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

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 that may be used for reliable device classification (manufacturer, model, or serial number). Emission classification is addressed here through detection, location, extraction, and exploitation of RF [fingerprints] to provide device‐specific identification. The most critical step in this process is burst detection which occurs prior to fingerprint extraction and classification. Previous variance trajectory (VT) work provided sensitivity analysis for burst detection capability and highlighted the need for more robust processing at lower signal‐to‐noise ratio (SNR). The work presented here introduces a dual‐tree complex wavelet transform (DT‐ℂWT) denoising process to augment and improve VT detection capability. The new method's performance is evaluated using the instantaneous amplitude responses of experimentally collected 802.11a OFDM signals at various SNRs. The impact of detection error on signal classification performance is then illustrated using extracted RF fingerprints and multiple discriminant analysis (MDA) with maximum likelihood (ML) classification. Relative to previous approaches, the DT‐ℂWT augmented process emerges as a better alternative at lower SNR and yields performance that is 34% closer (on average) to [perfect] burst location estimation performance. Copyright © 2009 John Wiley & Sons, Ltd. A Dual‐Tree Complex Wavelet Transform (DT‐ℂWT) denoising process is introduced to improve communication burst detection and device classification performance. The impact of detection error on signal classification performance is demonstrated using RF fingerprints and multiple discriminant analysis with maximum likelihood classification (MDA‐ML). Relative to previous approaches, the DT‐ℂWT denoising process emerges as a better alternative at lower SNR values and yields device classification performance that is 34% closer (on average) to “perfect” burst location estimation performance.