Cost-Effective Federated Learning-Based Approach for SINR Prediction in Cellular-Connected UAVs

This study introduces a novel approach to empower cellular-connected unmanned aerial vehi-cles (UAVs) in predicting signal quality. The proposed prediction model leverages data collected by the UAVs, addressing privacy concerns and ensuring effectiveness, while taking into account the constraints of UAVs. A unique three-step approach is proposed, which integrates a detailed physical ray-tracing (RT) method, deep learn-ing, and federated learning (FL) for continuous learning and field adaptation. A dual input feature fusion convo-lutional neural network (DIFF-CNN) model is proposed, which is pretrained on RT data and fine-tuned using data collected by the UAVs via FL. The proposed model demonstrates superior performance and robustness to data sparsity compared to traditional machine learning algorithms. Notably, the model achieves a root mean squared error of 0.837 dB and an R-squared of 97.7% for signal-to-interference-plus-noise ratio (SINR) prediction after the fine-tuning step in the fixed-altitude scenario, but performance drops with uniform altitude distribution, highlighting the impact of flying height on fine-tuning. The research indicates that the proposed approach can enhance performance while reducing training rounds by 35% to 90%, thus mitigating FL overheads. Future research could explore efficiency gains by using different pretrained models tailored to specific flying heights.