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A Blind Signal Separation Algorithm for Energy Detection of Dynamic PU Signals
Energy detection process for enabling opportunistic spectrum access in d...
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A Novel Feature Extraction for Robust EMG Pattern Recognition
Varieties of noises are major problem in recognition of Electromyography...
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Implementation of ASK, FSK and PSK with BER vs. SNR comparison over AWGN channel
This paper mainly discusses about three basic digital modulation process...
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Blind Modulation Classification based on MLP and PNN
In this work, a pattern recognition system is investigated for blind aut...
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Convolutional Neural Networks for Space-Time Block Coding Recognition
We find that the latest advances in machine learning with deep neural ne...
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Deep Learning for Signal Demodulation in Physical Layer Wireless Communications: Prototype Platform, Open Dataset, and Analytics
In this paper, we investigate deep learning (DL)-enabled signal demodula...
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Wideband Time-Domain Digital Backpropagation via Subband Processing and Deep Learning
We propose a low-complexity sub-banded DSP architecture for digital back...
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Blind Modulation Classification via Combined Machine Learning and Signal Feature Extraction
In this study, an algorithm to blind and automatic modulation classification has been proposed. It well benefits combined machine leaning and signal feature extraction to recognize diverse range of modulation in low signal power to noise ratio (SNR). The presented algorithm contains four. First, it advantages spectrum analyzing to branching modulated signal based on regular and irregular spectrum character. Seconds, a nonlinear soft margin support vector (NS SVM) problem is applied to received signal, and its symbols are classified to correct and incorrect (support vectors) symbols. The NS SVM employment leads to discounting in physical layer noise effect on modulated signal. After that, a k-center clustering can find center of each class. finally, in correlation function estimation of scatter diagram is correlated with pre-saved ideal scatter diagram of modulations. The correlation outcome is classification result. For more evaluation, success rate, performance, and complexity in compare to many published methods are provided. The simulation prove that the proposed algorithm can classified the modulated signal in less SNR. For example, it can recognize 4-QAM in SNR=-4.2 dB, and 4-FSK in SNR=2.1 dB with NS SVM and feature base function, the proposed algorithm has low complexity and simple implementation in practical issues.
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