Learning radio maps for UAV-aided wireless networks: A segmented regression approach

This paper targets the promising area of unmanned aerial vehicle (UAV)-assisted wireless networking, by which communication-enabled robots operate as flying wireless relays to help fill coverage or capacity gaps in the networks. In order to feed the UAV's autonomous path planning and positionin...

Full description

Saved in:
Bibliographic Details
Main Authors: Chen, Junting, Yatnalli, Uday, Gesbert, David
Format: Conference Proceeding
Language:English
Subjects:
Buildings
Gain
Maximum likelihood estimation
Shadow mapping
Signal processing algorithms
Urban areas
Wireless communication
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper targets the promising area of unmanned aerial vehicle (UAV)-assisted wireless networking, by which communication-enabled robots operate as flying wireless relays to help fill coverage or capacity gaps in the networks. In order to feed the UAV's autonomous path planning and positioning algorithm, a radio map is exploited, which must be, in practice, reconstructed from UAV-based measurements from a limited subset of locations. Unlike existing methods that ignore the segmented propagation structure of the radio map, this paper proposes a machine learning approach to reconstruct a finely structured map by exploiting both segmentation and signal strength models. A data clustering and parameter estimation problem is formulated using a maximum likelihood approach, and solved by an iterative clustering and regression algorithm. Numerical results demonstrate significant performance advantage in radio map reconstruction as compared to the baseline.
ISSN:1938-1883
DOI:10.1109/ICC.2017.7997333