Cluster-Based Characterization and Modeling for UAV Air-to-Ground Time-Varying Channels
(2022) IEEE Transactions on Vehicular Technology — Vol. 71, n° 7, p. 6872-6883 (2022)
Files
Cluster-AG-channel-model.pdf
Open Access - Adobe PDF
- 2.3 MB
Details
- Authors
Cui, Zhuangzhuang 
Guan, Ke Briso-Rodriguez, Cesar Zhong, Zhangdui
- Abstract
- With the deep integration between the unmanned aerial vehicle (UAV) and wireless communication, UAV-based air-to-ground (AG) propagation channels need more detailed descriptions and accurate models. In this paper, we aim to conduct cluster-based characterization and modeling for AG channels. To our best knowledge, this is the first study that concentrates on the clustering and tracking of multipath components (MPCs) for time-varying AG channels. Based on measurement data at 6.5 GHz with a bandwidth of 500 MHz, we first estimate potential MPCs utilizing the space-alternating generalized expectation-maximization (SAGE) algorithm. Then, we cluster the extracted MPCs by employing K-Power-Means (KPM) algorithm under multipath component distance (MCD) measure. For characterizing time-variant clusters, we exploit a clustering-based tracking (CBT) method, which efficiently quantifies the survival lengths of clusters. Ultimately, we establish a cluster-based channel model, and validations illustrate the accuracy of the proposed model. This work not only promotes a better understanding of AG propagation channels but also provides a general cluster-based AG channel model with certain extensibility.
- Affiliations
Citations
Cui, Z., Guan, K., Oestges, C., Briso-Rodriguez, C., Ai, B., & Zhong, Z. (2022). Cluster-Based Characterization and Modeling for UAV Air-to-Ground Time-Varying Channels. IEEE Transactions on Vehicular Technology, 71(7), 6872-6883. https://doi.org/10.1109/tvt.2022.3168073 (Original work published 2022)