Geographically Adaptive Fine-Tuning of Deep Learning based Propagation Models in Diverse Environments

Effective radio propagation models are crucial for designing wireless communication systems that are both efficient and reliable, especially in tasks such as estimating coverage and analyzing interference. Mobile communication systems operate across diverse environments, including urban, suburban, and rural areas, necessitating propagation models tailored to specific geographical characteristics. We previously proposed a map-based method for clustering geographical data to classify environments. However, building a separate model for each cluster often suffers from limited training data, which can undermine the benefits of geographical classification. To address this trade-off, this paper introduces a novel approach: first, a global model is pre-trained using extensive training data that cover diverse geographical features across the entire area. Then, this global model is fine-tuned for each cluster identified through geographical clustering. This strategy customizes the propagation model to each geography while leveraging knowledge from the entire dataset, mitigating data scarcity in individual clusters. We validate the efficacy of the proposed approach using empirical data collected from various environmental settings. The proposed method demonstrates its effectiveness in reducing RMSE compared to a conventional method without fine-tuning, particularly in clusters representing high-rise areas, which are challenging to model due to propagation environment factors and limited training data.