Title:

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

Group publication title:

2024, nr 2, JTIT-artykuły

Creator:

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

Subject and Keywords:

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.

Number:

2

Publisher:

National Institute of Telecommunications

Date:

2024

Resource Type:

artykuł

Resource Identifier:

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

DOI:

10.26636/jtit.2024.2.1530

ISSN:

1509-4553

eISSN:

1899-8852

Source:

Journal of Telecommunications and Information Technology

Language:

ang

License:

CC BY 4.0

rights owner:

Instytut Łączności - Państwowy Instytut Badawczy