arXiv AI By Yaxuan Liu

Decision Transformer for UAV-Mounted RIS-Assisted Dynamic D2D Communications

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The paper investigates UAV‑mounted reconfigurable intelligent surface (RIS) assisted device‑to‑device (D2D) communication with stochastic link activation. It models UAV motion, attitude, time‑varying Rician angles, and angle‑dependent RIS reflection, and formulates a joint optimization of UAV trajectory, attitude, and RIS phases to maximize average sum rate under mobility, energy, and hardware constraints. The authors employ deep reinforcement learning and a Decision Transformer trained on expert trajectories from multiple scenarios, showing that zero‑shot transfer outperforms direct DRL transfer and that online fine‑tuning achieves competitive performance with fewer interactions.

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arXiv AI
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Diffusion Models for Smarter UAVs: Decision-Making and Modeling

The paper discusses how Diffusion Models (DMs) can improve decision-making and digital modeling for Uncrewed Aerial Vehicles (UAVs). It highlights the limitations of Reinforcement Learning (RL) and Digital Twin (DT) approaches, noting that DMs learn underlying probability distributions and generate realistic patterns, thereby addressing data scarcity and enhancing modeling accuracy. Simulation results demonstrate DMs’ effectiveness in estimating neighbor velocities for a four‑UAV swarm coordination task using Deep Reinforcement Learning.

By Yousef Emami, Hao Zhou, Luis Almeida, Kai Li
arXiv AI
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By Nguyen Duc Minh Quang, Chang Liu, Shuangyang Li, Derrick Wing Kwan Ng