TD3-Based Optimization Framework for RSMA-Enhanced UAV-Aided Downlink Communications in Remote Areas

The need for reliable wireless communication in remote areas has led to the adoption of unmanned aerial vehicles (UAVs) as flying base stations (FlyBSs). FlyBSs hover over a designated area to ensure continuous communication coverage for mobile users on the ground. Moreover, rate-splitting multiple...

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Bibliographic Details
Published inRemote sensing (Basel, Switzerland) Vol. 15; no. 22; p. 5284
Main Authors Nguyen, Tri-Hai, Nguyen, Luong Vuong, Dang, L. Minh, Hoang, Vinh Truong, Park, Laihyuk
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.11.2023
Subjects
Algorithms
Communications systems
Convexity
downlink communication
Downlinking
Drone aircraft
Energy efficiency
Flexibility
flying base station
Internet of Things
Optimization
Optimization techniques
Performance evaluation
Power
rate-splitting multiple access
Remote regions
Simulation methods
twin-delayed deep deterministic policy gradient
unmanned aerial vehicle
Unmanned aerial vehicles
Wireless communications
Wireless networks
Wireless telecommunications equipment
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Summary:The need for reliable wireless communication in remote areas has led to the adoption of unmanned aerial vehicles (UAVs) as flying base stations (FlyBSs). FlyBSs hover over a designated area to ensure continuous communication coverage for mobile users on the ground. Moreover, rate-splitting multiple access (RSMA) has emerged as a promising interference management scheme in multi-user communication systems. In this paper, we investigate an RSMA-enhanced FlyBS downlink communication system and formulate an optimization problem to maximize the sum-rate of users, taking into account the three-dimensional FlyBS trajectory and RSMA parameters. To address this continuous non-convex optimization problem, we propose a TD3-RFBS optimization framework based on the twin-delayed deep deterministic policy gradient (TD3). This framework overcomes the limitations associated with the overestimation issue encountered in the deep deterministic policy gradient (DDPG), a well-known deep reinforcement learning method. Our simulation results demonstrate that TD3-RFBS outperforms existing solutions for FlyBS downlink communication systems, indicating its potential as a solution for future wireless networks.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs15225284