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 precise probabilistic methods. We first transform a general pattern classification problem into one of function approximation, so that fuzzy logic systems (FLS’s) can be used to construct a classifier; then, we introduce the concepts of fuzzy modulation type and fuzzy decision and develop a nonsingleton fuzzy logic classifier (NSFLC) by using an additive FLS as a core building block. Our NSFLC uses two-dimensional (2-D) fuzzy sets, whose membership functions are isotropic so that they are well suited for a modulation classifier (MC). We establish that our NSFLC, although completely based on heuristics, reduces to the maximum-likelihood modulation classifier (ML MC) in ideal conditions. In our application of NSFLC to MC in a mixture of -stable 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 no impulsive noise is present.