Image-Driven Spatial Interpolation With Deep Learning for Radio Map Construction
Katsuya Suto, Shinsuke Bannai, Koya Sato, Kei Inage, Koichi Adachi, Takeo Fujii
Abstract
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.