Enhancing Secrecy in UAV RSMA Networks: Deep Unfolding Meets Deep Reinforcement Learning. (arXiv:2310.01437v1 [cs.CR])

In this paper, we consider the maximization of the secrecy rate in multiple
unmanned aerial vehicles (UAV) rate-splitting multiple access (RSMA) network. A
joint beamforming, rate allocation, and UAV trajectory optimization problem is
formulated which is nonconvex. Hence, the problem is transformed into a Markov
decision problem and a novel multiagent deep reinforcement learning (DRL)
framework is designed. The proposed framework (named DUN-DRL) combines deep
unfolding to design beamforming and rate allocation, data-driven to design the
UAV trajectory, and deep …

access decision network networks optimization problem rate secrecy trajectory vehicles