Joint Hybrid Beamforming and Artificial Noise Design for Secure Multi-UAV ISAC Networks
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915674088013824 |
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| author | Dong, Runze Wang, Buhong Feng, Cunqian Weng, Jiang Han, Chen Tian, Jiwei |
| author_facet | Dong, Runze Wang, Buhong Feng, Cunqian Weng, Jiang Han, Chen Tian, Jiwei |
| contents | Integrated sensing and communication (ISAC) emerges as a key enabler for next-generation applications such as smart cities and autonomous systems. Its integration with unmanned aerial vehicles (UAVs) unlocks new potentials for reliable communication and precise sensing in dynamic aerial environments. However, existing research predominantly treats UAVs as aerial base stations, overlooking their role as ISAC users, and fails to leverage large-scale antenna arrays at terrestrial base stations to enhance security and spectral efficiency. This paper propose a secure and spectral efficient ISAC framework for multi-UAV networks, and a two-stage optimization approach is developed to jointly design hybrid beamforming (HBF), artificial noise (AN) injection, and UAV trajectories. Aiming at maximizing the sum secrecy rate, the first stage employs Proximal Policy Optimization (PPO) to optimize digital beamformers and trajectories, and the second stage decomposes the digital solution into analog and digital components via low-complexity matrix factorization. Simulation results demonstrate the effectiveness of the proposed framework compared to benchmark schemes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_23687 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Joint Hybrid Beamforming and Artificial Noise Design for Secure Multi-UAV ISAC Networks Dong, Runze Wang, Buhong Feng, Cunqian Weng, Jiang Han, Chen Tian, Jiwei Signal Processing Artificial Intelligence Integrated sensing and communication (ISAC) emerges as a key enabler for next-generation applications such as smart cities and autonomous systems. Its integration with unmanned aerial vehicles (UAVs) unlocks new potentials for reliable communication and precise sensing in dynamic aerial environments. However, existing research predominantly treats UAVs as aerial base stations, overlooking their role as ISAC users, and fails to leverage large-scale antenna arrays at terrestrial base stations to enhance security and spectral efficiency. This paper propose a secure and spectral efficient ISAC framework for multi-UAV networks, and a two-stage optimization approach is developed to jointly design hybrid beamforming (HBF), artificial noise (AN) injection, and UAV trajectories. Aiming at maximizing the sum secrecy rate, the first stage employs Proximal Policy Optimization (PPO) to optimize digital beamformers and trajectories, and the second stage decomposes the digital solution into analog and digital components via low-complexity matrix factorization. Simulation results demonstrate the effectiveness of the proposed framework compared to benchmark schemes. |
| title | Joint Hybrid Beamforming and Artificial Noise Design for Secure Multi-UAV ISAC Networks |
| topic | Signal Processing Artificial Intelligence |
| url | https://arxiv.org/abs/2509.23687 |