Application of wavelet denoising to improve OFDM-based signal detection and classification

摘要: The developmental emphasis on improving wireless access security through various OSI PHY layer mechanisms continues. This work investigates the exploitation of RF waveform features that are inherently unique to specific devices and may be used for reliable device classification (manufacturer, model, or serial number). Emission is addressed here detection, location, extraction, [fingerprints] provide device-specific identification. most critical step in this process burst detection which occurs prior fingerprint extraction classification. Previous variance trajectory (VT) provided sensitivity analysis capability highlighted need more robust processing at lower signal-to-noise ratio (SNR). presented introduces a dual-tree complex wavelet transform (DT-ℂWT) denoising augment improve VT capability. new method's performance evaluated using instantaneous amplitude responses experimentally collected 802.11a OFDM signals SNRs. impact error signal then illustrated extracted fingerprints multiple discriminant (MDA) with maximum likelihood (ML) Relative previous approaches, DT-ℂWT augmented emerges as better alternative SNR yields 34% closer (on average) [perfect] location estimation performance. Copyright © 2009 John Wiley & Sons, Ltd.