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

XU Junjie; LI Bin; YANG Jingsong

doi:10.11999/JEIT250681

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XU Junjie, LI Bin, YANG Jingsong. Performance Optimization of UAV-RIS-Assisted Communication Networks Under No-Fly Zone Constraints[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250681

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XU Junjie, LI Bin, YANG Jingsong. Performance Optimization of UAV-RIS-Assisted Communication Networks Under No-Fly Zone Constraints[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250681

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

doi: 10.11999/JEIT250681 cstr: 32379.14.JEIT250681

1.
Waterford Institute, Nanjing University of Information Science and Technology, Nanjing 210044, China
2.
School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China
3.
Hengxing University, Qingdao 266000, China

Funds: The National Natural Science Foundation of China (62101277), Open Project of Guangxi Key Laboratory of Functional Information Materials and Intelligent Information Processing (XXGN202508)

Received Date: 2025-07-21
Accepted Date: 2025-11-03
Rev Recd Date: 2025-11-03

Available Online: 2025-11-12

Abstract

Abstract

Objective Reconfigurable Intelligent Surfaces (RIS) mounted on Unmanned Aerial Vehicles (UAVs) have emerged as a promising solution to enhance wireless communication coverage and adaptability in complex or constrained environments. However, in practical deployments, two major challenges remain largely underexplored. First, the existence of No-Fly Zones (NFZs), such as airports, government facilities, and high-rise areas, significantly restricts the UAV’s flight trajectory and may lead to communication blind spots. Second, the continuous attitude variation of UAVs during flight causes dynamic misalignment between the RIS and the desired reflection direction, which significantly degrades signal strength and system throughput. To address these issues, this paper proposes a comprehensive UAV-RIS-assisted communication framework that simultaneously considers NFZ avoidance and UAV attitude adjustment. Specifically, this paper studies a quadrotor UAV with a bottom-mounted RIS, operating in an environment with multiple polygonal NFZs and a group of ground users (GUs). The objective is to jointly optimize the UAV trajectory, RIS phase shift, UAV attitude (expressed via Euler angles), and base station (BS) beamforming, with the aim of maximizing the system sum rate while ensuring complete obstacle avoidance and high-quality service for GUs located both inside and outside the NFZs. Methods To 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. RIS phase shifts are dynamically adjusted based on the UAV’s orientation to maintain beam alignment, while UAV motion follows quadrotor dynamics and avoids polygonal NFZs. Owing to the high dimensionality and non-convexity, conventional optimization methods are computationally prohibitive and lack real-time adaptability. To overcome this, the problem is reformulated as a Markov Decision Process (MDP), enabling policy learning via deep reinforcement learning. Specifically, this paper adopts the Soft Actor-Critic (SAC) algorithm, which leverages entropy regularization for efficient exploration and stable convergence. The UAV-RIS agent iteratively interacts with the environment, updating actor-critic networks to determine UAV position, RIS phases, and beamforming. Through continuous learning, the framework achieves higher throughput with guaranteed NFZ avoidance, outperforming benchmarks. Results and Discussions As 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 mitigates premature convergence. While DDPG converges faster, it exhibits instability and inferior long-term performance. As illustrated in (Fig. 4), the UAV trajectories under different settings confirm the proposed algorithm’s ability to achieve complete obstacle avoidance while maintaining reliable communication. Regardless of changes in initial UAV positions, BS locations, or NFZ configurations, the UAV consistently avoids all NFZs and adjusts its trajectory to serve users both inside and outside restricted zones, demonstrating strong adaptability and scalability of the proposed model. In (Fig. 5), it shows that increasing the number of BS antennas enhances system performance. The proposed scheme significantly outperforms fixed phase shift, random phase shift and without RIS methods due to improved beamforming flexibility. Conclusions This paper investigates a UAV-RIS-assisted wireless communication system, where a quadrotor UAV carries a RIS for signal reflection and NFZ avoidance. Unlike conventional methods focusing only on avoidance, a path integral-based approach is proposed to ensure obstacle-free trajectories while maintaining reliable service for GUs inside and outside NFZs. To enhance generality, NFZs are modeled as prismatic obstacles with regular n-sided polygonal cross-sections. The system jointly considers UAV trajectory, RIS phase shifts, UAV attitude, and BS beamforming. A DRL framework with SAC is developed to optimize system efficiency. Simulations show that the proposed method achieves reliable avoidance and maximized sum rate, and it outperforms benchmarks in communication performance, scalability, and stability.
- Unmanned Aerial Vehicle(UAV),
- Reconfigurable Intelligent Surface(RIS),
- No Fly Zones(NFZs),
- Attitude adjustment,
- Deep reinforcement learning

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References(15)

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