IEICE TRANSACTIONS on Communications
Graph Laplacian-Based Sequential Smooth Estimator for Three-Dimensional RSS Map
Summary :
In wireless links between ground stations and UAVs (Unmanned Aerial Vehicles), wireless signals may be attenuated by obstructions such as buildings. A three-dimensional RSS (Received Signal Strength) map (3D-RSS map), which represents a set of RSSs at various reception points in a three-dimensional area, is a promising geographical database that can be used to design reliable ground-to-air wireless links. The construction of a 3D-RSS map requires higher computational complexity, especially for a large 3D area. In order to sequentially estimate a 3D-RSS map from partial observations of RSS values in the 3D area, we propose a graph Laplacian-based sequential smooth estimator. In the proposed estimator, the 3D area is divided into voxels, and a UAV observes the RSS values at the voxels along a predetermined path. By considering the voxels as vertices in an undirected graph, a measurement graph is dynamically constructed using vertices from which recent observations were obtained and their neighboring vertices, and the 3D-RSS map is sequentially estimated by performing graph Laplacian regularized least square estimation.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E104-B No.7 pp.738-748
- Publication Date
- 2021/07/01
- Publicized
- 2021/01/08
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.2020CQP0003
- Type of Manuscript
- Special Section PAPER (Special Section on Future Directions of Research and Development on Communication Quality)
- Category
Authors
Takahiro MATSUDA
Tokyo Metropolitan University
Fumie ONO
National Institute of Information and Communication
Shinsuke HARA
Osaka City University
Keyword
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Takahiro MATSUDA, Fumie ONO, Shinsuke HARA, "Graph Laplacian-Based Sequential Smooth Estimator for Three-Dimensional RSS Map" in IEICE TRANSACTIONS on Communications,
vol. E104-B, no. 7, pp. 738-748, July 2021, doi: 10.1587/transcom.2020CQP0003.
Abstract: In wireless links between ground stations and UAVs (Unmanned Aerial Vehicles), wireless signals may be attenuated by obstructions such as buildings. A three-dimensional RSS (Received Signal Strength) map (3D-RSS map), which represents a set of RSSs at various reception points in a three-dimensional area, is a promising geographical database that can be used to design reliable ground-to-air wireless links. The construction of a 3D-RSS map requires higher computational complexity, especially for a large 3D area. In order to sequentially estimate a 3D-RSS map from partial observations of RSS values in the 3D area, we propose a graph Laplacian-based sequential smooth estimator. In the proposed estimator, the 3D area is divided into voxels, and a UAV observes the RSS values at the voxels along a predetermined path. By considering the voxels as vertices in an undirected graph, a measurement graph is dynamically constructed using vertices from which recent observations were obtained and their neighboring vertices, and the 3D-RSS map is sequentially estimated by performing graph Laplacian regularized least square estimation.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2020CQP0003/_p
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@ARTICLE{e104-b_7_738,
author={Takahiro MATSUDA, Fumie ONO, Shinsuke HARA, },
journal={IEICE TRANSACTIONS on Communications},
title={Graph Laplacian-Based Sequential Smooth Estimator for Three-Dimensional RSS Map},
year={2021},
volume={E104-B},
number={7},
pages={738-748},
abstract={In wireless links between ground stations and UAVs (Unmanned Aerial Vehicles), wireless signals may be attenuated by obstructions such as buildings. A three-dimensional RSS (Received Signal Strength) map (3D-RSS map), which represents a set of RSSs at various reception points in a three-dimensional area, is a promising geographical database that can be used to design reliable ground-to-air wireless links. The construction of a 3D-RSS map requires higher computational complexity, especially for a large 3D area. In order to sequentially estimate a 3D-RSS map from partial observations of RSS values in the 3D area, we propose a graph Laplacian-based sequential smooth estimator. In the proposed estimator, the 3D area is divided into voxels, and a UAV observes the RSS values at the voxels along a predetermined path. By considering the voxels as vertices in an undirected graph, a measurement graph is dynamically constructed using vertices from which recent observations were obtained and their neighboring vertices, and the 3D-RSS map is sequentially estimated by performing graph Laplacian regularized least square estimation.},
keywords={},
doi={10.1587/transcom.2020CQP0003},
ISSN={1745-1345},
month={July},}
Copy
TY - JOUR
TI - Graph Laplacian-Based Sequential Smooth Estimator for Three-Dimensional RSS Map
T2 - IEICE TRANSACTIONS on Communications
SP - 738
EP - 748
AU - Takahiro MATSUDA
AU - Fumie ONO
AU - Shinsuke HARA
PY - 2021
DO - 10.1587/transcom.2020CQP0003
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E104-B
IS - 7
JA - IEICE TRANSACTIONS on Communications
Y1 - July 2021
AB - In wireless links between ground stations and UAVs (Unmanned Aerial Vehicles), wireless signals may be attenuated by obstructions such as buildings. A three-dimensional RSS (Received Signal Strength) map (3D-RSS map), which represents a set of RSSs at various reception points in a three-dimensional area, is a promising geographical database that can be used to design reliable ground-to-air wireless links. The construction of a 3D-RSS map requires higher computational complexity, especially for a large 3D area. In order to sequentially estimate a 3D-RSS map from partial observations of RSS values in the 3D area, we propose a graph Laplacian-based sequential smooth estimator. In the proposed estimator, the 3D area is divided into voxels, and a UAV observes the RSS values at the voxels along a predetermined path. By considering the voxels as vertices in an undirected graph, a measurement graph is dynamically constructed using vertices from which recent observations were obtained and their neighboring vertices, and the 3D-RSS map is sequentially estimated by performing graph Laplacian regularized least square estimation.
ER -
English
