Predictive Position Control for Movable Antenna Arrays in UAV Communications: A Spatio-Temporal Transformer-LSTM Framework

2025-08-14

AI Summary

To address link attenuation and coverage dead zones in urban low-altitude communications caused by dynamic obstacles and multipath effects, this paper proposes a UAV-coordinated control framework leveraging a movable antenna (MA) array. To overcome the speed mismatch between mechanical MA repositioning latency and high-speed UAV mobility, we introduce, for the first time, a spatiotemporal joint prediction model integrating Transformer and LSTM architectures to accurately estimate optimal antenna positions. Furthermore, we design a joint optimization algorithm guided by the secrecy rate maximization criterion. Simulation results demonstrate that the proposed method reduces normalized mean square error by over 49% and significantly outperforms state-of-the-art approaches in communication reliability, thereby enhancing real-time performance, security, and energy efficiency in complex urban environments.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Security and Privacy: Security and privacy of machine learning and AI applications

Abstract

In complex urban environments, dynamic obstacles and multipath effects lead to significant link attenuation and pervasive coverage blind spots. Conventional approaches based on large-scale fixed antenna arrays and UAV trajectory optimization struggle to balance energy efficiency, real-time adaptation, and spatial flexibility. The movable antenna (MA) technology has emerged as a promising solution, offering enhanced spatial flexibility and reduced energy consumption to overcome the bottlenecks of urban low-altitude communications. However, MA deployment faces a critical velocity mismatch between UAV mobility and mechanical repositioning latency, undermining real-time link optimization and security assurance. To overcome this, we propose a predictive MA-UAV collaborative control framework. First, optimal antenna positions are derived via secrecy rate maximization. Second, a Transformer-enhanced long short-term memory (LSTM) network predicts future MA positions by capturing spatio-temporal correlations in antenna trajectories. Extensive simulations demonstrate superior prediction accuracy (NMSE reduction exceeds 49%) and communication reliability versus current popular benchmarks.

Problem

• Dynamic obstacles and multipath effects degrade UAV communication in cities
• Fixed antenna arrays lack energy efficiency and real-time adaptation
• Velocity mismatch between UAV mobility and antenna repositioning harms optimization

Innovation

• Predictive MA-UAV collaborative control framework
• Transformer-enhanced LSTM for position prediction
• Secrecy rate maximization for optimal positions