Identification and classification of OFDM based signals using preamble correlation and cyclostationary feature extraction

In this thesis, a scheme for the identification and classification of orthogonal frequency division multiplexing based signals is proposed. Specifically, the cyclostationary signature of IEEE 802.11 and IEEE 802.16 standard compliant waveforms is investigated. A model is introduced that identifies the waveform; in the case of IEEE 802.11, confirms identification decision via cyclostationary feature extraction. If the waveform is identified as being IEEE 802.16 compliant, the scheme will classify the cyclic prefix size of the waveform. After cyclic prefix classification, the 802.16 waveform will be subjected to cyclostationary feature extraction for identification confirmation. The cyclostationary signature of each waveform is generated via a computationally efficient algorithm called the fast Fourier transform accumulation method, which produces an estimate of the waveform's spectral correlation density function. Simulation results based on MATLAB implementation are presented. http://archive.org/details/identificationnd109454620 US Marine Corps (USMC) author. Approved for public release; distribution is unlimited.