A Novel Approach for Automatic Modulation Classification via Hidden Markov Models and Gabor Features
- 24 May 2017
- journal article
- research article
- Published by Springer Science and Business Media LLC in Wireless Personal Communications
- Vol. 96 (3), 4199-4216
- https://doi.org/10.1007/s11277-017-4378-x
Abstract
No abstract availableKeywords
This publication has 29 references indexed in Scilit:
- Modulation classification for asynchronous high‐order QAM signalsWireless Communications and Mobile Computing, 2011
- Classification of linear and non-linear modulations using the Baum–Welch algorithm and MCMC methodsSignal Processing, 2010
- Cyclostationarity-Based Modulation Classification of Linear Digital Modulations in Flat Fading ChannelsWireless Personal Communications, 2009
- Classification of modulated signals using multifractal featuresJournal of the Chinese Institute of Engineers, 2008
- An expert Discrete Wavelet Adaptive Network Based Fuzzy Inference System for digital modulation recognitionExpert Systems with Applications, 2007
- Survey of automatic modulation classification techniques: classical approaches and new trendsIET Communications, 2007
- Maximum Log-Likelihood Function-Based QAM Signal Classification over Fading ChannelsWireless Personal Communications, 2004
- Hierarchical digital modulation classification using cumulantsIEEE Transactions on Communications, 2000
- Algorithms for automatic modulation recognition of communication signalsIEEE Transactions on Communications, 1998
- Classification of quadrature amplitude modulated (QAM) signals via sequential probability ratio test (SPRT)Signal Processing, 1997