Integrated satellite–aerial networks (ISANs) are emerging as a promising architecture that combines high-throughput inter-satellite transmission with the agility of uncrewed aerial vehicles (UAVs) to support flexible and low-latency traffic delivery. Owing to the inherently uneven traffic distribution in the satellite layer, traffic flows often suffer from congestion and excessive multi-hop forwarding delays. UAVs can act as adaptive relays to offload congested traffic and mitigate routing detours, thereby reducing end-to-end latency. However, latency-aware traffic management in ISANs is fundamentally challenged by highly dynamic satellite topologies, heterogeneous link characteristics, and the tight coupling between satellite traffic dynamics and UAV mobility. Existing approaches often suffer from cross-layer misalignment between satellite routing and aerial relaying, which limits coordinated latency adaptation. To address these challenges, this paper proposes an agentic UAV-assisted relay framework, termed DUS-SACUD, in which an autonomous UAV acts as an embodied agent that proactively steers traffic. First, a graph-conditioned diffusion model is developed for generative UAV–satellite link (USL) selection under dynamic network states. Second, a soft actor–critic-based reinforcement learning scheme is employed for embodied UAV deployment to minimize USL-induced delay. Through closed-loop alternating execution, DUS-SACUD jointly optimizes connectivity adaptation and mobility control in ISANs. Extensive simulations based on a realistic satellite constellation demonstrate significant end-to-end latency reduction over existing routing and UAV-assisted baselines, while maintaining robust performance under diverse ISAN conditions.
Xintong Li, Feng Wang, Qi Wu et al.· IEEE Transactions on Cogniti...· 0 citations
This letter considers a secure multi-uncrewed aerial vehicle (UAV) enabled over-the-air computation (AirComp) system, where multiple UAVs cooperatively transmit data to a ground fusion center (FC) via AirComp under eavesdropping threats. To achieve reliable and secure aggregation, we jointly optimize the UAV transmit power, FC denoising factor, and UAV trajectories to minimize the mean square error (MSE) at the FC while enforcing the eavesdropper (EVE) MSE, UAV power, and mobility constraints. The formulated problem is non-convex due to coupled variables and mobility constraints. An efficient algorithm combining alternating optimization and successive convex approximation (SCA) is proposed to obtain a stationary solution. Numerical results verify the effectiveness of the proposed scheme and its superiority over benchmark schemes.
Jianping Yao, Yuxia Gong, Sunan Wang et al.· IEEE Wireless Communications...· 0 citations