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

TL;DR

This paper proposes an innovative MA-empowered AAV system for low-altitude wireless networks, optimizing trajectory, beamforming, and antenna positions to significantly improve uplink data collection efficiency and reliability.

Contribution

It introduces a joint optimization framework with an efficient AO algorithm for MA-empowered AAVs in LAWNs, demonstrating superior performance over benchmarks.

Findings

Enhanced sum achievable rate in simulations

Improved service reliability with adaptive beamforming

Superior spectral efficiency compared to benchmarks

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.