Order recognition of continuous-phase FSK

In this paper we study a set of distinguishing features based on approximate entropy. The set identifies the order of continuous-time frequency shift keyings in the joint presence of carrier offset, asynchronous sampling and symbol intervals, correlated fast fading and additive white Gaussian noise. Performance of the approximate-entropy-based features is compared to the performance of wavelet-based feature. For fair comparison of the features, both the approximate-entropy- based and wavelet-based features are classified by support vector machines. Major benefit of employing support vector machines is that they are able to train themselves using a very few realizations. Also, no a priori information is required about carrier phase, symbol rate and carrier amplitude.