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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.04205 |
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| _version_ | 1866908009481895936 |
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| author | Chen, Xinwei Zhong, Xiaofeng Zhang, Zijian Dai, Linglong Zhou, Shidong |
| author_facet | Chen, Xinwei Zhong, Xiaofeng Zhang, Zijian Dai, Linglong Zhou, Shidong |
| contents | Recent widespread applications for unmanned aerial vehicles (UAVs) -- from infrastructure inspection to urban logistics -- have prompted an urgent need for high-accuracy three-dimensional (3D) radio maps. However, existing methods designed for two-dimensional radio maps face challenges of high measurement costs and limited data availability when extended to 3D scenarios. To tackle these challenges, we first build a real-world large-scale 3D radio map dataset, covering over 4.2 million m^3 and over 4 thousand data points in complex urban environments. We propose a Gaussian Process Regression-based scheme for 3D radio map estimation, allowing us to realize more accurate map recovery with a lower RMSE than state-of-the-art schemes by over 2.5 dB. To further enhance data efficiency, we propose two methods for training point selection, including an offline clustering-based method and an online maximum a posterior (MAP)-based method. Extensive experiments demonstrate that the proposed scheme not only achieves full-map recovery with only 2% of UAV measurements, but also sheds light on future studies on 3D radio maps. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_04205 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | High-Efficiency Urban 3D Radio Map Estimation Based on Sparse Measurements Chen, Xinwei Zhong, Xiaofeng Zhang, Zijian Dai, Linglong Zhou, Shidong Information Theory Recent widespread applications for unmanned aerial vehicles (UAVs) -- from infrastructure inspection to urban logistics -- have prompted an urgent need for high-accuracy three-dimensional (3D) radio maps. However, existing methods designed for two-dimensional radio maps face challenges of high measurement costs and limited data availability when extended to 3D scenarios. To tackle these challenges, we first build a real-world large-scale 3D radio map dataset, covering over 4.2 million m^3 and over 4 thousand data points in complex urban environments. We propose a Gaussian Process Regression-based scheme for 3D radio map estimation, allowing us to realize more accurate map recovery with a lower RMSE than state-of-the-art schemes by over 2.5 dB. To further enhance data efficiency, we propose two methods for training point selection, including an offline clustering-based method and an online maximum a posterior (MAP)-based method. Extensive experiments demonstrate that the proposed scheme not only achieves full-map recovery with only 2% of UAV measurements, but also sheds light on future studies on 3D radio maps. |
| title | High-Efficiency Urban 3D Radio Map Estimation Based on Sparse Measurements |
| topic | Information Theory |
| url | https://arxiv.org/abs/2408.04205 |