Е-Публикации
Технически университет - София

Abstract: Emerging low-altitude economy networks require agile and privacy-preserving resource control under dynamic agent mobility and limited infrastructure support. To address these challenges, we propose a vision-aided integrated sensing and communication framework for intelligent aerial agent-assisted access systems, where onboard masked De-Diffusion models extract compact semantic tokens, including agent type, activity class, and heading orientation, while explicitly suppressing sensitive visual content. These tokens are fused with mmWave radar measurements to construct a semantic risk heatmap reflecting motion density, occlusion, and scene complexity, which guides access technology selection and resource scheduling. We formulate a multi-objective optimization problem to jointly maximize weighted energy and perception efficiency via radio access technology (RAT) assignment, power control, and beamforming, subject to agent-specific QoS constraints. To solve it, we develop De-Diffusion-driven vision-aided risk-aware resource optimization algorithm (DeDiff-VARARO), a novel two-stage cross-modal control algorithm: the first stage reconstructs visual scenes from tokens via De-Diffusion model for semantic parsing, while the second stage employs a deep deterministic policy gradient-based policy to adapt RAT selection, power control, and beam assignment based on fused radar-visual states. Simulation results show that DeDiff-VARARO consistently outperforms baselines in reward convergence, link robustness, and semantic fidelity, achieving within 4% of the performance of a raw-image upper bound while preserving user privacy and scalability in dense environments.

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