Deep Reinforcement Learning For Multi-User Access Control in Non-Terrestrial Networks

Non-Terrestrial Networks (NTNs) composed of space-borne (e.g., satellites) and airborne vehicles (e.g., drones and blimps) have recently been proposed by 3GPP as a new paradigm of infrastructures to enhance the capacity and coverage of existing terrestrial wireless networks. The mobility of non-terr...

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Bibliographic Details
Published in:IEEE transactions on communications 2021-03, Vol.69 (3), p.1605-1619
Main Authors: Cao, Yang, Lien, Shao-Yu, Liang, Ying-Chang
Format: Article
Language:English
Subjects:
Access control
Control equipment
Deep learning
deep reinforcement learning (DRL)
Drone vehicles
Drones
Handover
handovers
multi-user access control
Non-terrestrial networks (NTNs)
Optimization
Parameters
Radio equipment
Reinforcement learning
Throughput
Trajectory
UE-driven scheme
Wireless networks
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Summary:Non-Terrestrial Networks (NTNs) composed of space-borne (e.g., satellites) and airborne vehicles (e.g., drones and blimps) have recently been proposed by 3GPP as a new paradigm of infrastructures to enhance the capacity and coverage of existing terrestrial wireless networks. The mobility of non-terrestrial base stations (NT-BSs) however leads to a dynamic environment, which imposes unique challenges for handover and throughput optimization particularly in multi-user access control for NTNs. To achieve performance optimization, each terrestrial user equipment (UE) should autonomously estimate the dynamics of moving NT-BSs, which is different from the existing user access control schemes in terrestrial wireless networks. Consequently, new learning schemes for optimum multi-user access control are desired. In this article, we therefore propose a UE-driven deep reinforcement learning (DRL) based scheme, in which a centralized agent deployed at the backhaul side of NT-BSs is responsible for training the parameter of a deep Q-network (DQN), and each UE independently makes its own access decisions based on the parameter from the trained DQN. With the proposed scheme, each UE is able to access a proper NT-BS intelligently to enhance the long-term system throughput and avoid frequent handovers among NT-BSs. Through comprehensive simulation studies, we justify the performance of the proposed scheme, and show its effectiveness in addressing the fundamental issues in the NTNs deployment.
ISSN:0090-6778
1558-0857
DOI:10.1109/TCOMM.2020.3041347