Conclusions and Future Work

Abstract

This book presented research work on the promising applications of compressive sensing (CS) technique in wideband spectrum sensing, which is regarded as one of the most challenging tasks in cognitive radio networks (CRNs). It has been demonstrated that CS is capable of enabling sub-Nyquist sampling at secondary users (SUs), by exploiting the natural sparsity of spectral signals. By invoking CS technique, the signal sampling costs at SUs are significantly reduced, which is of great significance in CRNs as the SUs are normally energy-constrained devices. Within this book, the fundamental research has been presented on the design of novel compressive spectrum sensing algorithms, with particular efforts to improve energy efficiency, robustness, and security of CRNs. All the proposed designs are verified by real-world data, which also demonstrated the potential of data-driven compressive spectrum sensing.