Paper Info
Title
A Novel Automatic Modulation Classifier Using Graph-Based Constellation Analysis for <inline-formula> <tex-math notation="LaTeX">${M}$ </tex-math></inline-formula>-ary QAM
Abstract
An innovative automatic modulation classification via graph-based constellation analysis for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${M}$ </tex-math></inline-formula> -ary QAM signals is presented. In our framework, a unified mesh model for the constellation diagrams of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${M}$ </tex-math></inline-formula> -QAM signals within the modulation candidate set is first constructed and exploited to transform the received <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${M}$ </tex-math></inline-formula> -QAM signal into graph domain. The concise graph representation of the received <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${M}$ </tex-math></inline-formula> -QAM signal is established from its constellation according to the positions of the recovered symbols in the mesh model. Then, the modulation feature vector is built from the eigenvector(s) corresponding to the maximum eigenvalue of its adjacency matrix. The modulation type can be identified by measuring the angle between the feature vectors resulting from the training data and the test data. Monte Carlo simulation results and theoretical analysis demonstrate that the proposed method with lower computational complexity can provide superior performance to the existing subtractive clustering technique, and is robust to the residual phase and timing offsets.
Year
DOI
Venue
2019
10.1109/LCOMM.2018.2889084
IEEE Communications Letters
Keywords
Field
DocType
Quadrature amplitude modulation,Symmetric matrices,Eigenvalues and eigenfunctions,Feature extraction,Training,Signal to noise ratio
Adjacency matrix,Quadrature amplitude modulation,Computer science,QAM,Signal-to-noise ratio,Algorithm,Real-time computing,Modulation,Eigenvalues and eigenvectors,Graph (abstract data type),Computational complexity theory
Journal
Volume
Issue
ISSN
23
2
1089-7798
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Xiao Yan171.48
Guoyu Zhang200.34
Hsiao-chun Wu395997.99