Wideband spectrum sensing for cognitive radio networks: a survey
By Hongjian Sun, Arumgam Nallanathan, Cheng-Xiang Wang, Yunfei Chen. IEEE wireless communications, 2013
596 citations recorded by the source corpus.
Cognitive radio has emerged as one of the most promising candidate solutions to improve spectrum utilization in next generation cellular networks. A crucial requirement for future cognitive radio networks is wideband spectrum sensing: secondary users reliably detect spectral opportunities across a wide frequency range. In this article, various wideband spectrum sensing algorithms are presented, together with a discussion of the pros and cons of each algorithm and the challenging issues. Special attention is paid to the use of sub-Nyquist techniques, including compressive sensing and multichannel sub- Nyquist sampling techniques.
Subfields: Computational Mechanics, Computer Networks and Communications
View Paper: https://www.semanticscholar.org/paper/aa6238f7849b0396d1edc0ff82dd0d1f3dffbffe
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In Computational Mechanics
In Computer Networks and Communications
Citations in the corpus
- Advances on Spectrum Sensing for Cognitive Radio Networks: Theory and Applications (2017, background): “The problem of wideband (WB) spectrum sensing [6] consists of observing a wideband and identifying the portions of such band which are occupied by a signal and those which are free.”