Leveraging Transfer Learning for Radio Map Estimation via Mixture of Experts

This paper leverages transfer learning (TL) on a mixture of experts (MoE) model for indoor radio map estimation. The proposed MoE combines location-based and location-free experts through a gating network exploiting their complementary benefits. To estimate the radio map in a new wireless environment, the learned model of another sufficiently similar wireless environment is transferred and fine-tuned with additional data from the new wireless environment. The proposed data-driven similarity measure predicts the amount of training data needed for TL. Results demonstrate that the proposed method achieves similar accuracy, in terms of mean square error (MSE), to a model trained without TL while only requiring 5-40% of measurement data to adapt to several varying wireless environments. The test environments include both the office area and the cafe area wireless environments. As expected, the proposed MoE method outperforms both experts as well as the state-of-the-art methods, in both the presence and the absence of noise in location-based and location-free features.