Deep Learning-Aided Signal Enumeration for Lens Antenna Array
This work investigates a data-driven approach to detect the number of incoming signals for a lens antenna array (LAA). First, the energy-focusing property of an electromagnetic (EM) lens is utilized to generate an input spectrum, which can be used to enumerate both the multipath and independent sign...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9963532/ |
| _version_ | 1811214256616505344 |
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| author | Dai Trong Hoang Kyungchun Lee |
| author_facet | Dai Trong Hoang Kyungchun Lee |
| author_sort | Dai Trong Hoang |
| collection | DOAJ |
| description | This work investigates a data-driven approach to detect the number of incoming signals for a lens antenna array (LAA). First, the energy-focusing property of an electromagnetic (EM) lens is utilized to generate an input spectrum, which can be used to enumerate both the multipath and independent signals. Next, we present the deep learning (DL)-assisted sharp peak recognition method referred to as the power spectrum-based convolutional neural network (PSCNet). Unlike classical techniques, such as constant false alarm rate (CFAR) detection, this data-driven detector can count received signals adaptively based on the LAA power spectrum without requiring any initial configurations. In addition, the PSCNet outperforms other state-of-the-art subspace-based detectors, even under challenging conditions, such as a low signal-to-noise ratio (SNR), a small observation size, and angular ambiguity. For the training phase, we propose a pretrained-model reusing strategy and an input pre-processing approach referred to as the power spectrum shortening (PSS) to alleviate the training burden and achieve lower complexity compared to fully retraining all isolated networks. The simulation results demonstrate that our proposed sharp peak-recognition algorithm not only accomplishes the improved signal enumeration performance but also requires lower computational resources than other subspace-based approaches. |
| first_indexed | 2024-04-12T06:01:23Z |
| format | Article |
| id | doaj.art-39a1a5667b4044bb8e0e5a9a8e127740 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T06:01:23Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-39a1a5667b4044bb8e0e5a9a8e1277402022-12-22T03:45:02ZengIEEEIEEE Access2169-35362022-01-011012383512384610.1109/ACCESS.2022.32246089963532Deep Learning-Aided Signal Enumeration for Lens Antenna ArrayDai Trong Hoang0https://orcid.org/0000-0001-7501-611XKyungchun Lee1https://orcid.org/0000-0002-4070-549XDepartment of Electrical and Information Engineering, Research Center for Electrical and Information Technology, Seoul National University of Science and Technology, Seoul, South KoreaDepartment of Electrical and Information Engineering, Research Center for Electrical and Information Technology, Seoul National University of Science and Technology, Seoul, South KoreaThis work investigates a data-driven approach to detect the number of incoming signals for a lens antenna array (LAA). First, the energy-focusing property of an electromagnetic (EM) lens is utilized to generate an input spectrum, which can be used to enumerate both the multipath and independent signals. Next, we present the deep learning (DL)-assisted sharp peak recognition method referred to as the power spectrum-based convolutional neural network (PSCNet). Unlike classical techniques, such as constant false alarm rate (CFAR) detection, this data-driven detector can count received signals adaptively based on the LAA power spectrum without requiring any initial configurations. In addition, the PSCNet outperforms other state-of-the-art subspace-based detectors, even under challenging conditions, such as a low signal-to-noise ratio (SNR), a small observation size, and angular ambiguity. For the training phase, we propose a pretrained-model reusing strategy and an input pre-processing approach referred to as the power spectrum shortening (PSS) to alleviate the training burden and achieve lower complexity compared to fully retraining all isolated networks. The simulation results demonstrate that our proposed sharp peak-recognition algorithm not only accomplishes the improved signal enumeration performance but also requires lower computational resources than other subspace-based approaches.https://ieeexplore.ieee.org/document/9963532/Signal enumerationlens antenna array (LAA)convolutional neural network (CNN)signal power spectrum |
| spellingShingle | Dai Trong Hoang Kyungchun Lee Deep Learning-Aided Signal Enumeration for Lens Antenna Array IEEE Access Signal enumeration lens antenna array (LAA) convolutional neural network (CNN) signal power spectrum |
| title | Deep Learning-Aided Signal Enumeration for Lens Antenna Array |
| title_full | Deep Learning-Aided Signal Enumeration for Lens Antenna Array |
| title_fullStr | Deep Learning-Aided Signal Enumeration for Lens Antenna Array |
| title_full_unstemmed | Deep Learning-Aided Signal Enumeration for Lens Antenna Array |
| title_short | Deep Learning-Aided Signal Enumeration for Lens Antenna Array |
| title_sort | deep learning aided signal enumeration for lens antenna array |
| topic | Signal enumeration lens antenna array (LAA) convolutional neural network (CNN) signal power spectrum |
| url | https://ieeexplore.ieee.org/document/9963532/ |
| work_keys_str_mv | AT daitronghoang deeplearningaidedsignalenumerationforlensantennaarray AT kyungchunlee deeplearningaidedsignalenumerationforlensantennaarray |