Recurrent Deep Learning for Beam Pattern Synthesis in Optimized Antenna Arrays

Recurrent Deep Learning for Beam Pattern Synthesis in Optimized Antenna Arrays

This work proposes and describes a deep learning-based approach utilizing recurrent neural networks (RNNs) for beam pattern synthesis considering uniform linear arrays. In this particular case, the deep neural network (DNN) learns from previously optimized radiation patterns as inputs and generates...

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Bibliographic Details
Main Authors: Armando Arce, Fernando Arce, Enrique Stevens-Navarro, Ulises Pineda-Rico, Marco Cardenas-Juarez, Abel Garcia-Barrientos
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Applied Sciences
Subjects:
deep learning
neural networks
antenna arrays
antenna radiation patterns
synthesis
optimization
Online Access:https://www.mdpi.com/2076-3417/15/1/204
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