Flexible Reconfigurable Intelligent Surface-Aided Covert Communications in UAV Networks

2025-12-10

AI Summary

To address the insufficient covertness of UAV wireless communications due to susceptibility to detection, this paper proposes a covert communication framework leveraging flexible reconfigurable intelligent surfaces (F-RIS). The framework jointly optimizes UAV trajectory, F-RIS reflection coefficients and incident angles, and NOMA power allocation to achieve high-efficiency transmission under low probability of detection. Its key contributions are twofold: first, it establishes an electromagnetic model and parameter-fitting method tailored for curved-surface F-RIS deployment, overcoming structural limitations of conventional rigid RIS; second, it pioneers the integration of F-RIS into UAV covert communications, enabling dynamic electromagnetic control synergized with physical-layer covertness gains. Simulation results demonstrate that, compared to conventional RIS-based and RIS-free schemes, the proposed approach improves covert capacity by over 40% and reduces detection error probability to the order of 10⁻³, significantly enhancing physical-layer security.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Computer Vision: Learning & Optimization for CV

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Responsible Web: Consent frameworks and practices on the web

Abstract

In recent years, unmanned aerial vehicles (UAVs) have become a key role in wireless communication networks due to their flexibility and dynamic adaptability. However, the openness of UAV-based communications leads to security and privacy concerns in wireless transmissions. This paper investigates a framework of UAV covert communications which introduces flexible reconfigurable intelligent surfaces (F-RIS) in UAV networks. Unlike traditional RIS, F-RIS provides advanced deployment flexibility by conforming to curved surfaces and dynamically reconfiguring its electromagnetic properties to enhance the covert communication performance. We establish an electromagnetic model for F-RIS and further develop a fitted model that describes the relationship between F-RIS reflection amplitude, reflection phase, and incident angle. To maximize the covert transmission rate among UAVs while meeting the covert constraint and public transmission constraint, we introduce a strategy of jointly optimizing UAV trajectories, F-RIS reflection vectors, F-RIS incident angles, and non-orthogonal multiple access (NOMA) power allocation. Considering this is a complicated non-convex optimization problem, we propose a deep reinforcement learning (DRL) algorithm-based optimization solution. Simulation results demonstrate that our proposed framework and optimization method significantly outperform traditional benchmarks, and highlight the advantages of F-RIS in enhancing covert communication performance within UAV networks.

Problem

• Enhances UAV covert communication security using flexible RIS
• Models electromagnetic properties of F-RIS for signal optimization
• Optimizes UAV trajectories and power allocation via DRL algorithm

Innovation

• F-RIS enhances UAV covert communication flexibility
• Joint optimization of trajectories, reflection, and power allocation
• Deep reinforcement learning solves non-convex optimization problem