Volume 18, Issue 2 (June 2022)                   IJEEE 2022, 18(2): 92-104 | Back to browse issues page

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Ramezanpour P, Aghababaie M, Mosavi M R, M. de Andrés D. Multi-Stage Beamforming Using DNNs. IJEEE 2022; 18 (2) :92-104
URL: http://ijeee.iust.ac.ir/article-1-2266-en.html
Multi-Stage Beamforming Using DNNs
P. Ramezanpour , M. Aghababaie , M. R. Mosavi , D. M. de Andrés
Abstract:   (1733 Views)
Through beamforming, the desired signal is estimated by calculating the weighted sum of the input signals of an array of antenna elements. In the classical beamforming methods, computing the optimal weight vector requires prior knowledge on the direction of arrival (DoA) of the desired signal sources. However, in practice, the DoA of the signal of interest is unknown. In this paper, we introduce two different deep-neural-network-based beamformers which can estimate the signal of interest while suppressing noise and interferences in two/three stages when the DoAs are unknown. Employing deep neural networks (DNNs) such as convolutional neural networks (CNNs) and bidirectional long short-term memory (bi-LSTM) networks enables the proposed method to have better performance than existing methods. In most cases, the output signal to interference and noise ratio (SINR) of the proposed beamformer is more than 10dB higher than the output SINR of the classical beamformers.
Keywords: Adaptive Digital Beamforming , Bidirectional Long Short-Term Memory , Convolutional Neural Network
Full-Text [PDF 4266 kb]   (1064 Downloads)    
Type of Study: Research Paper | Subject: Signal Processing
Received: 2021/08/27 | Revised: 2024/05/13 | Accepted: 2021/12/17
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© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.