A fuzzy logic method for modulation classification in nonideal environments

In this paper, we present a fuzzy logic modulation classifier that works in nonideal environments in which it is difficult or impossible to use precis e probabilistic methods. We first transform a general pattern classificatio n problem into one of function approximation, so that fuzzy logic systems ( FLS's) can be used to construct a classifier; then, me introduce the concep ts of fuzzy modulation type and fuzzy decision and develop a nonsingleton f uzzy logic classifier (NSFLC) by using an additive FLS as a core building b lock. Our NSFLC uses two-dimensional (2-D) fuzzy sets, whose membership fun ctions are isotropic so that they are well suited for a modulation classifi er (MC). We establish that our NSFLC, although completely based on heuristi cs, reduces to the maximum-likelihood modulation classifier (ML MC) in idea l conditions. In our application of NSFLC to MC in a mixture of alpha-stabl e and Gaussian noises, we demonstrate that our NSFLC performs consistently better than the ML MC and it gives the same performance as the ML MC when n o impulsive noise is present.