Adaptive Pinching Antenna Optimization via Meta-Learning for Physical-Layer Security in Dynamic Wireless Networks
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.
Cite
@article{arxiv.2601.00115,
title = {Adaptive Pinching Antenna Optimization via Meta-Learning for Physical-Layer Security in Dynamic Wireless Networks},
author = {Khalid T. Musri and Akram Y. Sarhan and Osamah A. Abdullah and Hayder Al-Hraishawi},
journal= {arXiv preprint arXiv:2601.00115},
year = {2026}
}