Joint Power and Trajectory Optimization for NOMA-Enabled Covert UAV Networks

Abstract

Covert Communication (CC) has emerged as a vital paradigm for 6G security, offering protection against eavesdropping without sole reliance on upper-layer encryption. Using their strong mobility and flexible deployment, Unmanned Aerial Vehicles (UAVs) can serve as the ideal platforms for CC. However, UAV mobility and multi-user interference in Non-Orthogonal Multiple Access (NOMA) enabled UAV networks degrade system performance. This paper investigates a robust joint power and trajectory optimization framework designed to secure NOMA-enabled system against an Eavesdropper (Eve) with uncertain locations. To determine the covertness constraint, we first derive the closed-form expressions for the optimal normalized detection threshold at Eve and the minimum total detection error probability. Given the unfair distribution of resources imposed by UAV mobility, we formulate a robust optimization problem with the objective of maximizing the minimum average covert transmission rate to guarantee a baseline quality of service for all users. To further address the impact of UAV mobility on the successive interference cancellation decoding order, we introduce binary variables to dynamically model the strong-weak channel relationships among users. Since the formulated problem is non-convex and intractable, we utilize auxiliary variables and the convex-concave procedure to transform it into a tractable form. To solve this problem, we then propose a joint optimization scheme based on penalty dual decomposition algorithm, which iteratively optimizes trajectory, power, and resource allocation via a dual-loop mechanism. Numerical simulations demonstrate the effectiveness of the proposed joint optimization scheme.