Geometry-Aware Cholesky Projection for Indoor Radio Map Sampling

Multi-frequency radio maps are vital for integrated sensing and communication, offering potential applications in indoor localization and smart homes. However, it is challenging to sample at sparse measurement locations and estimate the indoor radio map at unmeasured locations. Previous sampling algorithms do not consider the rough geometry of the furniture in the indoor environment. In order to utilize the room geometry to reduce the cost of precise on-site measurements, this work proposes a Geometry-Aware Cholesky Projection algorithm that effectively utilizes inaccurate indoor geometry information to suggest better measurement locations. Additionally, this study statistically analyzes the data distribution characteristics of 3D radio environments and radio map datasets, revealing a correlation between the room geometry and worst-case error variance. These insights justify the use of geometry information to enhance sampling efficiency in radio map reconstruction. With the proposed sampling algorithm and an autoencoder pretrained on uniform randomly masked radio maps, we find that the proposed algorithm outperforms state-of-the-art sampling algorithms.