Tytuł:

Deep Learning-based Beamforming Approach Incorporating Linear Antenna Arrays, Journal of Telecommunications and Information Technology, 2024, nr 2

Tytuł publikacji grupowej:

2024, nr 2, JTIT-artykuły

Autor:

Bhalke, Daulappa ; Paikrao, Pavan D. ; Anguera, Jaume

Temat i słowa kluczowe:

adaptive beamforming ; antenna arrays ; convolutional neural network

Abstrakt:

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.

Numer:

2

Wydawca:

National Institute of Telecommunications

Data wydania:

2024

Typ zasobu:

artykuł

Identyfikator zasobu:

ISSN 1509-4553, on-line: ISSN 1899-8852

DOI:

10.26636/jtit.2024.2.1530

ISSN:

1509-4553

eISSN:

1899-8852

Źródło:

Journal of Telecommunications and Information Technology

Język:

ang

Licencja:

CC BY 4.0

Właściciel praw:

Instytut Łączności - Państwowy Instytut Badawczy