Gaussian Process Based Channel Prediction for Communication-Relay UAV in Urban Environments

This paper presents a learning approach to predict air-to-ground communication channel strength to support the communication-relay mission using the unmanned aerial vehicle (UAV) in complex urban environments. The knowledge of the air-to-ground communication channel quality between the UAV and groun...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on aerospace and electronic systems 2020-02, Vol.56 (1), p.313-325
Main Authors: Ladosz, Pawel, Oh, Hyondong, Zheng, Gan, Chen, Wen-Hua
Format: Article
Language:English
Subjects:
Air-to-ground communication
Atmospheric modeling
Buildings
Communication channels
communication-relay unmanned aerial vehicle (UAV)
Computer simulation
Environment models
Gaussian process
Learning
Planning
Relay
Relays
Trajectory
Trajectory optimization
Trajectory planning
Unmanned aerial vehicles
Urban areas
urban environment
Urban environments
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper presents a learning approach to predict air-to-ground communication channel strength to support the communication-relay mission using the unmanned aerial vehicle (UAV) in complex urban environments. The knowledge of the air-to-ground communication channel quality between the UAV and ground nodes is essential for optimal relay trajectory planning. However, because of the obstruction by buildings and interferences in the urban environment, modeling and predicting the communication channel strength is a challenging task. We address this issue by leveraging the Gaussian process (GP) method to learn the communication shadow fading in a given environment and then employing the optimization-based relay trajectory planning by using learned communication properties. The key advantage of this learning method over fixed communication model based approaches is that it can keep refining channel prediction and trajectory planning as more channel measurement data are obtained. Two schemes incorporating GP-based channel prediction into trajectory planning are proposed. Monte Carlo simulations demonstrate the performance gain and robustness of the proposed approaches over the existing methods.
ISSN:0018-9251
1557-9603
DOI:10.1109/TAES.2019.2917989