Automatic modulation recognition using wavelet transform and neural networks in wireless systems
TL;DR: The proposed algorithm for automatic digital modulation recognition is verified using higher-order statistical moments (HOM) of continuous wavelet transform (CWT) as a features set and a multilayer feed-forward neural network trained with resilient backpropagation learning algorithm is proposed as a classifier.
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Abstract: Modulation type is one of the most important characteristics used in signal waveform identification. In this paper, an algorithm for automatic digital modulation recognition is proposed. The proposed algorithm is verified using higher-order statistical moments (HOM) of continuous wavelet transform (CWT) as a features set. A multilayer feed-forward neural network trained with resilient backpropagation learning algorithm is proposed as a classifier. The purpose is to discriminate among different M-ary shift keying modulation schemes and themodulation order without any priori signal information. Pre-processing and features subset selection using principal component analysis is used to reduce the network complexity and to improve the classifier's performance. The proposed algorithm is evaluated through confusion matrix and false recognition probability. The proposed classifier is shown to be capable of recognizing the modulation scheme with high accuracy over wide signal-to-noise ratio (SNR) range over both additive white Gaussian noise (AWGN) and different fading channels.
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Citations
An overview of feature-based methods for digital modulation classification
Alharbi Hazza,Mobien Shoaib,Saleh A. Alshebeili,A. Fahad +3 more
- 28 Mar 2013
TL;DR: An overview of feature-based (FB) methods developed for Automatic classification of digital modulations, using the most well-known features and classifiers to assist newcomers to the field to choose suitable algorithms for intended applications.
206
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A Survey on Deep Learning Techniques in Wireless Signal Recognition
TL;DR: A brief overview of signal recognition approaches is presented in this article, where classical methods, emerging machine learning, and deep leaning schemes are extended from modulation recognition to wireless technology recognition with the continuous evolution of wireless communication system.
Signal Identification for Multiple-Antenna Wireless Systems: Achievements and Challenges
TL;DR: The aim of this work is to provide a comprehensive state-of-the-art survey on algorithms proposed for the new and challenging signal identification problems specific to MIMO systems, including space-time block code (STBC) identification, MIMo modulation identification, and detection of the number of transmit antennas.
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Automatic Modulation Classification Using Gated Recurrent Residual Network
TL;DR: This article proposes a novel gated recurrent residual neural network (GrrNet) for feature-based AMC, where the amplitude and phase of the received signal are utilized as the inputs of GrrNet and it is shown that Grr net outperforms other recent DL- based AMC methods.
117
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