Performance Optimization of UAV-RIS-assisted Communication Networks Under No-Fly Zone Constraints

ObjectiveReconfigurable Intelligent Surfaces (RIS) mounted on Unmanned Aerial Vehicles (UAVs) are considered an effective approach to enhance wireless communication coverage and adaptability in complex or constrained environments. However, two major challenges remain in practical deployment. The existence of No-Fly Zones (NFZs), such as airports, government facilities, and high-rise areas, restricts the UAV flight trajectory and may result in communication blind spots. In addition, the continuous attitude variation of UAVs during flight causes dynamic misalignment between the RIS and the desired reflection direction, which reduces signal strength and system throughput. To address these challenges, a UAV-RIS-assisted communication framework is proposed that simultaneously considers NFZ avoidance and UAV attitude adjustment. In this framework, a quadrotor UAV equipped with a bottom-mounted RIS operates in an environment containing multiple polygonal NFZs and a group of Ground Users (GUs). The aim is to jointly optimize the UAV trajectory, RIS phase shift, UAV attitude (represented by Euler angles), and Base Station (BS) beamforming to maximize the system sum rate while ensuring complete obstacle avoidance and stable, high-quality service for GUs located both inside and outside NFZs.MethodsTo achieve this objective, a multi-variable coupled non-convex optimization problem is formulated, jointly capturing UAV trajectory, RIS configuration, UAV attitude, and BS beamforming under NFZ constraints. The RIS phase shifts are dynamically adjusted according to the UAV orientation to maintain beam alignment, and UAV motion follows quadrotor dynamics while avoiding polygonal NFZs. Because of the high dimensionality and non-convexity of the problem, conventional optimization approaches are computationally intensive and lack real-time adaptability. To address this issue, the problem is reformulated as a Markov Decision Process (MDP), which enables policy learning through deep reinforcement learning. The Soft Actor-Critic (SAC) algorithm is employed, leveraging entropy regularization to improve exploration efficiency and convergence stability. The UAV-RIS agent interacts iteratively with the environment, updating actor-critic networks to determine UAV position, RIS phase configuration, and BS beamforming. Through continuous learning, the proposed framework achieves higher throughput and reliable NFZ avoidance, outperforming existing benchmarks.Results and DiscussionsAs shown in (Fig. 3), the proposed SAC algorithm achieves higher communication rates than PPO, DDPG, and TD3 during training, benefiting from entropy-regularized exploration that prevents premature convergence. Although DDPG converges faster, it exhibits instability and inferior long-term performance. As illustrated in (Fig. 4), the UAV trajectories under different conditions demonstrate the proposed algorithm’s capability to achieve complete obstacle avoidance while maintaining reliable communication. Regardless of variations in initial UAV positions, BS locations, or NFZ configurations, the UAV consistently avoids all NFZs and dynamically adjusts its trajectory to serve users located both inside and outside restricted zones, indicating strong adaptability and scalability of the proposed model. As shown in (Fig. 5), increasing the number of BS antennas enhances system performance. The proposed framework significantly outperforms fixed phase shift, random phase shift, and non-RIS schemes because of improved beamforming flexibility.ConclusionsThis paper investigates a UAV-RIS-assisted wireless communication system in which a quadrotor UAV carries an RIS to enhance signal reflection and ensure NFZ avoidance. Unlike conventional approaches that emphasize avoidance alone, a path integral-based method is proposed to generate obstacle-free trajectories while maintaining reliable service for GUs both inside and outside NFZs. To improve generality, NFZs are represented as prismatic obstacles with regular n-sided polygonal cross-sections. The system jointly optimizes UAV trajectory, RIS phase shifts, UAV attitude, and BS beamforming. A DRL framework based on the SAC algorithm is developed to enhance system efficiency. Simulation results demonstrate that the proposed approach achieves reliable NFZ avoidance and maximized sum rate, outperforms benchmarks in communication performance, scalability, and stability.