Deep Learning-based Beamforming Approach Incorporating Linear Antenna Arrays

• Autorzy: Bhalke Daulappa , Paikrao Pavan D. , Anguera Jaume

Identyfikatory

DOI

10.26636/jtit.2024.2.1530

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

adaptive beamforming; antenna arrays; convolutional neural network

• Wydawca: Instytut Łączności - Państwowy Instytut Badawczy
• Czasopismo: Journal of Telecommunications and Information Technology
• Rocznik: 2024
• Tom: nr 2
• Strony: 66--70
• Opis fizyczny: Bibliogr. 19 poz., rys., tab.

Twórcy

autor

Bhalke Daulappa

• Patil Institute of Technology, Pimpri, Pune, India

• https://orcid.org/0000-0002-8453-7280

autor

Paikrao Pavan D.

• Patil Institute of Technology, Pimpri, Pune, India

• https://orcid.org/0000-0003-2419-7189

autor

Anguera Jaume

• University at Ramon Llull, Barcelona, Spain

• https://orcid.org/0000-0002-3364-342X

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