Empirical LTE Air-to-Ground (A2G) Channel Measurement Dataset for Cellular-Connected UAVs

This dataset contains empirical measurements obtained through extensive aerial drive tests conducted in an operational Long-Term Evolution (LTE) cellular network environment, specifically designed for cellular-connected unmanned aerial vehicles (UAVs). The collected data comprises approximately 11,060 rigorously filtered samples, originally extracted from over 95,000 raw measurements, encompassing critical network parameters including Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), Received Signal Strength Indicator (RSSI), and Path Loss (PL). Each measurement is accompanied by detailed metadata such as log-transformed 3D and 2D propagation distances, azimuth angles, and elevation angles, providing comprehensive spatial context. This dataset serves as a robust foundation for developing and validating advanced machine learning-based 3D channel propagation models, contributing significantly to research and practical deployments involving UAV-integrated wireless communication systems.