A FL-Based Radio Map Reconstruction Approach for UAV-Aided Wireless Networks

Abstract

Radio maps, which can provide metrics for signal strength at any location in a geographic space, are useful for many applications of 6G technologies, including UAV-assisted communication, network planning, and resource allocation. However, current crowd-sourced reconstruction methods necessitate large amounts of privacy-sensitive user data and entail the training of all data with large models, especially in deep learning. This poses a threat to user privacy, reducing the willingness to provide data, and consuming significant server resources, rendering the reconstruction of radio maps on resource-constrained UAVs challenging. To address these limitations, a self-supervised federated learning model called RadioSRCNet is proposed. The model utilizes a super-resolution (SR)-based network and feedback training strategy to predict the pathloss for continuous positioning. In our proposition, users retain the original data locally for training, acting as clients, while the UAV functions as a server to aggregate non-sensitive data for radio map reconstruction in a federated learning (FL) manner. We have employed a feedback training strategy to accelerate convergence and alleviate training difficulty. In addition, we have introduced an arbitrary position prediction (APP) module to decrease resource consumption in clients. This innovative module struck a balance between spatial resolution and computational complexity. Our experimental results highlight the superiority of our proposed framework, as our model achieves higher accuracy while incurring less communication overheads in a computationally and storage-efficient manner as compared to other deep learning methods.