Cyclostationary beamforming for multiple cycle frequencies estimation and detection

• Main Author: Yang, John Xingguang
• Other Authors: Ser Wee
• Format: Thesis-Doctor of Philosophy
• Language: English
• Published: Nanyang Technological University 2020
• Subjects: Engineering::Electrical and electronic engineering::Electronic systems::Signal processing; Engineering::Electrical and electronic engineering::Wireless communication systems
• Online Access: https://hdl.handle.net/10356/136778
• Institution: Nanyang Technological University

The research work reported in this thesis focuses on the investigation of the design and performances of the cyclostationary beamforming technique for multiple cycle frequencies estimation and detection. Many communications, radar and sonar systems have statistical parameters which vary in time. As a result, a significant amount of research work has been reported on the cyclostationarity property of such signals. Cyclostationary beamforming algorithms, such as the cyclic adaptive beamforming (CAB), are sensitive to the accuracy of the presumed cycle frequency (CF) of the signal-of-interest (SOI) and their performance degrades significantly in the presence of mismatch between the presumed and actual CF. In cognitive radio networks, the main challenge for spectrum sensing (SS) is to develop blind sensing techniques that can detect primary user (PU) with low signal-to-noise ratio (SNR). For SS, cycle frequency of the PU signal can be used as a feature for detection. Furthermore, due to the hidden primary user problem, SS performance degrades significantly when the received signal-to-interference ratio (SIR) at the receiver is small. Hence, this thesis studies the application of using cyclostationary beamforming for estimating and detecting multiple cycle frequencies under co-channel interference. Firstly, the thesis proposes two novel multiple cycle frequencies estimation algorithms for the estimation of the number of weak cyclostationary signals and their algorithms have been evaluated via numerical simulations and the results obtained show that they outperform other existing algorithms in estimation accuracy.