Cyclostationary Approaches to Signal Detection and Classification in Cognitive Radio

By Kyouwoong Kim, Ihsan Akbar, K. Bae, Jungsun Um, C. Spooner, Jeffrey H. Reed. 2007 2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2007

480 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/efd1c122e6d1f12e48531d0f8d88b621e594e6d7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Networks and Communications

In Signal Processing

Citations in the corpus

Likelihood-Ratio Approaches to Automatic Modulation Classification (2011, methodology): “Research has been conducted in classifying analog, digital, and orthogonal frequency-division multiplexing (OFDM) modulation schemes using maximumlikelihood (ML) test, waveform zero crossing measurement [29], spectral feature [51], wavelet analysis [23], [68], signal cyclostationarity [36], [50], as”
A survey of spectrum sensing algorithms for cognitive radio applications (2009, methodology): “Cycle frequencies of the incoming signal are used for detection and signal classification in [79].”
Multiple antenna spectrum sensing in cognitive radios (2010, background): “For the case of unknown noise variance, the cyclostationarity property of communication signals is exploited in [5], [11], [12].”
Spectrum Sensing for Cognitive Radio (2009, background): “It has also featured in other applications that include the following: � detection of cochannel and adjacent signals, which is a key property that can mitigate the hidden-node problem in cognitive radio [38]; discrimination between various types of modulation [39]–[ 41 ]; estimation of modulation pa”