Joint 3D Trajectory and High-Dimensional PhaseShift Optimization for Reconfigurable Intelligent Surface (RIS)-Assisted UAV Networks: A Deep Reinforcement Learning Approach
Abstract
This paper investigates the joint optimization of a three-dimensional (3D) rotary-wing unmanned aerial vehicle (UAV) trajectory and the high-dimensional phase-shift vector of a large reconfigurable intelligent surface (RIS) deployed on offshore energy infrastructure. Motivated by India’s emerging offshore wind program, a cascaded air-to-ground/ground-to-air channel model and a propulsion-aware energy consumption model are combined to formulate a long-horizon cumulative energy-efficiency (EE) maximization problem under mobility and unit-modulus RIS constraints. A deep deterministic policy gradient (DDPG) framework is adopted to learn continuous control policies for both UAV motion and RIS phases. Representative plots demonstrate convergence behavior, trajectory characteristics, EE scalability with the number of RIS elements, and throughput sensitivity to UAV speed. A real-world case study is constructed around India’s planned 500 MW offshore wind project site in the Gulf of Khambhat (Gujarat), consistent with national policy and tender documents.
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