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

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.

UAV Relay Energy Consumption Minimization in an MEC-Assisted Marine Data Collection System

“…One of the main challenges is the limited service capability of a UAV when relaying large-scale marine container data, as these containers are sparsely distributed on the sea surface. Additionally, advanced techniques, such as reconfigurable intelligent surface sand rate-splitting multiple access, can be used to enhance system performance of marine communication networks [29][30][31]. Thus, future research could focus on resource optimization in collaborative MIoT systems with multiple aerial nodes.…”