![Electronic Thesis and Dissertation Repository](../../assets/md5images/943042f88f25dd8adfffc3aeec6e330c.png)
Degree
Master of Engineering Science
Program
Electrical and Computer Engineering
Supervisor
Xianbin Wang
Abstract
Protection of critical wireless infrastructure from malicious attacks has become increasingly important in recent years, with the widespread deployment of various wireless technologies and dramatic growth in user populations. This brings substantial technical challenges to the interception receiver design to sense and identify various wireless signals using different transmission technologies. The key requirements for the receiver design include estimation of the signal parameters/features and classification of the modulation scheme. With the proper identification results, corresponding signal interception techniques can be developed, which can be further employed to enhance the network behaviour analysis and intrusion detection.
In detail, the initial stage of the blind interception receiver design is to identify the signal parameters. In the thesis, two low-complexity approaches are provided to realize the parameter estimation, which are based on iterative cyclostationary analysis and envelope spectrum estimation, respectively. With the estimated signal parameters, automatic modulation classification (AMC) is performed to automatically identify the modulation schemes of the transmitted signals. A novel approach is presented based on Gaussian Mixture Models (GMM) in Chapter 4. The approach is capable of mitigating the negative effect from multipath fading channel. To validate the proposed design, the performance is evaluated under an experimental propagation environment. The results show that the proposed design is capable of adapting blind parameter estimation, realize timing and frequency synchronization and classifying the modulation schemes with improved performances.
Recommended Citation
Liu, Gejie, "Multi-stage Wireless Signal Identification for Blind Interception Receiver Design" (2012). Electronic Thesis and Dissertation Repository. 516.
https://ir.lib.uwo.ca/etd/516