OnRMap: An Online Radio Mapping Approach for Large Intelligent Surfaces
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866914676596539392 |
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| author | Santos, Herman L. Croisfelt, Victor Vaca-Rubio, Cristian J. Abrao, Taufik Popovski, Petar |
| author_facet | Santos, Herman L. Croisfelt, Victor Vaca-Rubio, Cristian J. Abrao, Taufik Popovski, Petar |
| contents | We introduce OnRMap, an online radio mapping (RMap) approach for the sensing and localization of active users (AUs), devices that are transmitting radio signals, and passive elements (PEs), elements that are in the environment and are illuminated by the AUs' radio signals. OnRMap processes the signals received by a large intelligent surface and produces a radio map (RM) of the environment based on signal processing techniques. The method then senses and locate the different elements without the need for offline scanning phases, which is important for environments with frequently changing spatial layouts. Empirical results demonstrate that OnRMap presents a higher localization accuracy than an offline method, but the price paid for being an online method is a moderate reduction in the detection rate. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2302_02638 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | OnRMap: An Online Radio Mapping Approach for Large Intelligent Surfaces Santos, Herman L. Croisfelt, Victor Vaca-Rubio, Cristian J. Abrao, Taufik Popovski, Petar Signal Processing Image and Video Processing We introduce OnRMap, an online radio mapping (RMap) approach for the sensing and localization of active users (AUs), devices that are transmitting radio signals, and passive elements (PEs), elements that are in the environment and are illuminated by the AUs' radio signals. OnRMap processes the signals received by a large intelligent surface and produces a radio map (RM) of the environment based on signal processing techniques. The method then senses and locate the different elements without the need for offline scanning phases, which is important for environments with frequently changing spatial layouts. Empirical results demonstrate that OnRMap presents a higher localization accuracy than an offline method, but the price paid for being an online method is a moderate reduction in the detection rate. |
| title | OnRMap: An Online Radio Mapping Approach for Large Intelligent Surfaces |
| topic | Signal Processing Image and Video Processing |
| url | https://arxiv.org/abs/2302.02638 |