Exemplar-Based Radio Map Reconstruction of Missing Areas Using Propagation Priority

被引:2
作者
Zhang, Songyang [1 ]
Yu, Tianhang [1 ]
Tivald, Jonathan [1 ]
Choi, Brian [2 ]
Ouyang, Feng [2 ]
Ding, Zhi [1 ]
机构
[1] Univ Calif Davis, Dept Elect & Comp Engn, Davis, CA 95616 USA
[2] Johns Hopkins Univ, Appl Phys Lab, Johns Hopkins Rd, Laurel, MD 20723 USA
来源
2022 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM 2022) | 2022年
基金
美国国家科学基金会;
关键词
Radio map; inpainting; dictionary learning;
D O I
10.1109/GLOBECOM48099.2022.10001269
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Radio map describes network coverage and is a practically important tool for network planning in modern wireless systems. Generally, radio strength measurements are collected to construct fine-resolution radio maps for analysis. However, certain protected areas are not accessible for measurement due to physical constraints and security considerations, leading to blanked spaces on a radio map. Non-uniformly spaced measurement and uneven observation resolution make it more difficult for radio map estimation and spectrum planning in protected areas. This work explores the distribution of radio spectrum strengths and proposes an exemplar-based approach to reconstruct missing areas on a radio map. Instead of taking generic image processing approaches, we leverage radio propagation models to determine directions of region filling and develop two different schemes to estimate the missing radio signal power. Our test results based on high-fidelity simulation demonstrate efficacy of the proposed methods for radio map reconstruction.
引用
收藏
页码:1217 / 1222
页数:6
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