In this book, we will discuss the applications of spare representation in wireless
communications, with particular focus on the most recently developed compressive sensing-enabled approaches. With the help of sparsity property, sub-Nyquist sampling can be achieved in wideband cognitive radio networks by adopting compressive sensing. This book starts from a comprehensive overview of CS principles.
Subsequently, we will present a complete framework for data-driven compressive
spectrum sensing in cognitive radio networks, which is able to provide guarantee on
robustness, low complexity, and security. Particularly, robust compressive spectrum
sensing, low-complexity compressive spectrum sensing, and secure compressive
sensing-based malicious user detection are proposed to address the various issues
in wideband cognitive radio networks. Correspondingly, the real-world signals and
data collected by experiments carried out during TV white space pilot trial enable
data-driven compressive spectrum sensing. The collected data are used to verify our
designs and provide significant insights on the potential of applying compressive
sensing to wideband spectrum sensing. We believe this book will provide readers a
clear picture on how to exploit the compressive sensing to process wireless signals
in wideband cognitive radio networks.