Multi-RIS-Assisted 3D Localization and Synchronization via Deep Learning
Reconfigurable intelligent surfaces (RISs) have received considerable attention in applications related to localization. However, operation in multi-path scenarios is challenging from both complexity and performance perspectives. This study presents a two-stage low complexity method for joint three-...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Open Journal of the Communications Society |
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| Online Access: | https://ieeexplore.ieee.org/document/10542461/ |
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| author | Alireza Fadakar Maryam Sabbaghian Henk Wymeersch |
| author_facet | Alireza Fadakar Maryam Sabbaghian Henk Wymeersch |
| author_sort | Alireza Fadakar |
| collection | DOAJ |
| description | Reconfigurable intelligent surfaces (RISs) have received considerable attention in applications related to localization. However, operation in multi-path scenarios is challenging from both complexity and performance perspectives. This study presents a two-stage low complexity method for joint three-dimensional (3D) localization and synchronization using multiple RISs. Firstly, the received signals are preprocessed, and an efficient deep learning architecture is proposed to initially estimate the angles of departure (AODs) of the virtual line of sight paths from the RISs to the user. Then, a hybrid asynchronous AOD time-of-arrival-based approach is proposed in the first stage to estimate an initial guess of the position of the user equipment (UE). Finally, in the second stage, an optimization problem is formulated to refine the position of the UE by effectively utilizing the estimated delays and the clock offset. Our comparative study reveals that the proposed method outperforms the existing methods in terms of accuracy and complexity. Notably, the proposed method showcases enhanced robustness against multipath effects when compared to the state-of-the-art approaches. |
| format | Article |
| id | doaj-art-53f9205094a245649c8dc0f0ec3f28c8 |
| institution | Kabale University |
| issn | 2644-125X |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of the Communications Society |
| spelling | doaj-art-53f9205094a245649c8dc0f0ec3f28c82025-01-07T00:02:58ZengIEEEIEEE Open Journal of the Communications Society2644-125X2024-01-0153299331410.1109/OJCOMS.2024.339960510542461Multi-RIS-Assisted 3D Localization and Synchronization via Deep LearningAlireza Fadakar0https://orcid.org/0009-0002-2379-4547Maryam Sabbaghian1https://orcid.org/0000-0003-4387-590XHenk Wymeersch2https://orcid.org/0000-0002-1298-6159School of Electrical and Computer Engineering, University of Tehran, Tehran, IranDepartment of Electrical Engineering, Chalmers University of Technology, Gothenburg, SwedenDepartment of Electrical Engineering, Chalmers University of Technology, Gothenburg, SwedenReconfigurable intelligent surfaces (RISs) have received considerable attention in applications related to localization. However, operation in multi-path scenarios is challenging from both complexity and performance perspectives. This study presents a two-stage low complexity method for joint three-dimensional (3D) localization and synchronization using multiple RISs. Firstly, the received signals are preprocessed, and an efficient deep learning architecture is proposed to initially estimate the angles of departure (AODs) of the virtual line of sight paths from the RISs to the user. Then, a hybrid asynchronous AOD time-of-arrival-based approach is proposed in the first stage to estimate an initial guess of the position of the user equipment (UE). Finally, in the second stage, an optimization problem is formulated to refine the position of the UE by effectively utilizing the estimated delays and the clock offset. Our comparative study reveals that the proposed method outperforms the existing methods in terms of accuracy and complexity. Notably, the proposed method showcases enhanced robustness against multipath effects when compared to the state-of-the-art approaches.https://ieeexplore.ieee.org/document/10542461/3D localizationdeep learningmmWavereconfigurable intelligent surfacesynchronization |
| spellingShingle | Alireza Fadakar Maryam Sabbaghian Henk Wymeersch Multi-RIS-Assisted 3D Localization and Synchronization via Deep Learning IEEE Open Journal of the Communications Society 3D localization deep learning mmWave reconfigurable intelligent surface synchronization |
| title | Multi-RIS-Assisted 3D Localization and Synchronization via Deep Learning |
| title_full | Multi-RIS-Assisted 3D Localization and Synchronization via Deep Learning |
| title_fullStr | Multi-RIS-Assisted 3D Localization and Synchronization via Deep Learning |
| title_full_unstemmed | Multi-RIS-Assisted 3D Localization and Synchronization via Deep Learning |
| title_short | Multi-RIS-Assisted 3D Localization and Synchronization via Deep Learning |
| title_sort | multi ris assisted 3d localization and synchronization via deep learning |
| topic | 3D localization deep learning mmWave reconfigurable intelligent surface synchronization |
| url | https://ieeexplore.ieee.org/document/10542461/ |
| work_keys_str_mv | AT alirezafadakar multirisassisted3dlocalizationandsynchronizationviadeeplearning AT maryamsabbaghian multirisassisted3dlocalizationandsynchronizationviadeeplearning AT henkwymeersch multirisassisted3dlocalizationandsynchronizationviadeeplearning |