Joint Hybrid Beamforming and Artificial Noise Design for Secure Multi-UAV ISAC Networks
This work addresses security and efficiency challenges for UAV-based ISAC networks, which is incremental as it builds on existing ISAC and UAV research by incorporating new optimization methods.
The paper tackled the problem of secure and spectral efficient integrated sensing and communication in multi-UAV networks by proposing a joint optimization of hybrid beamforming, artificial noise, and UAV trajectories, resulting in improved sum secrecy rates as demonstrated in simulations.
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.