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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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/3/1742 |
| _version_ | 1797623147194744832 |
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| author | Somayeh Komeylian Christopher Paolini |
| author_facet | Somayeh Komeylian Christopher Paolini |
| author_sort | Somayeh Komeylian |
| collection | DOAJ |
| 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%. |
| first_indexed | 2024-03-11T09:24:30Z |
| format | Article |
| id | doaj.art-d056880c68244dc9a524c1c089eecd9e |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T09:24:30Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-d056880c68244dc9a524c1c089eecd9e2023-11-16T18:05:30ZengMDPI AGSensors1424-82202023-02-01233174210.3390/s23031742Implementation of the Digital QS-SVM-Based Beamformer on an FPGA PlatformSomayeh Komeylian0Christopher Paolini1Electrical and Computer Engineering, San Diego State University, San Diego, CA 92182, USAElectrical and Computer Engineering, San Diego State University, San Diego, CA 92182, USATo 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%.https://www.mdpi.com/1424-8220/23/3/1742digital beamformingsupport vector machineminimum variance distortionless responselinearly constrained minimum variancedirection of arrival estimationFPGA |
| spellingShingle | Somayeh Komeylian Christopher Paolini Implementation of the Digital QS-SVM-Based Beamformer on an FPGA Platform Sensors digital beamforming support vector machine minimum variance distortionless response linearly constrained minimum variance direction of arrival estimation FPGA |
| 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 | digital beamforming support vector machine minimum variance distortionless response linearly constrained minimum variance direction of arrival estimation FPGA |
| url | https://www.mdpi.com/1424-8220/23/3/1742 |
| work_keys_str_mv | AT somayehkomeylian implementationofthedigitalqssvmbasedbeamformeronanfpgaplatform AT christopherpaolini implementationofthedigitalqssvmbasedbeamformeronanfpgaplatform |