Paper Info
Title
Adaptive UAV-Assisted Geographic Routing with Q-Learning in VANET
Abstract
The Q-learning based geographic routing approaches suffer from problems of low converging speed and inefficient resources utilization in VANET due to the dynamic scale of Q-value table. In addition, the next hop selection based on local information can not always be conducive to the global message forwarding. In this letter, we propose an adaptive unmanned aerial vehicle (UAV) assisted geographic ...
Year
DOI
Venue
2021
10.1109/LCOMM.2020.3048250
IEEE Communications Letters
Keywords
DocType
Volume
Routing,Roads,Vehicular ad hoc networks,Reinforcement learning,Convergence,Unmanned aerial vehicles,Information processing
Journal
25
Issue
ISSN
Citations 
4
1089-7798
2
PageRank 
References 
Authors
0.42
0
3
Name
Order
Citations
PageRank
Shanshan Jiang120.42
Zhitong Huang220.42
Yuefeng Ji3138.67