RIS-Assisted UAV-Based Dynamic Coverage Control for 6G-Enabled Internet of Everything Using Multi-Agent DRL

The Internet of Everything (IoE) is accelerating the demand for intelligent, low-latency, and highly reliable communication systems to support automation and real-time decision-making. In dense urban and industrial environments, unmanned aerial vehicles (UAVs) are increasingly utilized to extend network coverage, improve connectivity, and enable dynamic data collection. However, managing RIS-assisted UAV-enabled IoE networks poses significant challenges, including accurate signal prediction, high computational complexity, and decentralized task assignment. To address these issues, we propose a novel RIS-empowered UAV-based Dynamic Area of Coverage (DAC) architecture. In this framework, UAVs equipped with reconfigurable intelligent surfaces (RIS) adaptively adjust the phase of reflected signals to optimize wireless channel conditions, suppress interference, and enhance signal quality.We formulate the Dynamic Area of Coverage with Location, Resource Allocation, and Trajectory Optimization (DAC-LRT) problem as a mixed-integer nonlinear programming (MINLP) model, aiming to jointly optimize UAV positioning, power distribution, and trajectory control to maximize real-time downlink capacity and ensure energy efficiency. To solve the DAC-LRT problem in dynamic and large-scale IoE environments, we design a Multi-Agent Distributed Deep Deterministic Policy Gradient (MAD3PG) algorithm. MAD3PG enables decentralized and adaptive policy learning by allowing UAVs to derive optimal actions directly from environmental observations. Simulation results demonstrate that our proposed approach significantly outperforms state-of-the-art methods, achieving improvements of 82.92%, 78.02%, and 71.9% in downlink capacity, coverage ratio, average throughput, and spectral efficiency over Deep Deterministic Policy Gradient (DDPG), Asynchronous Advantage Actor-Critic (A3C), and Greedy algorithms, respectively.