Sub-Nyquist Sampling and Compressed Sensing in Cognitive Radio Networks

Sun, Hongjian, Nallanathan, Arumugam and Jiang, Jing (2014) Sub-Nyquist Sampling and Compressed Sensing in Cognitive Radio Networks. In: Compressed Sensing & Sparse Filtering. Signals and Communication Technology . Springer, pp. 149-185. ISBN 9783642383977

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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.

Item Type: Book Section
Uncontrolled Keywords: Time Slot, Power Spectral Density, Cognitive Radio, Primary User, Cognitive Radio Network
Subjects: H600 Electronic and Electrical Engineering
Department: Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Depositing User: Paul Burns
Date Deposited: 07 Sep 2018 16:20
Last Modified: 11 Oct 2019 19:30

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