Recognition of communication signal types using genetic algorithm and support vector machines based on the higher order statistics

Automatic recognition of communication signal type plays an important role in various applications. Most of the existing recognizers can only identify a few types of communication signal. This paper presents a novel intelligent technique that identifies a variety of digital signal types. Here, a hierarchical support vector machine based structure is proposed as the multiclass classifier. A proper set of the higher order moments (up to eighth) and higher order cumulants (up to eighth) are proposed as the effective features for recognizing of the digital communication signal. A genetic algorithm is used for selecting the suitable parameters of support vector machines. This idea improves the performance of the recognizer, efficiently. Simulation results show that the proposed recognizer has a high success rate for recognition of the different modulations even at very low SNRs.