Recovery of UAV Swarm-enabled Collaborative Beamforming in Low-altitude Wireless Networks under Wind Field Disturbances

TL;DR

This paper presents a novel adaptive control framework using PPO-LA for UAV swarm beamforming in low-altitude wireless networks, effectively mitigating wind disturbances and enhancing communication reliability.

Contribution

It introduces a new PPO-LA algorithm that models wind effects and adaptively optimizes UAV beamforming in real-time, improving robustness over existing methods.

Findings

PPO-LA effectively recovers beamforming performance under various wind conditions.

The proposed method outperforms benchmark algorithms in simulation.

Adaptive control maintains high directivity and low sidelobe levels.

Abstract

Unmanned aerial vehicle (UAV) swarms utilizing collaborative beamforming (CB) in low-altitude wireless networks (LAWN) demonstrate significant potential for enhanced communication range, energy efficiency, and signal directivity through the formation of virtual antenna arrays (VAA). However, environmental disturbances, particularly wind fields, significantly degrade CB performance by introducing positional errors that disrupt beam patterns, thereby compromising transmission reliability. This paper investigates the critical challenge of maintaining CB performance in UAV-based VAAs operating in LAWN under wind field disturbances. We propose a comprehensive framework that models the impact of three distinct wind conditions (constant, shear, and turbulent) on UAV array performance, and formulate a long-term real-time optimization problem to maximize directivity while minimizing maximum sidelobe levels through adaptive excitation current weight adjustments. To address the inherent complexity of this problem, we propose a novel proximal policy optimization algorithm with long short-term memory (LSTM) structure and adaptive learning rate (PPO-LA), which effectively captures temporal patterns in wind field disturbances and enables real-time adaptation without requiring extensive prior training for specific wind conditions. Our simulation results demonstrate that the proposed PPO-LA algorithm successfully recovers degraded CB performance across various wind scenarios, and thus significantly outperforming benchmark algorithms.