Hybrid Quantum Convolutional Neural Networks for UWB Signal Classification
With the increasing requirements for location-based services for Internet of things (IoT) applications, ultrawideband (UWB) technology provides accurate indoor positioning capabilities. However, indoor environments contain various obstacles leading to significant signal propagation effects. This res...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10274707/ |
| _version_ | 1797655637413330944 |
|---|---|
| author | Seon-Geun Jeong Quang-Vinh Do Hae-Ji Hwang Mikio Hasegawa Hiroo Sekiya Won-Joo Hwang |
| author_facet | Seon-Geun Jeong Quang-Vinh Do Hae-Ji Hwang Mikio Hasegawa Hiroo Sekiya Won-Joo Hwang |
| author_sort | Seon-Geun Jeong |
| collection | DOAJ |
| description | With the increasing requirements for location-based services for Internet of things (IoT) applications, ultrawideband (UWB) technology provides accurate indoor positioning capabilities. However, indoor environments contain various obstacles leading to significant signal propagation effects. This results in errors in the time-of-arrival-based UWB positioning system. Specifically, a non-line-of-sight (NLOS) signal induces additional distance and position errors owing to the path delay compared to a line-of-sight (LOS) signal. Therefore, UWB signal classification is essential for improving positioning accuracy. Recently, various approaches have successfully classified UWB signals, including machine-learning-based methods such as convolutional neural networks (CNNs) and long short-term memory (LSTM). This study proposes a hybrid quantum CNN (HQCNN) inspired by a CNN for UWB signal classification. HQCNN employs a classical layer before a quantum embedding circuit and variational quantum circuits for the convolutional filter. These structures enable efficient training and implementation. We used UWB channel impulse response data to demonstrate the performance of the proposed algorithm and compared the benchmarks with HQCNN using the evaluation metrics. The results showed that the HQCNN outperformed the others. |
| first_indexed | 2024-03-11T17:17:22Z |
| format | Article |
| id | doaj.art-50345fb75fc04010b2536e74ca5e84c5 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-11T17:17:22Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-50345fb75fc04010b2536e74ca5e84c52023-10-19T23:01:37ZengIEEEIEEE Access2169-35362023-01-011111372611373910.1109/ACCESS.2023.332301910274707Hybrid Quantum Convolutional Neural Networks for UWB Signal ClassificationSeon-Geun Jeong0https://orcid.org/0000-0002-5926-6642Quang-Vinh Do1Hae-Ji Hwang2Mikio Hasegawa3https://orcid.org/0000-0001-5638-8022Hiroo Sekiya4https://orcid.org/0000-0003-3557-1463Won-Joo Hwang5https://orcid.org/0000-0001-8398-564XDepartment of Information Convergence Engineering, Center for Artificial Intelligence Research, Pusan National University, Yangsan-si, South KoreaArtificial Intelligence Research Center, Pusan National University, Busan, South KoreaDepartment of Information Convergence Engineering, Center for Artificial Intelligence Research, Pusan National University, Yangsan-si, South KoreaDepartment of Electrical Engineering, Tokyo University of Science, Tokyo, JapanGraduate School of Engineering, Chiba University, Chiba, JapanDepartment of Information Convergence Engineering, Center for Artificial Intelligence Research, Pusan National University, Yangsan-si, South KoreaWith the increasing requirements for location-based services for Internet of things (IoT) applications, ultrawideband (UWB) technology provides accurate indoor positioning capabilities. However, indoor environments contain various obstacles leading to significant signal propagation effects. This results in errors in the time-of-arrival-based UWB positioning system. Specifically, a non-line-of-sight (NLOS) signal induces additional distance and position errors owing to the path delay compared to a line-of-sight (LOS) signal. Therefore, UWB signal classification is essential for improving positioning accuracy. Recently, various approaches have successfully classified UWB signals, including machine-learning-based methods such as convolutional neural networks (CNNs) and long short-term memory (LSTM). This study proposes a hybrid quantum CNN (HQCNN) inspired by a CNN for UWB signal classification. HQCNN employs a classical layer before a quantum embedding circuit and variational quantum circuits for the convolutional filter. These structures enable efficient training and implementation. We used UWB channel impulse response data to demonstrate the performance of the proposed algorithm and compared the benchmarks with HQCNN using the evaluation metrics. The results showed that the HQCNN outperformed the others.https://ieeexplore.ieee.org/document/10274707/Hybrid quantum convolutional neural networkquantum convolutional neural networksignal classificationultrawidebandvariational quantum circuit |
| spellingShingle | Seon-Geun Jeong Quang-Vinh Do Hae-Ji Hwang Mikio Hasegawa Hiroo Sekiya Won-Joo Hwang Hybrid Quantum Convolutional Neural Networks for UWB Signal Classification IEEE Access Hybrid quantum convolutional neural network quantum convolutional neural network signal classification ultrawideband variational quantum circuit |
| title | Hybrid Quantum Convolutional Neural Networks for UWB Signal Classification |
| title_full | Hybrid Quantum Convolutional Neural Networks for UWB Signal Classification |
| title_fullStr | Hybrid Quantum Convolutional Neural Networks for UWB Signal Classification |
| title_full_unstemmed | Hybrid Quantum Convolutional Neural Networks for UWB Signal Classification |
| title_short | Hybrid Quantum Convolutional Neural Networks for UWB Signal Classification |
| title_sort | hybrid quantum convolutional neural networks for uwb signal classification |
| topic | Hybrid quantum convolutional neural network quantum convolutional neural network signal classification ultrawideband variational quantum circuit |
| url | https://ieeexplore.ieee.org/document/10274707/ |
| work_keys_str_mv | AT seongeunjeong hybridquantumconvolutionalneuralnetworksforuwbsignalclassification AT quangvinhdo hybridquantumconvolutionalneuralnetworksforuwbsignalclassification AT haejihwang hybridquantumconvolutionalneuralnetworksforuwbsignalclassification AT mikiohasegawa hybridquantumconvolutionalneuralnetworksforuwbsignalclassification AT hiroosekiya hybridquantumconvolutionalneuralnetworksforuwbsignalclassification AT wonjoohwang hybridquantumconvolutionalneuralnetworksforuwbsignalclassification |