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For 8PSK modulation, the classification reveals 86 different classes of mappings out of a total of 5,040 mappings [14].
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Figure 6 shows the modulation family classification flow based on the MCA method.
The general behavior of a cognitive radio, including 'Analysis,' where the modulation classification is used.
Considering that real communication signals corrupted by noise are generally nonstationary, and timefrequency distributions are especially suitable for the analysis of nonstationary signals, time-frequency distributions are introduced for the modulation classification of communication signals.
To make the modulation classification system more suitable for signals in a wide range of signal to noise rate (SNR), a feature extraction method based on signal wavelet packet transform modulus maxima matrix (WPTMMM) and a novel support vector machine fuzzy network (SVMFN) classifier is presented.
The accurate modulation classification is realized based on the known modulation families.
Figure 18 Performance of the proposed AMC algorithm pertaining to the classification of PSK modulation in presence of G and BK noises.
Here, while it is noted that the number of antennas used does not affect the overall modulation-family classification performance, using the multistage approach does increase the observed classification performance by approximately two to three times.
What is more, the accurate modulation classification part can be extended easily within a certain modulation family.
Figure 16 Performance of the proposed AMC algorithm pertaining to the classification of QAM modulations in presence of G and BK noises.
Figure 15 Performance of the proposed AMC algorithm pertaining to the classification of APSK modulations in presence of G and BK noises.
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