Adaptive Pinching Antenna Optimization via Meta-Learning for Physical-Layer Security in Dynamic Wireless Networks
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909979722645504 |
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| author | Musri, Khalid T. Sarhan, Akram Y. Abdullah, Osamah A. Al-Hraishawi, Hayder |
| author_facet | Musri, Khalid T. Sarhan, Akram Y. Abdullah, Osamah A. Al-Hraishawi, Hayder |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_00115 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Adaptive Pinching Antenna Optimization via Meta-Learning for Physical-Layer Security in Dynamic Wireless Networks Musri, Khalid T. Sarhan, Akram Y. Abdullah, Osamah A. Al-Hraishawi, Hayder Signal Processing 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. |
| title | Adaptive Pinching Antenna Optimization via Meta-Learning for Physical-Layer Security in Dynamic Wireless Networks |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2601.00115 |