Likelihood ratio tests for modulation classification
By P. Panagiotou, A. Anastasopoulos, A. Polydoros. MILCOM 2000 Proceedings. 21st Century Military Communications. Architectures and Technologies for Information Superiority (Cat. No.00CH37155), 2000
301 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/1d32176a29a39dcea57d23ff259100cbce4c447e
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In Artificial Intelligence
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In Molecular Biology
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Citations in the corpus
- Survey of automatic modulation classification techniques: classical approaches and new trends (2007, methodology): “GLRT displays some implementation advantages over ALRT and HLRT, as it avoids the calculation of exponential functions and does not require the knowledge of noise power to compute the LF.”
- Likelihood-Ratio Approaches to Automatic Modulation Classification (2011, background): “discuss GLRT with ui = {sk,i , θc} [46].”
- On the likelihood-based approach to modulation classification (2009, background): “The disadvantage of GLRT is its failure to identify nested signal constellations, such as Quadrature Amplitude Modulation (QAM) with 16 and 64 points in the signal constellation [10].”