@article {IOPORT.01454132,
    author       = {Mobasseri, B.G.},
    title        = {Digital modulation classification using constellation shape.},
    year         = {2000},
    journal      = {Signal Processing},
    volume       = {80},
    number       = {2},
    issn         = {0165-1684},
    pages        = {251-277},
    publisher    = {Elsevier Science, Amsterdam},
    doi          = {10.1016/S0165-1684(99)00127-9},
    abstract     = {Summary: Constellation diagram is a traditional and powerful tool for design and evaluation of digital modulations. In this work we propose to use constellation shape as a robust signature for digital modulation recognition. We represent the transmitted ``information'' by the geometry of the constellation. Received information is in turn the recovered constellation shape that is deformed by noise, channel and receiver implementation. We first demonstrate that fuzzy $c$-means clustering is capable of robust recovery of the unknown constellation. To perform Bayesian inference, the reconstructed constellation is modeled by a discrete multiple-valued nonhomogeneous spatial random field. For candidate modulations, their corresponding random fields are modeled off-line. The unknown constellation shape is then classified by an ML rule based on the preceding model building phase. The algorithm is applicable to digital modulations of arbitrary size and dimensionality.},
    identifier   = {01454132},
}