Low-Altitude UAV-Carried Movable Antenna for Joint Wireless Power Transfer and Covert Communications
Chuang Zhang, Geng Sun, Jiahui Li, Jiacheng Wang, Qingqing Wu, Dusit Niyato, Shiwen Mao, Tony Q. S. Quek

TL;DR
This paper introduces a low-altitude UAV system with movable antennas that jointly optimizes wireless power transfer and covert communications to enhance IoT network sustainability and security, using a novel deep reinforcement learning approach.
Contribution
It proposes a joint WPT and covert communication system with a new MoE-SAC algorithm for complex multi-objective optimization in UAV-assisted IoT networks.
Findings
The proposed method outperforms baseline algorithms in energy harvesting and covert communication rates.
The MoE-SAC algorithm effectively handles conflicting objectives and constraints.
Simulation results validate the superiority of the proposed approach over existing methods.
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…
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