Vision-Aided ISAC in Low-Altitude Economy Networks via De-Diffused Visual Priors
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866918079976439808 |
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| author | Gao, Yulan Ye, Ziqiang Lyu, Zhonghao Xiao, Ming Xiao, Yue Yang, Ping Manolova, Agata |
| author_facet | Gao, Yulan Ye, Ziqiang Lyu, Zhonghao Xiao, Ming Xiao, Yue Yang, Ping Manolova, Agata |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_01574 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Vision-Aided ISAC in Low-Altitude Economy Networks via De-Diffused Visual Priors Gao, Yulan Ye, Ziqiang Lyu, Zhonghao Xiao, Ming Xiao, Yue Yang, Ping Manolova, Agata Systems and Control 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. |
| title | Vision-Aided ISAC in Low-Altitude Economy Networks via De-Diffused Visual Priors |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2507.01574 |