Enhancing Secrecy in UAV RSMA Networks: Deep Unfolding Meets Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2310.01437v1
• Date: Sat, 30 Sep 2023 12:26:24 GMT
• Title: Enhancing Secrecy in UAV RSMA Networks: Deep Unfolding Meets Deep Reinforcement Learning
• Authors: Abuzar B. M. Adam, Mohammed A. M. Elhassan,
• Abstract summary: We consider the network of the secrecy in multiple unmanned aerial vehicles (UAV) rate trajectory (SMAR) The proposed deep reinforcement learning (DRL) has shown great performance and outperformed other DRL-based methods in the literature.
• Score: 0.8287206589886881
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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 deterministic policy gradient (DDPG) for the learning procedure. The proposed DUN-DRL have shown great performance and outperformed other DRL-based methods in the literature.

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