Vision-Aided ISAC in Low-Altitude Economy Networks via De-Diffused Visual Priors
Journal:
arXiv
Published Date:
Jul 2, 2025
Abstract
Emerging low-altitude economy networks (LAENets) require agile and
privacy-preserving resource control under dynamic agent mobility and limited
infrastructure support. To meet these challenges, we propose a vision-aided
integrated sensing and communication (ISAC) framework for UAV-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 this, 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 (DDPG)-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.
