Fine Tuning an AI-Based Indoor Radio Propagation Model with Crowd-Sourced Data

Recent years have known the development of radio propagation models (RPM) and specially the AI-based ones. These models are interesting for many applications such as radio planing and design, fingerprinting-based localization, radio resources management etc. However most of the proposed AI-based RPMs have been trained on simulated radio data making them not ready or reliable for real condition applications. In this work we tackle the problem of learning and fine-tuning an AI-based RPM from simulated data to real world radio measurements. We raise the inherent problems and limitations and then propose solutions to overcome them. The study has been focused on 5 GHz Wi-Fi and Home network environment.