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Implementation of the Digital QS-SVM-Based Beamformer on an FPGA Platform

To address practical challenges in establishing and maintaining robust wireless connectivity such as multi-path effects, low latency, size reduction, and high data rate, we have deployed the digital beamformer, as a spatial filter, by using the hybrid antenna array at an operating frequency of 10 GH...

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Autores principales: Komeylian, Somayeh, Paolini, Christopher
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9919919/
https://www.ncbi.nlm.nih.gov/pubmed/36772781
http://dx.doi.org/10.3390/s23031742
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author Komeylian, Somayeh
Paolini, Christopher
author_facet Komeylian, Somayeh
Paolini, Christopher
author_sort Komeylian, Somayeh
collection PubMed
description To address practical challenges in establishing and maintaining robust wireless connectivity such as multi-path effects, low latency, size reduction, and high data rate, we have deployed the digital beamformer, as a spatial filter, by using the hybrid antenna array at an operating frequency of 10 GHz. The proposed digital beamformer utilizes a combination of the two well-established beamforming techniques of minimum variance distortionless response (MVDR) and linearly constrained minimum variance (LCMV). In this case, the MVDR beamforming method updates weight vectors on the FPGA board, while the LCMV beamforming technique performs nullsteering in directions of interference signals in the real environment. The most well-established machine learning technique of support vector machine (SVM) for the Direction of Arrival (DoA) estimation is limited to problems with linearly-separable datasets. To overcome the aforementioned constraint, the quadratic surface support vector machine (QS-SVM) classifier with a small regularizer has been used in the proposed beamformer for the DoA estimation in addition to the two beamforming techniques of LCMV and MVDR. In this work, we have assumed that five hybrid array antennas and three sources are available, at which one of the sources transmits the signal of interest. The QS-SVM-based beamformer has been deployed on the FPGA board for spatially filtering two signals from undesired directions and passing only one of the signals from the desired direction. The simulation results have verified the strong performance of the QS-SVM-based beamformer in suppressing interference signals, which are accompanied by placing deep nulls with powers less than −10 dB in directions of interference signals, and transferring the desired signal. Furthermore, we have verified that the performance of the QS-SVM-based beamformer yields other advantages including average latency time in the order of milliseconds, performance efficiency of more than 90%, and throughput of nearly 100%.
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spelling pubmed-99199192023-02-12 Implementation of the Digital QS-SVM-Based Beamformer on an FPGA Platform Komeylian, Somayeh Paolini, Christopher Sensors (Basel) Article To address practical challenges in establishing and maintaining robust wireless connectivity such as multi-path effects, low latency, size reduction, and high data rate, we have deployed the digital beamformer, as a spatial filter, by using the hybrid antenna array at an operating frequency of 10 GHz. The proposed digital beamformer utilizes a combination of the two well-established beamforming techniques of minimum variance distortionless response (MVDR) and linearly constrained minimum variance (LCMV). In this case, the MVDR beamforming method updates weight vectors on the FPGA board, while the LCMV beamforming technique performs nullsteering in directions of interference signals in the real environment. The most well-established machine learning technique of support vector machine (SVM) for the Direction of Arrival (DoA) estimation is limited to problems with linearly-separable datasets. To overcome the aforementioned constraint, the quadratic surface support vector machine (QS-SVM) classifier with a small regularizer has been used in the proposed beamformer for the DoA estimation in addition to the two beamforming techniques of LCMV and MVDR. In this work, we have assumed that five hybrid array antennas and three sources are available, at which one of the sources transmits the signal of interest. The QS-SVM-based beamformer has been deployed on the FPGA board for spatially filtering two signals from undesired directions and passing only one of the signals from the desired direction. The simulation results have verified the strong performance of the QS-SVM-based beamformer in suppressing interference signals, which are accompanied by placing deep nulls with powers less than −10 dB in directions of interference signals, and transferring the desired signal. Furthermore, we have verified that the performance of the QS-SVM-based beamformer yields other advantages including average latency time in the order of milliseconds, performance efficiency of more than 90%, and throughput of nearly 100%. MDPI 2023-02-03 /pmc/articles/PMC9919919/ /pubmed/36772781 http://dx.doi.org/10.3390/s23031742 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Komeylian, Somayeh
Paolini, Christopher
Implementation of the Digital QS-SVM-Based Beamformer on an FPGA Platform
title Implementation of the Digital QS-SVM-Based Beamformer on an FPGA Platform
title_full Implementation of the Digital QS-SVM-Based Beamformer on an FPGA Platform
title_fullStr Implementation of the Digital QS-SVM-Based Beamformer on an FPGA Platform
title_full_unstemmed Implementation of the Digital QS-SVM-Based Beamformer on an FPGA Platform
title_short Implementation of the Digital QS-SVM-Based Beamformer on an FPGA Platform
title_sort implementation of the digital qs-svm-based beamformer on an fpga platform
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9919919/
https://www.ncbi.nlm.nih.gov/pubmed/36772781
http://dx.doi.org/10.3390/s23031742
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