Multi-Agent Reinforcement Learning for Cooperative Trajectory Design of UAV-BS Fleets in Terrestrial/Non-Terrestrial Integrated Networks
Author:
Affiliation:
1. The University of Aizu,Computer Communications Lab.,Fukushima,Japan,965-0006
2. School of Electrical and Electronic Engineering, Hanoi University of Science and Technology,Hanoi,Vietnam,100000
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx8/10250155/10634097/10554673.pdf?arnumber=10554673
Reference8 articles.
1. 6G Wireless Systems: Vision, Requirements, Challenges, Insights, and Opportunities
2. Trajectory Design and Link Selection in UAV-Assisted Hybrid Satellite-Terrestrial Network
3. Online Trajectory and Radio Resource Optimization of Cache-Enabled UAV Wireless Networks With Content and Energy Recharging
4. Adaptive Deployment for UAV-Aided Communication Networks
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