Radio Map Estimation: Empirical Validation and Analysis
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866909742006272000 |
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| author | Shrestha, Raju Ha, Tien Ngoc Viet, Pham Q. Romero, Daniel |
| author_facet | Shrestha, Raju Ha, Tien Ngoc Viet, Pham Q. Romero, Daniel |
| contents | Radio maps provide metrics such as the received signal strength at every location in a geographical region of interest. Extensive research has been carried out in this context, but it relies almost exclusively on synthetic-data experiments. Thus, the practical aspects of the radio map estimation (RME) problem as well as the performance of existing estimators in the real world remain unknown. To fill this gap end, this paper puts forth the first comprehensive, rigorous, and reproducible study of RME with real data. The main contributions include (C1) an assessment of the viability of RME based on the estimation error that can be achieved, (C2) the analysis of the main phenomena and trade-offs involved in RME, including the experimental verification of theoretical findings in the literature, and (C3) a thorough evaluation of a wide range of estimators on realworld data. Remarkably, this reveals that the performance gain of existing deep estimators in their pure form may not compensate for their complexity. A simple enhancement (C4) is proposed to alleviate this issue. The vast amount of data collected for this study is published along with the developed simulator to enable research on new schemes, hopefully bringing RME one step closer to practical deployment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_11036 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Radio Map Estimation: Empirical Validation and Analysis Shrestha, Raju Ha, Tien Ngoc Viet, Pham Q. Romero, Daniel Signal Processing Artificial Intelligence Applied Physics Radio maps provide metrics such as the received signal strength at every location in a geographical region of interest. Extensive research has been carried out in this context, but it relies almost exclusively on synthetic-data experiments. Thus, the practical aspects of the radio map estimation (RME) problem as well as the performance of existing estimators in the real world remain unknown. To fill this gap end, this paper puts forth the first comprehensive, rigorous, and reproducible study of RME with real data. The main contributions include (C1) an assessment of the viability of RME based on the estimation error that can be achieved, (C2) the analysis of the main phenomena and trade-offs involved in RME, including the experimental verification of theoretical findings in the literature, and (C3) a thorough evaluation of a wide range of estimators on realworld data. Remarkably, this reveals that the performance gain of existing deep estimators in their pure form may not compensate for their complexity. A simple enhancement (C4) is proposed to alleviate this issue. The vast amount of data collected for this study is published along with the developed simulator to enable research on new schemes, hopefully bringing RME one step closer to practical deployment. |
| title | Radio Map Estimation: Empirical Validation and Analysis |
| topic | Signal Processing Artificial Intelligence Applied Physics |
| url | https://arxiv.org/abs/2310.11036 |