Vision-Aided ISAC in Low-Altitude Economy Networks via De-Diffused Visual Priors
Yulan Gao, Ziqiang Ye, Zhonghao Lyu, Ming Xiao, Yue Xiao, Ping Yang, Agata Manolova

TL;DR
This paper introduces a vision-aided ISAC framework for UAV-based low-altitude networks, combining visual priors and radar data to optimize resource control while preserving privacy.
Contribution
It proposes a novel De-Diffusion model for extracting semantic tokens and a two-stage control algorithm for joint resource optimization in low-altitude networks.
Findings
Achieves within 4% of raw-image upper bound performance.
Outperforms baselines in reward, robustness, and semantic fidelity.
Preserves user privacy and scalability in dense environments.
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…
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Taxonomy
TopicsUAV Applications and Optimization · Vehicular Ad Hoc Networks (VANETs) · Mobile Crowdsensing and Crowdsourcing
