RAICE: Real-time Autoencoder for Indoor Coverage Estimation

Indoor environments present significant challenges for electromagnetic propagation prediction due to the complexity of their geometric and physical modeling, as well as the difficulties in accurately reproducing the numerous interactions of electromagnetic waves.Complementing statistical and physical simulation tools, recent years have seen the emergence of approaches leveraging advancements in machine learning, particularly deep learning.This paper introduces a new framework for predicting electromagnetic coverage maps in unknown indoor environments, targeting frequencies from UHF to C-band.