A Wavelet Approach to Wideband Spectrum Sensing for Cognitive Radios

By Zhi Tian, Georgios B. Giannakis. International Conference on Cognitive Radio Oriented Wireless Networks and Communications, 2006

641 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/e9d8faceef5393adf9ef0a902f389efe287a4aa0

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In Computational Mechanics

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Citations in the corpus

Advances on Spectrum Sensing for Cognitive Radio Networks: Theory and Applications (2017, background): “2) Wavelet-Based Sensing: A wavelet-based spectrum sensing algorithm is proposed in [96] and [97].”
Wideband spectrum sensing for cognitive radio networks: a survey (2013, background): “Unfortunately, due to the parallel structure of the ﬁlter bank, the implementation of this algorithm requires a large number of RF components.”
Signal Processing in Cognitive Radio (2009, methodology): “Specifically, wavelet-based detection has been developed under the following assumptions [ 27 ].”
Compressed Sensing for Wideband Cognitive Radios (2007, background): “DATES COVERED 00-00-2007 to 00-00-2007”
Optimal Multiband Joint Detection for Spectrum Sensing in Cognitive Radio Networks (2008, methodology): “In [25] and [26], the multiresolution features of the wavelet transform were used to estimate the PSD over a wide frequency range.”
Cooperative Communications for Cognitive Radio Networks (2009, methodology): “Forsignal detection over wide-band channels, the wavelet approach offers advantages in terms of both implementation cost and flexibility in adapting to the dynamic spectrum, as opposed to the conventional use of multiple narrow-band bandpass filters [ 37 ].”
Collaborative wideband sensing for cognitive radios (2008, methodology): “In [19] and [20], a wavelet transform was used to estimate the PSD over a wide frequency range given its multiresolution features.”
Cooperative Spectrum Sensing Optimization in Cognitive Radio Networks (2008, methodology): “To enhance the detection probability, many signal detection techniques can be used in spectrum sensing [11]–[14].”
A Review on Spectrum Sensing for Cognitive Radio: Challenges and Solutions (2010, methodology): “Quite a few sensing methods have been proposed, including the classic likelihood ratio test (LRT) [13], energy detection (ED) [9, 10, 13, 14], matched filtering (MF) detection [10, 13, 15], cyclostationary detection (CSD) [16–19], and some newly emerging methods such as eigenvalue-based sensing [6, ”
A survey of spectrum sensing algorithms for cognitive radio applications (2009, methodology): “The assumptions made in [96], however, need to be relaxed for building a practical sensing algorithm.”