Text

theses

Software-defined radios (SDRs), have become very important in both commercial as well as military applications that demand high Quality of Service (QoS) in hostile physical and spectral conditions. Simultaneously, interoperability with legacy communications equipment is also a critical requirement for widespread adoption. An ideal SDR supports multi-standard, multimode and multiband wireless communications. Such a system is reconfigurable in the sense that transmitted signals at different carrier frequencies and/or different modulation schemes can be reliably identified and appropriately demodulated in real-time. In this dissertation, such a radio system is developed using a wavelet transform-based transceiver platform, composed of four main wavelet-domain processors: Channel Estimator, Channel Equalizer, Automatic Modulation Recognition (AMR) and Demodulator. The AMR method is blind identification of the modulation scheme used to format digital data embedded in a signal. It is investigated using the Discrete Wavelet Transform (DWT) in conjunction with techniques typically used in signal processing field of pattern recognition. In particular, the concept of wavelet-domain template matching is used to achieve modulation identification prior to signal demodulation. The digital modulation schemes considered in this work include families of ASK, FSK, PSK and QAM. The test signals used in this study have been subjected to Additive White Gaussian Noise (AWGN) resulting in Signal-to-Noise Ratios (SNRs) in the range of -5 dB to 10 dB. Monte Carlo simulations using the wavelet-based AMR algorithms show correct classification rates that are better than most of existing methods that use other techniques For wavelet-based demodulation original signal information can be directly obtained in the wavelet-domain without an inverse transform of a signal to its original time-domain form, and that has been proven analytically herein. Extensive Monte Carlo simulations have shown that the Bit Error Rates (BERs) obtained from wavelet-based demodulation are very comparable with the optimal case of matched filter-based demodulation. The results of this work show the ability of wavelet transforms to enable the automatic recognition and subsequent demodulation of communications signals in a single processing sequence by solely using the computationally-friendly mathematics of the Discrete Wavelet Transform.

ETD_7647

Ph.D.

Includes bibliographical references

by Yao Ge

doi:10.7282/T3WW7M1P

ETD doctoral

The author owns the copyright to this work.