2009
DOI: 10.1155/2009/879812
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Signal Classification in Fading Channels Using Cyclic Spectral Analysis

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Cited by 49 publications
(34 citation statements)
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“…(1) the spectral line regeneration (Section 6.1, [114]) [79,335,357]; (2) the statistical test for presence of cyclostationarity (Section 6.3, [63]) [6,149,178,233,271,270,303,302]; (3) the statistical test for presence of spectral coherence (Section 6.4, [156]) or the cycle frequency domain profile (Section 7.2, [187]) [219,355]; (4) higher-order cyclostationarity properties (Section 6.7) [82,221,279,327,329].…”
Section: Spectrum Sensing and Signal Classificationmentioning
confidence: 99%
“…(1) the spectral line regeneration (Section 6.1, [114]) [79,335,357]; (2) the statistical test for presence of cyclostationarity (Section 6.3, [63]) [6,149,178,233,271,270,303,302]; (3) the statistical test for presence of spectral coherence (Section 6.4, [156]) or the cycle frequency domain profile (Section 7.2, [187]) [219,355]; (4) higher-order cyclostationarity properties (Section 6.7) [82,221,279,327,329].…”
Section: Spectrum Sensing and Signal Classificationmentioning
confidence: 99%
“…And identifying the communication signals precisely needs some powerful information. As a result, the powerful information is fertile such as amplitude, phase, frequency, high-order cumulants, and cyclic spectral features [11][12][13][14][15][16][17]. As time passes by, the feature extractor was not only focusing on the time-frequency analysis but entropy features [18-21, 38, 39].…”
Section: Feature Extractionmentioning
confidence: 99%
“…The motivation behind the idea of cognitive radio is to utilize the available bandwidth more efficiently since spectrum occupancy measurements show that there are large temporal and spatial variations in the spectrum occupancy [1]- [2]. Cognitive radios have different functions such as spectrum sensing which employs the detection of signals presence in a desired frequency band.…”
Section: Introductionmentioning
confidence: 99%
“…Different types of spectrum sensing techniques had been developed including energy detection, matched filter, and cyclostationary features extraction. Matched filter detection requires perfect knowledge of the signals characteristics as well as energy detections is sensitive to noise and interference level whereas cyclostationary features has a good performance in low SNR scenarios [2][3][4][5] but it costs large number of computational capacity. As for efficient signals detection, compressed features that carry only the significant information that will clearly represent each type of the classified signals individually are needed.…”
Section: Introductionmentioning
confidence: 99%