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.
Zhi-Xin Liu, Zhi-Cheng Liu, Yuan-Ai Xie et al.· IEEE Transactions on Communi...· 0 citations
Low-altitude uncrewed aerial vehicle (UAV) communication offers notable advantages over terrestrial base stations in terms of flexibility and deployment efficiency. However, the high likelihood of line-of-sight (LoS) propagation renders the communication links between UAVs and ground users (GUs) particularly susceptible to eavesdropping. To address this issue, we consider an intelligent reflecting surface (IRS)-assisted low-altitude UAV secure communication system, in which communication security is strengthened through adaptive control of the wireless propagation environment, even when eavesdroppers are present. We aim to maximize the secrecy rate of GUs while minimizing the UAV energy consumption by jointly optimizing the continuous UAV trajectory, power allocation, and discrete IRS phase shifts. Considering the dynamic, non-convex, and NP-hard nature of the optimization problem, we propose an agentic artificial intelligence (AI) approach, namely alternating optimization (AO) and generative diffusion model-based deep deterministic policy gradient (AO-GDMDDPG) approach. The proposed agentic AI approach is composed of two cooperative agents that operate over a hybrid and high-dimensional decision space, in which the UAV agent adopts a generative AI (GenAI)-enhanced deep reinforcement learning (DRL) method to optimize continuous decision variables, whereas the IRS agent relies on the AO method to determine discrete IRS phase shifts. Simulation results demonstrate the superiority of the AO-GDMDDPG approach over benchmark algorithms with respect to secrecy rate improvement and UAV energy consumption reduction.
Wenwen Xie, Geng Sun, Jiahui Li et al.· IEEE Transactions on Cogniti...· 0 citations
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