Moving or Predicting? RoleAware-MAPP: A Role-Aware Transformer Framework for Movable Antenna Position Prediction to Secure Wireless Communications
Wenxu Wang, Xiaowu Liu, Wei Gong, Yujia Zhao, Kaixuan Li, Qixun Zhang, Zhiyong Feng, Kan Yu

TL;DR
This paper introduces RoleAware-MAPP, a transformer-based framework that predicts movable antenna positions by integrating domain knowledge to improve physical layer security in wireless communications.
Contribution
It reformulates antenna positioning as a predictive task and incorporates role-aware embeddings, physics-informed features, and a specialized loss function for enhanced security performance.
Findings
Achieves an average secrecy rate of 0.3569 bps/Hz.
Secures a secrecy capacity of 81.52%.
Outperforms baseline methods by 48.4% in secrecy rate.
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…
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Taxonomy
TopicsAdvanced Wireless Communication Technologies · Wireless Communication Security Techniques · Wireless Signal Modulation Classification
