Spatial Frequency Interleaving Residual Autoencoder for Indoor Radio Map Reconstruction

Indoor radio maps with frequency domain data are difficult to reconstruct when only limited measurements at a few locations are available. Naive convolutional neural networks suffer from flawed structures in the frequency domain when predicting these radio maps, resulting in overly smoothed predictions. We propose a Spatial Frequency Interleaving Residual Autoencoder (SFIRA) architecture to tackle this problem, along with a Procedural Radio Map Generation (PRMG) method to address the data deficiency of indoor radio maps. Experimental results indicate that the proposed architecture achieves lower Normalized Root Mean Square Error (NRMSE). Visualization of the reconstruction further suggests that the proposed methods are effective.