Sub-Nyquist Sampling and Compressed Sensing in Cognitive Radio Networks

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


Cognitive radio has become one of the most promising solutions for addressing the spectral under-utilization problem in wireless communication systems. As a key technology, spectrum sensing enables cognitive radios to find spectrum holes and improve spectral utilization efficiency. To exploit more spectral opportunities, wideband spectrum sensing approaches should be adopted to search multiple frequency bands at a time. However, wideband spectrum sensing systems are difficult to design, due to either high implementation complexity or high financial/energy costs. Sub-Nyquist sampling and compressed sensing play crucial roles in the efficient implementation of wideband spectrum sensing in cognitive radios. In this chapter, Sect. 6.1 presents the fundamentals of cognitive radios. A literature review of spectrum sensing algorithms is given in Sect. 6.2. Wideband spectrum sensing algorithms are then discussed in Sect. 6.3. Special attention is paid to the use of Sub-Nyquist sampling and compressed sensing techniques for realizing wideband spectrum sensing. Finally, Sect. 6.4 shows an adaptive compressed sensing approach for wideband spectrum sensing in cognitive radio networks.
Original languageEnglish
Title of host publicationCompressed Sensing & Sparse Filtering
Place of PublicationBerlin
PublisherSpringer Berlin Heidelberg
Number of pages37
ISBN (Electronic)978-3-642-38398-4
ISBN (Print)978-3-642-38397-7
Publication statusPublished - 2014

Publication series

NameSignals and Communication Technology
PublisherSpringer Berlin Heidelberg
ISSN (Print)1860-4862


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