{"ID":2848121,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.26628","arxiv_id":"2510.26628","title":"Low-Altitude UAV-Carried Movable Antenna for Joint Wireless Power Transfer and Covert Communications","abstract":"The proliferation of Internet of Things (IoT) networks has created an urgent need for sustainable energy solutions, particularly for the battery-constrained spatially distributed IoT nodes. While low-altitude uncrewed aerial vehicles (UAVs) employed with wireless power transfer (WPT) capabilities offer a promising solution, the line-of-sight channels that facilitate efficient energy delivery also expose sensitive operational data to adversaries. This paper proposes a novel low-altitude UAV-carried movable antenna-enhanced transmission system joint WPT and covert communications, which simultaneously performs energy supplements to IoT nodes and establishes transmission links with a covert user by leveraging wireless energy signals as a natural cover. Then, we formulate a multi-objective optimization problem that jointly maximizes the total harvested energy of IoT nodes and sum achievable rate of the covert user, while minimizing the propulsion energy consumption of the low-altitude UAV. To address the non-convex and temporally coupled optimization problem, we propose a mixture-of-experts-augmented soft actor-critic (MoE-SAC) algorithm that employs a sparse Top-K gated mixture-of-shallow-experts architecture to represent multimodal policy distributions arising from the conflicting optimization objectives. We also incorporate an action projection module that explicitly enforces per-time-slot power budget constraints and antenna position constraints. Simulation results demonstrate that the proposed approach significantly outperforms some baseline approaches and other state-of-the-art deep reinforcement learning algorithms.","short_abstract":"The proliferation of Internet of Things (IoT) networks has created an urgent need for sustainable energy solutions, particularly for the battery-constrained spatially distributed IoT nodes. While low-altitude uncrewed aerial vehicles (UAVs) employed with wireless power transfer (WPT) capabilities offer a promising solu...","url_abs":"https://arxiv.org/abs/2510.26628","url_pdf":"https://arxiv.org/pdf/2510.26628v1","authors":"[\"Chuang Zhang\",\"Geng Sun\",\"Jiahui Li\",\"Jiacheng Wang\",\"Qingqing Wu\",\"Dusit Niyato\",\"Shiwen Mao\",\"Tony Q. S. Quek\"]","published":"2025-10-30T15:55:28Z","proceeding":"cs.NI","tasks":"[\"cs.NI\",\"eess.SP\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}