Cluster-Based Characterization and Modeling for UAV Air-to-Ground Time-Varying Channels

With the deep integration between unmanned aerial vehicle (UAV) and wireless communication, UAV-based air-to-ground (AG) propagation channels need more detailed descriptions accurate models. In this paper, we aim to conduct cluster-based characterization modeling for AG channels. To our best knowledge, is first study that concentrates on clustering tracking of multipath components (MPCs) time-varying Based measurement data at 6.5 GHz with a bandwidth 500 MHz, estimate potential MPCs utilizing space-alternating generalized expectation-maximization (SAGE) algorithm. Then, cluster extracted by employing K-Power-Means (KPM) algorithm under component distance (MCD) measure. For characterizing time-variant clusters, exploit clustering-based (CBT) method, which efficiently quantifies survival lengths clusters. Ultimately, establish channel model, validations illustrate accuracy proposed model. This work not only promotes better understanding but also provides general model certain extensibility.