Index-modulation OAM detectors resistant to beam misalignment
Orbital angular momentum with index modulation (OAM-IM) has a great potential of providing high spectral efficiency and energy efficiency by utilizing the indices of the orthogonal OAM modes. However, the harsh requirement of perfect alignment of the transceiver beams introduces great challenge...
| Principais autores: | , , , , |
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| Outros Autores: | |
| Formato: | Journal Article |
| Idioma: | English |
| Publicado em: |
2024
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| Assuntos: | |
| Acesso em linha: | https://hdl.handle.net/10356/174527 |
| _version_ | 1826117954621669376 |
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| author | Chen, Min Chen, Rui Zhao, Yufei Yang, Zhaojie Guan, Yong Liang |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Chen, Min Chen, Rui Zhao, Yufei Yang, Zhaojie Guan, Yong Liang |
| author_sort | Chen, Min |
| collection | NTU |
| description | Orbital angular momentum with index modulation
(OAM-IM) has a great potential of providing high spectral
efficiency and energy efficiency by utilizing the indices of the
orthogonal OAM modes. However, the harsh requirement of
perfect alignment of the transceiver beams introduces great
challenges to OAM-IM wireless communications. Therefore, we
first propose an angle of arrival (AoA)-based robust detector for
the misaligned OAM-IM system, which explicitly estimates the
AoA of the OAM beam and then utilizes the estimate to detect
the transmitted symbols. To further reduce the system overhead
and complexity, we propose another deep learning (DL)-based
robust detector, which implicitly estimates the AoA and directly
recovers the transmitted information bits. By using the dataset
collected through simulation, the first step is to train the DLbased
robust detector offline to minimize the mean-squared error,
and the second step is to use the trained model for real-time
OAM-IM signal detection online. Numerical simulations validate
that the both proposed robust detectors can address the channel
distortion in OAM channels with beam misalignment and achieve
superior bit error rate (BER) performance at high spectral
efficiency. Moreover, the proposed DL-based robust detector
is less complicated on runtime than the traditional OAM-IM
detector. |
| first_indexed | 2024-10-01T04:35:49Z |
| format | Journal Article |
| id | ntu-10356/174527 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:35:49Z |
| publishDate | 2024 |
| record_format | dspace |
| spelling | ntu-10356/1745272024-04-05T15:41:22Z Index-modulation OAM detectors resistant to beam misalignment Chen, Min Chen, Rui Zhao, Yufei Yang, Zhaojie Guan, Yong Liang School of Electrical and Electronic Engineering Engineering Angle of arrival Deep learning Orbital angular momentum with index modulation (OAM-IM) has a great potential of providing high spectral efficiency and energy efficiency by utilizing the indices of the orthogonal OAM modes. However, the harsh requirement of perfect alignment of the transceiver beams introduces great challenges to OAM-IM wireless communications. Therefore, we first propose an angle of arrival (AoA)-based robust detector for the misaligned OAM-IM system, which explicitly estimates the AoA of the OAM beam and then utilizes the estimate to detect the transmitted symbols. To further reduce the system overhead and complexity, we propose another deep learning (DL)-based robust detector, which implicitly estimates the AoA and directly recovers the transmitted information bits. By using the dataset collected through simulation, the first step is to train the DLbased robust detector offline to minimize the mean-squared error, and the second step is to use the trained model for real-time OAM-IM signal detection online. Numerical simulations validate that the both proposed robust detectors can address the channel distortion in OAM channels with beam misalignment and achieve superior bit error rate (BER) performance at high spectral efficiency. Moreover, the proposed DL-based robust detector is less complicated on runtime than the traditional OAM-IM detector. Info-communications Media Development Authority (IMDA) National Research Foundation (NRF) Submitted/Accepted version This work was supported in part by National Natural Science Foundation of China under Grant 62271376, in part by Guangdong Natural Science Fund for Distinguished Young Scholar under Grant 2023B1515020079, in part by Natural Science Foundation of Guangdong Province of China under Grant 2021A1515010812, in part by the Natural Science Basic Research Program of Shaanxi Province under Grant 2021JZ-18, and in part by National Research Foundation Singapore and Infocomm Media Development Authority through Future Communications Research and Development Programme under Grant FCP-NTU-RG-2021-015. 2024-04-01T06:05:13Z 2024-04-01T06:05:13Z 2023 Journal Article Chen, M., Chen, R., Zhao, Y., Yang, Z. & Guan, Y. L. (2023). Index-modulation OAM detectors resistant to beam misalignment. IEEE Transactions On Vehicular Technology, 73(2), 2836-2841. https://dx.doi.org/10.1109/TVT.2023.3312295 0018-9545 https://hdl.handle.net/10356/174527 10.1109/TVT.2023.3312295 2 73 2836 2841 en FCP-NTU-RG-2022-011 IEEE Transactions on Vehicular Technology © 2023 IEEE. All rights reserved. This article may be downloaded for personal use only. Any other use requires prior permission of the copyright holder. The Version of Record is available online at http://doi.org/10.1109/TVT.2023.3312295. application/pdf |
| spellingShingle | Engineering Angle of arrival Deep learning Chen, Min Chen, Rui Zhao, Yufei Yang, Zhaojie Guan, Yong Liang Index-modulation OAM detectors resistant to beam misalignment |
| title | Index-modulation OAM detectors resistant to beam misalignment |
| title_full | Index-modulation OAM detectors resistant to beam misalignment |
| title_fullStr | Index-modulation OAM detectors resistant to beam misalignment |
| title_full_unstemmed | Index-modulation OAM detectors resistant to beam misalignment |
| title_short | Index-modulation OAM detectors resistant to beam misalignment |
| title_sort | index modulation oam detectors resistant to beam misalignment |
| topic | Engineering Angle of arrival Deep learning |
| url | https://hdl.handle.net/10356/174527 |
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