Joint Placement Optimization and Sum Rate Maximization of RIS-Assisted UAV with LEO-Terrestrial Dual Wireless Backhaul

Achieving ubiquitous coverage in 6G networks presents significant challenges due to the limitations of high-frequency signals and the need for extensive infrastructure, and providing seamless connectivity in remote and rural areas remains a challenge. We propose an integrated optimization framework for UAV-LEO-RIS-assisted wireless networks, aiming to maximize system sum rate through the strategic placement and configuration of Unmanned Aerial Vehicles (UAVs), Low Earth Orbit (LEO) satellites, and Reconfigurable Intelligent Surfaces (RIS). The framework employs a dual wireless backhaul and utilizes a grid search method for UAV placement optimization, ensuring a comprehensive evaluation of potential positions to enhance coverage and data throughput. Simulated Annealing (SA) is utilized for RIS placement optimization, effectively navigating the solution space to identify configurations that improve signal reflection and network performance. For sum rate maximization, we incorporate several metaheuristic algorithms, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Grey Wolf Optimization (GWO), Salp Swarm Algorithm (SSA), Marine Predators Algorithm (MPA), and a hybrid PSO-GWO approach. Simulation results demonstrate that the hybrid PSO-GWO algorithm outperforms individual metaheuristics in terms of convergence speed and achieving a higher sum rate. The coverage improves from 62% to 100%, and the results show an increase in spectrum efficiency of 23.7%.