The amalgamation of Reconfigurable Intelligent Surface (RIS) and Non-Orthogonal Multiple Access (NOMA) within Integrated Sensing and Communication (ISAC) frameworks is a key enabler to enhance spectral efficiency in 6G networks. Nevertheless, the broadcast nature of dual-functional signals that simultaneously support communication and sensing introduces physical-layer security vulnerabilities, especially in sensor networks. This paper addresses this critical challenge by investigating secure beamforming design in an RIS-NOMA-ISAC system. We formulate an optimization problem to maximize the total secrecy rate of all users while ensuring that the radar sensing performance satisfies a required minimum signal-to-noise ratio (SNR). To tackle this non-convex problem, we develop an iterative algorithm based on the alternating optimization (AO) framework, which reduces computational complexity. Specifically, the secrecy rate maximization problem is decomposed into two subproblems. The non-convex objective function and constraints are handled by the successive convex approximation (SCA) technique, which transforms them into a series of second-order cone constraints and linear constraints. As a result, the originally non-convex problem is converted into a convex one that can be efficiently solved. Simulation results demonstrate that the proposed algorithm achieves satisfactory security performance while ensuring effective radar sensing, and outperforms benchmark schemes with random RIS phase shifts and without RIS assistance.
Yifeng Zhao, Hongzheng Wang, Peng Lu et al.· ACM transactions on sensor n...· 0 citations
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
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
