A fuzzy logic method for modulation classification in nonidealenvironments
Wen Wei
Mendel, J.M.
Signal & Image Process. Inst., Univ. of Southern California, Los Angeles, CA;
This paper appears in: Fuzzy Systems, IEEE Transactions on
Publication Date: Jun 1999
Volume: 7,
Issue: 3
On page(s): 333-344
ISSN: 1063-6706
References Cited: 16
CODEN: IEFSEV
INSPEC Accession Number: 6293494
Digital Object Identifier: 10.1109/91.771088
Current Version Published: 2002-08-06
Abstract
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) 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 2D 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 α-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
Index
Terms
Available to subscribers and IEEE members.
References
Available to subscribers and IEEE members.
Citing Documents
Available to subscribers and IEEE members.
Cart
