This thesis investigates the detection, classification, frame synchronization, and demodulation of wireless networking waveforms by a digital receiver. The approach is to develop detection thresholds for wireless networking signals based upon the probability density functions of the signal present or signal absent scenarios. A Neyman-Pearson test is applied to determine decision thresholds and the associated probabilities of detection. With a chosen threshold, MATLAB simulations are run utilizing models developed to generate and receive IEEE 802.11a, IEEE 802.16, and IEEE 802.11b signals in multipath channels characterized by Rayleigh fading. Algorithms are developed for frame synchronization for each of the three waveforms. The probability of signal detection, successful frame synchronization, and the bit error rates of the received packet header and data are calculated. The results show that, even in Rayleigh fading environments at low signal to noise levels, these three waveforms can be distinguished in a digital receiver. Further, the results show that significant signal information can be gathered on these wireless networking waveforms, even when the entire signal cannot be demodulated due to low signal to noise ratios.
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