Image-Driven Spatial Interpolation with Deep Learning for Radio Map Construction

Katsuya Suto, Shinsuke Bannai, Koya Sato, Kei Inage, Koichi Adachi, Takeo Fujii

研究成果: Article査読

3 被引用数 (Scopus)

抄録

Radio maps are a promising technology that can boost the capability of wireless networks by enhancing spectrum efficiency. Since spatial interpolation is a critical challenge to construct a precise radio map, the latest works have proposed deep learning (DL)-based interpolation methods. However, a DL model that achieves enough estimation accuracy for practical uses has not yet been established due to the complexity of radio propagation characteristics. Therefore, we propose a novel DL framework that transforms the spatial interpolation problem into a shadowing adjustment problem suitable for DL-based approaches. We evaluate the performance using real measurement data in urban and suburban areas to show that the proposed framework outperforms the state-of-the-art deep learning models.

本文言語English
論文番号9365700
ページ(範囲)1222-1226
ページ数5
ジャーナルIEEE Wireless Communications Letters
10
6
DOI
出版ステータスPublished - 6月 2021

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