Trajectory Design and Link Selection in UAV-Assisted Hybrid Satellite-Terrestrial Network

Mobile relaying is envisioned as a promising technology to alleviate the masking effect in satellite links. Nevertheless, due to the mobility and limited communication range, the network topology in mobile relay networks becomes highly dynamic and time-varying, resulting in increased difficulty of n...

Full description

Saved in:
Bibliographic Details
Published in:IEEE communications letters Vol. 26; no. 7; pp. 1643 - 1647
Main Authors: Chen, Yu-Jia, Chen, Wei, Ku, Meng-Lin
Format: Journal Article
Language:English
Published: New York IEEE 01.07.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Autonomous aerial vehicles
Bandwidth
Computational geometry
Convexity
Fading channels
graph neural network (GNN)
Graph neural networks
Low earth orbit satellites
Mixed integer
Network topologies
Optimization
Radio equipment
reinforcement learning
Relay networks
Relaying
Satellite communications
satellite networks
Satellites
Signal to noise ratio
Trajectory
trajectory design
Unmanned aerial vehicles
Unmanned aerial vehicles (UAVs)
ISSN:1089-7798, 1558-2558
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Mobile relaying is envisioned as a promising technology to alleviate the masking effect in satellite links. Nevertheless, due to the mobility and limited communication range, the network topology in mobile relay networks becomes highly dynamic and time-varying, resulting in increased difficulty of network optimization. This letter investigates unmanned aerial vehicles (UAVs) aided hybrid satellite-terrestrial network, where UAVs are served as mobile relay base stations to assist the communication between the satellite and ground users. We aim to maximize the number of served users by optimizing UAV trajectory and user link selection. To solve the formulated mixed-integer and non-convex optimization problem, we first find the optimal link selection via a designed graph neural network (GNN), and then adjust the UAV locations by using model-free reinforcement learning (RL), alternately. Numerical results demonstrate that our proposed scheme is superior to the state-of-the-art RL algorithms.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1089-7798
1558-2558
DOI:10.1109/LCOMM.2022.3166961