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Iranian Journal of Electrical and Electronic Engineering، جلد ۱۸، شماره ۲، صفحات ۲۲۶۶-۲۲۶۶
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چکیده فارسی مقاله
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عنوان انگلیسی Multi-Stage Beamforming Using DNNs
چکیده انگلیسی مقاله 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.
کلیدواژه‌های انگلیسی مقاله Adaptive Digital Beamforming, Bidirectional Long Short-Term Memory, Convolutional Neural Network.
نویسندگان مقاله | P. Ramezanpour
Department of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran.


| M. Aghababaie
Department of Electrical Engineering, Imam Khomeini Marine Science University, Mazandaran, Iran.


| M. R. Mosavi
Department of Electrical Engineering, Iran University of Science and Technology (IUST), Tehran, Iran.


| D. M. de Andrés
ETSI de Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain.
نشانی اینترنتی http://ijeee.iust.ac.ir/browse.php?a_code=A-10-78-28&slc_lang=en&sid=1
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کد مقاله (doi)
زبان مقاله منتشر شده en
موضوعات مقاله منتشر شده 5-Signal Processing
نوع مقاله منتشر شده Research Paper
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