RT Journal Article
T1 Learning to shape beams: using a neural network to control a beamforming antenna
A1 Fernández-Rodríguez, Jose David
A1 García Aguilar, Iván
A1 Luque-Baena, Rafael Marcos
A1 López-Rubio, Ezequiel
A1 Baena-Molina, Marcos
A1 Valenzuela-Valdés, Juan Francisco
K1 Redes neuronales artificiales
K1 Aprendizaje automático (Inteligencia artificial)
AB The field of reconfigurable intelligent surfaces (RIS) has gained significant traction in recent years in the wireless communications domain, owing to the ability to dynamically reconfigure surfaces to change their electromagnetic radiance patterns in real-time. In this work, we propose utilizing a novel deep learning model that innovatively employs only the parameters of each signal or beam as input, eliminating the need for the entire one-dimensional signal or its diffusion map (two-dimensional information). This approach enhances efficiency and reduces the overall complexity of the model, drastically reducing network size and enabling its implementation on low-cost devices. Furthermore, to enhance training effectiveness, the learning model attempts to estimate the discrete cosine transform applied to the output matrix rather than the raw matrix, significantly improving the achieved accuracy. This scheme is validated on a 1-bit programmable metasurface of size 1010, achieving an accuracy close to 95% using a K-fold methodology with K=10.
PB Elsevier
YR 2025
FD 2025
LK https://hdl.handle.net/10630/39486
UL https://hdl.handle.net/10630/39486
LA eng
NO Jose David Fernández-Rodríguez, Iván García-Aguilar, Rafael Marcos Luque-Baena, Ezequiel López-Rubio, Marcos Baena-Molina, Juan Francisco Valenzuela-Valdés, Learning to shape beams: Using a neural network to control a beamforming antenna, Computer Networks, Volume 270, 2025, 111544, ISSN 1389-1286, https://doi.org/10.1016/j.comnet.2025.111544.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 24 ene 2026