Machine Learning Based Rapid 3D Channel Modeling for UAV Communication Networks

This paper applies Machine Learning (ML) to predict the quality of Air-to-Ground (A2G) links performance for Unmanned Aerial Vehicles Base Stations (UAV-BSs) services. UAV-BSs can instantly identify the status of the current 3D wireless channel in an unknown environment without relying on previous statistical channel modeling. The proposed method that employs the unsupervised learning clustering technology applying to A2G channel modeling in 3D wireless communication scenarios. As environment changing, the proposed method can derive the 3D temporary channel model based on collected RSS data and analyzing. To evaluate the proposed method, the simulation data and measurement data are used to co-verify the performance. As the results shown, the RMSE of conventional statistical channel model and proposed temporary channel model are very similar. The similarity achieves about 91.8% both of the simulation and experimental environments to verify the accuracy and feasibility of our proposed method, and that provides more fast and effective of 3D channel modeling approach.

[1]  Lin He,et al.  Path Loss Measurement and Modeling for Low-Altitude UAV Access Channels , 2017, 2017 IEEE 86th Vehicular Technology Conference (VTC-Fall).

[2]  Jian Yu,et al.  Clustering Enabled Wireless Channel Modeling Using Big Data Algorithms , 2018, IEEE Communications Magazine.

[3]  Kandeepan Sithamparanathan,et al.  Optimal LAP Altitude for Maximum Coverage , 2014, IEEE Wireless Communications Letters.

[4]  Maurizio Magarini,et al.  A study of channel model parameters for aerial base stations at 2.4 GHz in different environments , 2018, 2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC).

[5]  Halim Yanikomeroglu,et al.  On the Number and 3D Placement of Drone Base Stations in Wireless Cellular Networks , 2016, 2016 IEEE 84th Vehicular Technology Conference (VTC-Fall).

[6]  David Gesbert,et al.  3D City Map Reconstruction from UAV-Based Radio Measurements , 2017, GLOBECOM 2017 - 2017 IEEE Global Communications Conference.

[7]  Agathoniki Trigoni,et al.  Non-Line-of-Sight Identification and Mitigation Using Received Signal Strength , 2015, IEEE Transactions on Wireless Communications.

[8]  David W. Matolak,et al.  A Survey of Air-to-Ground Propagation Channel Modeling for Unmanned Aerial Vehicles , 2018, IEEE Communications Surveys & Tutorials.