Moving or Predicting? RoleAware-MAPP: A Role-Aware Transformer Framework for Movable Antenna Position Prediction to Secure Wireless Communications

2025-10-23

AI Summary

To address the high real-time optimization complexity, mechanical motion lag behind channel variations, and the neglect of role asymmetry between legitimate users and eavesdroppers in movable-antenna (MA)-assisted physical-layer security (PLS) communications, this paper proposes a role-aware Transformer-based prediction framework. The framework innovatively incorporates role-aware embeddings, physics-informed semantic feature modeling, and a composite loss function—thereby deeply integrating domain-specific communication priors into the data-driven prediction process for efficient inference of secure MA positions. Evaluated under 3GPP-standardized scenarios, the framework achieves an average secrecy rate of 0.3569 bps/Hz and a positive secrecy capacity ratio of 81.52%, outperforming the best baseline by 48.4% in secrecy rate and 5.39 percentage points in positive secrecy capacity ratio.

Technology Category

• Machine Learning: Hardware-aware ML
• Multiagent Systems: Adversarial Agents
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Security and Privacy: Large-scale security measurements
• 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

Abstract

Movable antenna (MA) technology provides a promising avenue for actively shaping wireless channels through dynamic antenna positioning, thereby enabling electromagnetic radiation reconstruction to enhance physical layer security (PLS). However, its practical deployment is hindered by two major challenges: the high computational complexity of real time optimization and a critical temporal mismatch between slow mechanical movement and rapid channel variations. Although data driven methods have been introduced to alleviate online optimization burdens, they are still constrained by suboptimal training labels derived from conventional solvers or high sample complexity in reinforcement learning. More importantly, existing learning based approaches often overlook communication-specific domain knowledge, particularly the asymmetric roles and adversarial interactions between legitimate users and eavesdroppers, which are fundamental to PLS. To address these issues, this paper reformulates the MA positioning problem as a predictive task and introduces RoleAware-MAPP, a novel Transformer based framework that incorporates domain knowledge through three key components: role-aware embeddings that model user specific intentions, physics-informed semantic features that encapsulate channel propagation characteristics, and a composite loss function that strategically prioritizes secrecy performance over mere geometric accuracy. Extensive simulations under 3GPP-compliant scenarios show that RoleAware-MAPP achieves an average secrecy rate of 0.3569 bps/Hz and a strictly positive secrecy capacity of 81.52%, outperforming the strongest baseline by 48.4% and 5.39 percentage points, respectively, while maintaining robust performance across diverse user velocities and noise conditions.

Problem

• Predicting movable antenna positions to secure wireless communications
• Addressing computational complexity and temporal mismatch in antenna positioning
• Incorporating domain knowledge of user roles and adversarial interactions

Innovation

• Role-aware embeddings model user specific intentions
• Physics-informed semantic features capture channel characteristics
• Composite loss function prioritizes secrecy over geometric accuracy