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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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Deep Learning of the Sparse Array Configurations in Optimum Beamforming
The article examines neural network learning of the sparse array configurations in optimum beamforming. Unlike iterative greedy, convex, and global optimization methods for optimum array design, deep learning enables fast reconfigurations of the sparse array in rapid dynamic propagation environments. We employ three different convolutional neural network architectures with varying simplification and parameter counts. The network is trained to select M out of N uniformly spaced antennas to achieve maximum signal-to-interference and noise ratio (SINR) beamforming. Different values of M are considered, including N = 2 M, for studying network performance under an increased number of subarray classes. We consider one desired source and one interference of arbitrary angle, and delineate the learning results for the two cases where the network is trained with the desired source assuming fixed and varying angles. We discuss the benefits of reducing the number of possible configurations due to sidelobe level reductions. It is also shown that the network performance significantly improves with data augmentations and by removing redundant array configurations which produce the same SINR.
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- PAR ID:
- 10615873
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE Transactions on Aerospace and Electronic Systems
- Volume:
- 61
- Issue:
- 3
- ISSN:
- 0018-9251
- Page Range / eLocation ID:
- 5718 to 5730
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Journal Article:
- https://doi.org/10.1109/TAES.2024.3523276
