Physics-Inspired Distributed Radio Map Estimation

To gain panoramic awareness of spectrum coverage in complex wireless environments, data-driven learning approaches have recently been introduced for radio map estimation (RME). While existing deep learning based methods conduct RME given spectrum measurements gathered from dispersed sensors in the region of interest, they rely on the centralized data collected at a fusion center, which unfortunately raises critical concerns on data privacy leakages and high communication overloads. Federated learning (FL) enhances data security and communication efficiency in RME by allowing multiple clients to collaborate in model training without directly sharing local data. However, the performance of the FL-based RME might be hindered by the problem of task heterogeneity across clients located in different environments. To fill this gap, we propose a physicsinspired distributed RME solution for heterogeneous settings in this paper. The key idea is to develop a novel distributed RME framework empowered by leveraging the domain knowledge of radio propagation models. To do so, we design a new distributed learning approach that splits the entire RME deep model into two modules. A global autoencoder module is shared among clients to capture the common pathloss influence on radio propagation patterns, while a client-specific autoencoder module focuses on learning the individual features produced by local shadowing effects from the unique building distributions in local environment. Simulation results show that our proposed method outperforms the benchmarks in achieving higher performance.