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Automatic modulation classification (AMC) is the process of identifying modulation type of a detected signal without prior information.
The optimal threshold when recognizing the modulation type of received signals is derived to achieve the least error probability.
Previous studies on blind recognition of the modulation type of a received signal are generally performed in a single domain.
In[9], high-order statistics and neural networks were employed to identify the modulation type of MIMO system with and without channel state information.
Here, we need to discriminate MIMO radar signal from conventional radar signal and recognize the modulation type of MIMO radar signal.
The statistical property of depends on the symbol duration, the information bit sequence, and the modulation type of the PU signal.
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A matlab graphical user interface (GUI) is designed to see the intercepted signal, its power spectral density, frequency and modulation type on the screen of the personnel computer.
And the following steps of classifying various modulation types of signals are shown as follows: Step 1: ST-RFT spectrogram Computation.
Modulation type is one of the most important characteristics used in signal waveform identification.
Figure 11 generates the BER results for NSRO = −0.05 in comparison to NSRO = 0.05 for QPSK modulation type with length of 300 symbols.
This can be used for detection of a random signal with a particular modulation type in a background of noise and other modulated signals [9].
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