Proceedings ArticleDOI
Higher-order cyclic cumulants for high order modulation classification
Octavia A. Dobre,Yeheskel Bar-Ness,Wei Su +2 more
- Vol. 1, pp 112-117
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TLDR
This paper investigates automatic modulation classification (AMC) using homogeneous feature-vectors based on cyclic cumulants of fourth, sixth- and eight-orders for QAM, PSK and ASK signals within a pattern recognition framework.Abstract:
In this paper we investigate automatic modulation classification (AMC) using homogeneous feature-vectors based on cyclic cumulants (CCs) of fourth-, sixth- and eight-orders, respectively, for QAM, PSK and ASK signals within a pattern recognition framework. Analysis of CCs of the baseband signal at the receiver is performed and used for feature selection. The cycle spectrum of the baseband signal at the receiver is derived as a function of excess bandwidth for a raised cosine pulse shape and a necessary and sufficient condition on the oversampling factor is obtained. Theoretical arguments regarding the discrimination capability of the examined feature-vectors are verified through extensive simulations.read more
Citations
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Analysis of the Power Amplifier Nonlinearity on the Power Allocation in Cognitive Radio Networks
TL;DR: The power allocation in cognitive radio networks is studied by considering the nonlinear effects of the PA on the received signal-to-noise ratio (SNR) at the secondary receiver (SR) and the adjacent channel interference (ACI) to the PRs.
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Multi–Dimensional Wireless Signal Identification Based on Support Vector Machines
TL;DR: A general machine learning framework is proposed for radio air interface identification by utilizing the outputs of the spectral correlation function, fast Fourier Transform, auto–correlation function, and power spectral density as the training inputs for the support vector machines (SVMs).
Journal ArticleDOI
AMCRN: Few-Shot Learning for Automatic Modulation Classification
TL;DR: The AMC under few-shot conditions is considered, where a novel network architecture is proposed, namely automatic modulation classification relation network (AMCRN), and verified with the baseline methods.
Journal ArticleDOI
Prototype of an Automatic Digital Modulation Classifier Embedded in a Real-Time Spectrum Analyzer
TL;DR: This paper presents a prototype of an automatic digital modulation classifier based on a real-time spectrum analyzer (RTSA) architecture that is suitable for efficient spectrum monitoring to identify the signals present in a certain frequency band, without the need of knowing any parameter about them.
Journal ArticleDOI
Modulation classification in multipath fading channels using sixth-order cumulants and stacked convolutional auto-encoders
TL;DR: This study proposes a new algorithm, which applies sixth-order cumulants and SCAEs to modulation classification in multipath fading channels, and shows that the proposed method can achieve better classification accuracy than the existing approaches under various channel conditions.
References
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Journal ArticleDOI
Hierarchical digital modulation classification using cumulants
Ananthram Swami,Brian M. Sadler +1 more
TL;DR: It is shown that cumulant-based classification is particularly effective when used in a hierarchical scheme, enabling separation into subclasses at low signal-to-noise ratio with small sample size.
Proceedings ArticleDOI
Likelihood ratio tests for modulation classification
TL;DR: Simulation results show that two novel modulation classification algorithms that are based on the decision theoretic approach can offer a significant performance gain for classification of dense, non-constant envelope constellations.
Journal ArticleDOI
Asymptotic theory of mixed time averages and kth-order cyclic-moment and cumulant statistics
TL;DR: It is shown that time averages of such mixtures converge in the mean-square sense to their ensemble averages and that sample averages of arbitrary orders are jointly complex normal and provide their covariance expressions.
Journal ArticleDOI
The cumulant theory of cyclostationary time-series. II. Development and applications
TL;DR: The development of the theory of nonlinear processing of cyclostationary time-series that is initiated in Part I is continued and a new type of cumulant for complex-valued variables is introduced and used to generalize the temporal and spectral moments and cumulants for cyclostators from real-valued tocomplex-valued time- series.
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