This paper leverages transfer learning~(TL) on a mixture of experts~(MoE) model for 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 environment is transferred and fine-tuned with additional data from the new environment. The proposed data-driven similarity measure predicts the amount of data needed for TL. Results demonstrate that the proposed method only requires 20-40\% of measurements to adapt to several varying environments, and as expected, the proposed MoE method outperforms both experts.

Estimating accurate radio maps is important for various tasks in wireless communications, such as channel modeling, resource allocation, and network planning, to name a few. Due to the changes in the propagation characteristics of the wireless environments, a radio map model learned under a particular wireless environment cannot be directly used in a new wireless environment. Moreover, learning a new model for every environment requires, in general, a large amount of data and is computationally demanding. In this work, we design an effective novel data-driven transfer learning method that transfers and fine-tunes a deep neural network (DNN)-based radio map model learned from an original wireless environment to other wireless environments with a certain level of similarity, allowing the radio map to be estimated with less amount of training data. As opposed to other widely used similarity measures that do not take into account the wireless propagation characteristics, we design a data-driven similarity measure that predicts the mean square error (MSE) and the amount of training data needed when learning a radio map in a new wireless environment. The proposed solution is corroborated by extensive simulations over a range of wireless environments, achieving savings of approximately 60-70% in sensor measurement data.