Deep Learning-based Beamforming Approach Incorporating Linear Antenna Arrays

This research delves into exploring machine learning and deep learning techniques relied upon in antenna design processes. First, the general concepts of machine learning and deep learning are introduced. Then, the focus shifts to various antenna applications, such as those relying on millimeter wav...

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Bibliographic Details
Published inJournal of Telecommunications and Information Technology Vol. 2; no. 2; pp. 66 - 70
Main Authors Bhalke, Daulappa, Paikrao, Pavan, Anguera, Jaume
Format Journal Article
LanguageEnglish
Published Warsaw Instytut Lacznosci - Panstwowy Instytut Badawczy (National Institute of Telecommunications) 22.05.2024
National Institute of Telecommunications
Subjects
adaptive beamforming
Algorithms
Antenna arrays
Antenna design
Antennas
Arrays
Beamforming
Communication
Computing time
convolutional neural network
Correlation analysis
Deep learning
Design
Eigenvalues
Machine learning
Millimeter waves
Neural networks
Optimization techniques
Radiation
Receivers & amplifiers
Signal processing
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Summary:This research delves into exploring machine learning and deep learning techniques relied upon in antenna design processes. First, the general concepts of machine learning and deep learning are introduced. Then, the focus shifts to various antenna applications, such as those relying on millimeter waves. The feasibility of employing antennas in this band is examined and compared with conventional methods, emphasizing the acceleration of the antenna design process, reduction in the number of simulations, and improved computational efficiency. The proposed method is a low-complexity approach which avoids the need for eigenvalue decomposition, the procedure for computing the entire matrix inversion, as well as incorporating signal and interference correlation matrices in the weight optimization process. The experimental results clearly demonstrate that the proposed method outperforms the compared beamformers by achieving a better signal-to-interference ratio.
ISSN:1509-4553
1899-8852
DOI:10.26636/jtit.2024.2.1530