Movable-Antenna Empowered AAV-Enabled Data Collection over Low-Altitude Wireless Networks

2025-07-21

AI Summary

This work addresses the uplink data collection problem for ground users in low-altitude wireless networks (LAWNs) enabled by autonomous aerial vehicles (AAVs) equipped with movable antennas (MAs), aiming to maximize the total achievable rate. We propose a joint optimization framework that co-designs the AAV’s three-dimensional trajectory, receive beamforming, and MA positioning. To overcome limitations of conventional fixed-antenna architectures, we introduce adaptive beam–user alignment and spatial interference management. An alternating optimization algorithm is developed, integrating successive convex approximation (SCA), weighted minimum mean-square error (WMMSE), and particle swarm optimization (PSO) for efficient convergence. Simulation results demonstrate that the proposed scheme significantly outperforms multiple baseline approaches in both total achievable rate and service stability, achieving simultaneous gains in spectral efficiency and system reliability.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Algorithm Configuration
• Multiagent Systems: Teamwork

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Movable-antennas (MAs) are revolutionizing spatial signal processing by providing flexible beamforming in next-generation wireless systems. This paper investigates an MA-empowered autonomous aerial vehicle (AAV) system in low-altitude wireless networks (LAWNs) for uplink data collection from ground users. We aim to maximize the sum achievable rate by jointly optimizing the AAV trajectory, receive beamforming, and MA positions. An efficient alternating optimization (AO) algorithm that incorporates successive convex approximation, weighted minimum mean square error, and particle swarm optimization is developed. The analysis of the computational complexity and convergence features is provided. Extensive simulations demonstrate superior performance in terms of the sum achievable rate and the service reliability comparing to several benchmark schemes. These results demonstrate the distinctive advantages of the proposed scheme: enhanced spectral efficiency via adaptive beam-user alignment and improved collection reliability through spatial interference management, highlighting the implementation potential of the MA-empowered LAWNs.

Problem

• Maximize sum achievable rate in MA-empowered AAV systems
• Optimize AAV trajectory, beamforming, and MA positions jointly
• Enhance spectral efficiency and collection reliability in LAWNs

Innovation

• Optimizing AAV trajectory and MA positions
• Alternating optimization with advanced algorithms
• Enhancing spectral efficiency via adaptive alignment