Studies On Modulation Scheme Recognition Based On Cyclic Spectral Correlation Function

Automatic Modulation Recognition(AMR) is an intrinsically interesting problem with a variety of regulatory and military applications. With the increasing number of modulation formats and the increasing similarity of the induced marginal distributions to a Gaussian channel, the problem is becoming more and more challenging. Recognition of the modulation type of an unknown signal provides insight into its structure, origin and properties. Automatic modulation recognition is used for spectrum surveillance and management, interference identification, military threat evaluation, electronic counter measures, source identification and many others.This paper proposed a technique for automatic modulation recognition that based on the cyclic Spectral Correlation Function(SCF). Communication signals that have undergone periodic transformations, such as sampling, scanning, modulating, multiplexing, and coding operations, can have cyclostationarity, so they should be appropriately modeled as cyclostationary processes. It show in this paper that different types of modulated signals that have identical power density functions can have highly distinct spectral correlation functions. So we can calculate the signal's spectral correlation functions , and derive a set of characteristic features from it, then use this set of features to classify the modulation types. As well we designed a MATLAB program to verify the SCF recognition algorithm. We find out that the algorithm get good performance for fixed-parameter modulation types when SNR is above OdB.We finally implement this method in our hardware system that is designed for real-time modulation recognition and demodulation. Test results show that the SCF algorithm works well, and SCF method has a good prospect for modulation recognition and is of significant practice value.