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

Jing Ling Wang, Yun Ruei Li, Abebe Belay Adege, Li-Chun Wang, Shiann Shiun Jeng, Jen Yeu Chen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publication2019 16th IEEE Annual Consumer Communications and Networking Conference, CCNC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538655535
DOIs
StatePublished - 25 Feb 2019
Event16th IEEE Annual Consumer Communications and Networking Conference, CCNC 2019 - Las Vegas, United States
Duration: 11 Jan 201914 Jan 2019

Publication series

Name2019 16th IEEE Annual Consumer Communications and Networking Conference, CCNC 2019

Conference

Conference16th IEEE Annual Consumer Communications and Networking Conference, CCNC 2019
CountryUnited States
CityLas Vegas
Period11/01/1914/01/19

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