Publication Date


Document Type


Committee Members

Zhiqiang Wu (Committee Chair), Bin Wang (Committee Member), Yan Zhuang (Committee Member), Saiyu Ren (Committee Member), Michael Nowak (Committee Member)

Degree Name

Doctor of Philosophy (PhD)


The cognitive radio network has been considered one of the most promising communication technologies for next generation wireless communication. One important aspect of cognitive radio network research is the so-called Primary User Authentication. Naturally, the primary user authentication in the cognitive radio network requires strong encryption. For the primary user identification, this dissertation provides a method which hides the user information in the header of the data frames with an underlay waveform. It is important to note that the underlay waveform will not bring redundancy data to the communication system. Using the synchronizing frequency as the underlay waveform carrier also will not damage the system BER performance. The simulation results show that the underlay waveform can be decoded correctly by the receiver side with the cyclostationary signal processing, but can’t be detected by conventional Fourier transform based signal detection algorithms. This dissertation also suggests a keyless-encryption method which uses the instantaneous channel information to enhance the anti-eavesdropping capability. This method is based on the wireless channel properties: the upstream parameters are often times the same as the downstream parameters, and those parameters are stable in a short period meanwhile varying in the long term at the slow fading channel. Those parameters are unique between the pair of the base station and the mo-bile terminal, and no third party has the ability to obtain them. The simulations results show that the method is able to provide exceptional encryption with excellent BER performance.

Page Count


Department or Program

Department of Electrical Engineering

Year Degree Awarded


Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.