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

• URL: http://arxiv.org/abs/2512.16719v1
• Date: Thu, 18 Dec 2025 16:22:03 GMT
• Title: Channel State Information Preprocessing for CSI-based Physical-Layer Authentication Using Reconciliation
• Authors: Atsu Kokuvi Angelo Passah, Rodrigo C. de Lamare, Arsenia Chorti,
• Abstract summary: This paper introduces an adaptive preprocessing technique to enhance the accuracy of channel state information-based physical layer authentication (CSI-PLA)<n>We develop an adaptive robust principal component analysis (A-RPCA) preprocessing method based on robust principal component analysis (RPCA)<n>We have compared against state-of the-art preprocessing schemes in both synthetic and real datasets.
• Score: 15.01668968424277
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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