Flexible Reconfigurable Intelligent Surface-Aided Covert Communications in UAV Networks

TL;DR

This work tackles covert communications in UAV networks by integrating flexible reconfigurable intelligent surfaces (F-RIS) that can conform to curved/deformed surfaces and actively reshape the electromagnetic environment. It develops an EM-F-RIS model with angle-dependent reflection properties and a fitted relation between reflection amplitude, phase, and incident angle, enabling precise control of the propagation channel. A joint optimization is formulated to maximize the covert rate at a legitimate UAV (Bob) while satisfying a public-rate constraint for a ground user (Carol), using NOMA and time-varying UAV trajectories. A DSAC-based deep reinforcement learning algorithm learns a policy to optimize UAV paths, F-RIS deformations, phase shifts, and power allocation under long-term, non-convex constraints, demonstrating significant performance gains over baselines and highlighting F-RIS’s potential for robust, covert UAV communications in dynamic scenarios.

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.

Figures (11)

• Figure 1: Covert communications in F-RIS-aided UAV networks..
• Figure 2: Transformation of an arbitrary incident wavefront into a desired scattering profile using a transversely inhomogeneous metasurface.
• Figure 3: The schematic of the unit cell of the proposed RIS in 2bit and its equivalent circuit model.
• Figure 4: Illustration of F-RIS.
• Figure 5: F-RIS experimentation platform.