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This research applies Decision Transformers to optimize UAV trajectories and RIS phase shifts for dynamic D2D communications, leveraging deep reinforcement learning to solve complex joint optimization problems under mobility constraints. The study highlights the model's ability to generalize across scenarios via zero-shot transfer, reducing the need for extensive online fine-tuning compared to standard direct DRL approaches.
Decision Transformer for UAV-Mounted RIS-Assisted Dynamic D2D Communications
arXiv:2609.09885v1 Announce Type: new Abstract: This paper studies unmanned aerial vehicle (UAV)-mouted reconfigurable intelligent surface (RIS)-assisted device-to-device (D2D) communication with stochastic link activation. It models UAV motion and attitude, time-varying Rician angles, and angle-dependent RIS reflection. A joint optimization of UAV trajectory, attitude, and RIS phases is formulated to maximize average sum rate under mobility, energy, and hardware constraints. The problem is addressed using deep reinforcement learning and a Decision Transformer trained on expert trajectories from multiple scenarios. Results demonstrate effective cross-scenario generalization, with zero-shot transfer outperforming direct DRL transfer and online fine-tuning achieving competitive performance with fewer interactions.
Abstract:This paper studies unmanned aerial vehicle (UAV)-mouted reconfigurable intelligent surface (RIS)-assisted device-to-device (D2D) communication with stochastic link activation. It models UAV motion and attitude, time-varying Rician angles, and angle-dependent RIS reflection. A joint optimization of UAV trajectory, attitude, and RIS phases is formulated to maximize average sum rate under mobility, energy, and hardware constraints. The problem is addressed using deep reinforcement learning and a Decision Transformer trained on expert trajectories from multiple scenarios. Results demonstrate effective cross-scenario generalization, with zero-shot transfer outperforming direct DRL transfer and online fine-tuning achieving competitive performance with fewer interactions.
Artificial Intelligence (cs.AI); Robotics (cs.RO)
arXiv:2609.09885 [cs.AI]
(or arXiv:2609.09885v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.09885 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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