# Learning-Aided Physical Layer Authentication as an Intelligent Process

arXiv: https://arxiv.org/abs/1808.02456 · HTML: https://mlsanity.com/paper/1808.02456/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Aug 2018.

## Built on by (most cited first, 5 of 5)

- [A Generalizable Model-and-Data Driven Approach for Open-Set RFF Authentication](https://mlsanity.com/paper/2108.04436/) (2021)
- [Towards Zero Touch Networks: Cross-Layer Automated Security Solutions for 6G Wireless Networks](https://mlsanity.com/paper/2502.20627/) (2025)
- [AoA-Based Physical Layer Authentication in Analog Arrays under Impersonation Attacks](https://mlsanity.com/paper/2407.08282/) (2024)
- [On the Use of CSI for the Generation of RF Fingerprints and Secret Keys](https://mlsanity.com/paper/2110.15415/) (2021)
- [AI-Assisted Authentication: State of the Art, Taxonomy and Future Roadmap](https://mlsanity.com/paper/2204.12492/) (2022)

## Abstract (arXiv)

Performance of the existing physical layer authentication schemes could be severely affected by the imperfect estimates and variations of the communication link attributes used. The commonly adopted static hypothesis testing for physical layer authentication faces significant challenges in time-varying communication channels due to the changing propagation and interference conditions, which are typically unknown at the design stage. To circumvent this impediment, we propose an adaptive physical layer authentication scheme based on machine-learning as an intelligent process to learn and utilize the complex and time-varying environment, and hence to improve the reliability and robustness of physical layer authentication. Explicitly, a physical layer attribute fusion model based on a kernel machine is designed for dealing with multiple attributes without requiring the knowledge of their statistical properties. By modeling the physical layer authentication as a linear system, the proposed technique directly reduces the authentication scope from a combined N-dimensional feature space to a single dimensional (scalar) space, hence leading to reduced authentication complexity. By formulating the learning (training) objective of the physical layer authentication as a convex problem, an adaptive algorithm based on kernel least-mean-square is then proposed as an intelligent process to learn and track the variations of multiple attributes, and therefore to enhance the authentication performance. Both the convergence and the authentication performance of the proposed intelligent authentication process are theoretically analyzed. Our simulations demonstrate that our solution significantly improves the authentication performance in time-varying environments.