Indoor Radio Map Dataset

Efficient and realistic tools capable of modeling radio signal propagation are an indispensable component for the effective operation of wireless communication networks. The advent of artificial intelligence (AI) has propelled the evolution of a new generation of signal modeling tools, leveraging deep learning (DL) models that learn to infer signal characteristics. This Grand Challenge will probe the potential of DL algorithms to infer wireless signal attenuation in indoor propagation environments, where modeling signal propagation is more challenging due to a substantially larger number of reflected, refracted, scattered, or diffracted electromagnetic field components. To this end, we release a large dataset comprising radio maps from ray tracing simulations conducted in indoor environments of varying complexity, multiple frequency bands, and assuming different antenna radiation patterns. Exploiting these data enables the development of full-fledged data-driven propagation models that can generalize simultaneously over new building layouts, frequency bands, and antenna radiation patterns, thus paving the way for the replacement of legacy radio signal propagation modeling techniques.