Low-Altitude UAV-Carried Movable Antenna for Joint Wireless Power Transfer and Covert Communications

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 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.