1. TD3-Based Optimization Framework for RSMA-Enhanced UAV-Aided Downlink Communications in Remote Areas.
- Author
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Nguyen, Tri-Hai, Nguyen, Luong Vuong, Dang, L. Minh, Hoang, Vinh Truong, and Park, Laihyuk
- Subjects
DEEP reinforcement learning ,TELECOMMUNICATION systems ,REINFORCEMENT learning ,WIRELESS communications ,TELECOMMUNICATION satellites ,MATHEMATICAL optimization ,DRONE aircraft - Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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