2025/12/18 by Passah, Atsu Kokuvi Angelo, de Lamare, Rodrigo C., Chorti, Arsenia
Computer Science · Engineering · #Advanced Wireless Communication Techniques #Authentication (law) #Channel (broadcasting) #Component (thermodynamics) #Cryptography and Security (cs.CR) #Data pre-processing #FOS: Computer and information sciences #FOS: Electrical engineering #Pattern recognition (psychology) #Preprocessor #Principal component analysis #Robust principal component analysis #Robustness (evolution) #Signal Processing (eess.SP) #Wireless Communication Security Techniques #Wireless Signal Modulation Classification #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2512.16719
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2025/12/18 · openalex created_date 2025/12/21 · openalex updated_date 2026/08/05
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.