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Identification of L-ary CPFSK in a fading channel using approximate entropy

In this paper we study approximate entropy as the feature to distinguish within the class of L-ary continuous-time FSK in the presence of correlated fast fading and additive white Gaussian noise. Support vector machines are employed to distinguish the signals. One benefit of using support vector machines is that they require very few realizations for training. Moreover, no a priori information is required about carrier amplitude, carrier phase, symbol rate and pulse shape. Performance of the approximate entropy feature classified by support vector machines is compared to the performance of wavelet-based feature classified by support vector machines.