better coverage in an indoor environment and at the edge of a cell. To make the best use of this technology, it is important to know the location of the device to direct the antenna beam of the radio Base Station (BS). Consequently, the Direction of Arrival (DOA) method is becoming very crucial and essential in this time. This paper proposes a Machine Learning (ML) based approach for DOA by evaluating three models: Support Vector Classification (SVC), Decision Tree (DT) and Bagging Classifier (BC). These models are trained using a public database built from drone’s radio frequency signals. The proposed model significantly outperforms the techniques presented in previous work.

Pages: 54 to 59

Copyright: Copyright (c) IARIA, 2021

Publication date: July 18, 2021

Published in: conference

ISSN: 2308-4219

ISBN: 978-1-61208-878-5

Location: Nice, France

Dates: from July 18, 2021 to July 22, 2021