Automatic Modulation Recognition of MPSK Signals Using Constellation Rotation and its 4-th Order Cumulant

We derive and analyze a new pattern recognition approach for automatic modulation recognition of MPSK (2, 4, and 8) signals in broad-band Gaussian noise. Presented method is based on constellation rotation of the received symbols, and a 4th order cumulant of a 1D distribution of the signal's in...

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Main Authors: Pedzisz, M., Mansour, Ali
Format: Journal Article
Published: Academic Press 2005
Online Access:http://hdl.handle.net/20.500.11937/17244
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author Pedzisz, M.
Mansour, Ali
author_facet Pedzisz, M.
Mansour, Ali
author_sort Pedzisz, M.
building Curtin Institutional Repository
collection Online Access
description We derive and analyze a new pattern recognition approach for automatic modulation recognition of MPSK (2, 4, and 8) signals in broad-band Gaussian noise. Presented method is based on constellation rotation of the received symbols, and a 4th order cumulant of a 1D distribution of the signal's in-phase component. Using Fourier series expansion of this cumulant as a function of the rotation angle, we extract invariant features which are then used in a neural classifier. Discrimination power of the proposed set of features is verified through extensive simulations, and the performance of the suggested algorithm is compared to the maximum-likelihood (ML) classifiers. Corresponding results show that our technique is comparable to the coherent ML classifier and outperforms the non-coherent pseudo-ML method for all considered signal-to-noise ratio (SNR) without the computational overhead of the latter.
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spelling curtin-20.500.11937-172442017-10-02T02:28:15Z Automatic Modulation Recognition of MPSK Signals Using Constellation Rotation and its 4-th Order Cumulant Pedzisz, M. Mansour, Ali We derive and analyze a new pattern recognition approach for automatic modulation recognition of MPSK (2, 4, and 8) signals in broad-band Gaussian noise. Presented method is based on constellation rotation of the received symbols, and a 4th order cumulant of a 1D distribution of the signal's in-phase component. Using Fourier series expansion of this cumulant as a function of the rotation angle, we extract invariant features which are then used in a neural classifier. Discrimination power of the proposed set of features is verified through extensive simulations, and the performance of the suggested algorithm is compared to the maximum-likelihood (ML) classifiers. Corresponding results show that our technique is comparable to the coherent ML classifier and outperforms the non-coherent pseudo-ML method for all considered signal-to-noise ratio (SNR) without the computational overhead of the latter. 2005 Journal Article http://hdl.handle.net/20.500.11937/17244 10.1016/j.dsp.2004.12.007 Academic Press restricted
spellingShingle Pedzisz, M.
Mansour, Ali
Automatic Modulation Recognition of MPSK Signals Using Constellation Rotation and its 4-th Order Cumulant
title Automatic Modulation Recognition of MPSK Signals Using Constellation Rotation and its 4-th Order Cumulant
title_full Automatic Modulation Recognition of MPSK Signals Using Constellation Rotation and its 4-th Order Cumulant
title_fullStr Automatic Modulation Recognition of MPSK Signals Using Constellation Rotation and its 4-th Order Cumulant
title_full_unstemmed Automatic Modulation Recognition of MPSK Signals Using Constellation Rotation and its 4-th Order Cumulant
title_short Automatic Modulation Recognition of MPSK Signals Using Constellation Rotation and its 4-th Order Cumulant
title_sort automatic modulation recognition of mpsk signals using constellation rotation and its 4-th order cumulant
url http://hdl.handle.net/20.500.11937/17244