Maximum-likelihood classification for digital amplitude-phase modulations

By Wen Wei, Jerry M. Mendel. IEEE Transactions on Communications, 2000

577 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Signal Processing

View Paper: https://www.semanticscholar.org/paper/f3df4398ac13b73a6256b0289965569c6d2ac9ee

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In Artificial Intelligence

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Citations in the corpus

Automatic Modulation Classification Using Combination of Genetic Programming and KNN (2012, methodology): “Wei and Mendel used Maximum Likelihood (ML) approach for classification of digital amplitude phase modulations [13].”
Survey of automatic modulation classification techniques: classical approaches and new trends (2007, methodology): “Theoretical performance analysis of the ML classifier was performed in [2] for no unknown parameters, when identifying linearly modulated signals in AWGN, with the LF given in (10) 2 .”
Likelihood-Ratio Approaches to Automatic Modulation Classification (2011, background): “The theoretical derivation discovers the asymptotic behavior that the ML-based classifier is capable of identifying any constellation with zero error as long as the number of available symbols goes to infinity, even under very noisy transmission conditions [66].”
Novel Automatic Modulation Classification Using Cumulant Features for Communications via Multipath Channels (2008, background): “The majority of existing AMC literature restricts the discussion on the additive noise channel, where the additive white Gaussian noise (AWGN) appears solely (no multipath) [1]– [3], [5], [7]–[12], [14]–[17].”
On the likelihood-based approach to modulation classification (2009, methodology): “The criterion used to make a decision on the modulation is [8], [9] ˆ í µí± = arg max í µí± ln{í µí± (í µí±) ALRT-UB (r)}, (6) wherê í µí± is the estimate of the modulation, and ln{⋅} is the natural logarithm.”