Channel State Information Preprocessing for CSI-based Physical-Layer Authentication Using Reconciliation

TL;DR

This paper presents an adaptive preprocessing technique using robust principal component analysis to improve CSI-based physical-layer authentication accuracy, significantly reducing error rates and increasing detection probabilities in various scenarios.

Contribution

The paper introduces an adaptive robust PCA preprocessing method tailored for CSI-PLA, enhancing authentication accuracy and outperforming existing preprocessing techniques.

Findings

A-RPCA reduces error probability after reconciliation.

A-RPCA increases detection probabilities to near 1.

Proposed method outperforms PCA, autoencoders, and ReProCS in tests.

Abstract

This paper introduces an adaptive preprocessing technique to enhance the accuracy of channel state information-based physical layer authentication (CSI-PLA) alleviating CSI variations and inconsistencies in the time domain. To this end, we develop an adaptive robust principal component analysis (A-RPCA) preprocessing method based on robust principal component analysis (RPCA). The performance evaluation is then conducted using a PLA framework based on information reconciliation, in which Gaussian approximation (GA) for Polar codes is leveraged for the design of short codelength Slepian Wolf decoders. Furthermore, an analysis of the proposed A-RPCA methods is carried out. Simulation results show that compared to a baseline scheme without preprocessing and without reconciliation, the proposed A-RPCA method substantially reduces the error probability after reconciliation and also substantially increases the detection probabilities that is also 1 in both line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios. We have compared against state-of the-art preprocessing schemes in both synthetic and real datasets, including principal component analysis (PCA) and robust PCA, autoencoders and the recursive projected compressive sensing (ReProCS) framework and we have validated the superior performance of the proposed approach.