Adaptive Pinching Antenna Optimization via Meta-Learning for Physical-Layer Security in Dynamic Wireless Networks

TL;DR

This paper introduces a meta-learning framework for real-time, adaptive control of pinching antennas in wireless networks, enhancing physical-layer security amid user mobility and localization uncertainties.

Contribution

It develops a gradient-based meta-learning approach that enables rapid adaptation of antenna positioning and power control in dynamic environments, outperforming existing methods.

Findings

Significantly reduces outage probability and improves secrecy performance.

Achieves faster convergence and adaptation with limited feedback.

Outperforms Reptile, reinforcement learning, and static methods in simulations.

Abstract

This paper develops a gradient-based meta-learning framework for real-time control of waveguided pinching-antenna systems under user-location uncertainty and physical-layer security (PLS) constraints. A probabilistic system model is introduced to capture the impact of imperfect localization on outage performance and secrecy. Based on this model, a joint antenna-positioning and transmit-power optimization problem is formulated to satisfy probabilistic reliability and secrecy requirements. To enable rapid adaptation in highly dynamic environments, the proposed approach employs model-agnostic meta-learning (MAML) to learn a transferable initialization across diverse mobility and channel conditions, allowing few-shot online adaptation using limited pilot feedback. Simulation results demonstrate that the proposed framework significantly outperforms Reptile-based meta-learning, non-meta reinforcement learning, conventional optimization, static antenna placement, and power-only control in terms of outage probability, secrecy performance, and convergence latency. These results establish meta-learning as an effective tool for secure and low-latency control of reconfigurable pinching-antenna systems in non-stationary wireless environments.