Proceedings ArticleDOI
A new method of modulation classification for digitally modulated signals
Khaled Assaleh,Kevin R. Farrell,Richard J. Mammone +2 more
- pp 712-716
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TLDR
In this article, a modulation model is formed by estimating the instantaneous frequency and bandwidth using autoregressive spectrum analysis, which performed extremely well for input carrier-to-noise ratios as low as 15 dB.Abstract:
A modulation model representation of a signal is used to provide a convenient form for subsequent analysis. The modulation model is formed by estimating the instantaneous frequency and bandwidth using autoregressive spectrum analysis. In particular, the instantaneous bandwidth and derivative of the instantaneous frequency prove to be valuable parameters in estimating modulation type. This method performed extremely well for input carrier-to-noise ratios as low as 15 dB. Additionally, since the autoregressive fit to the frequency spectrum is second order, the autoregressive polynomials coefficients and corresponding roots can be computed with closed-form expressions. Thus, the method is computationally efficient. >read more
Citations
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Journal ArticleDOI
Survey of automatic modulation classification techniques: classical approaches and new trends
TL;DR: The authors provide a comprehensive survey of different modulation recognition techniques in a systematic way, and simulated some major techniques under the same conditions, which allows a fair comparison among different methodologies.
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.
Journal ArticleDOI
Maximum-likelihood classification for digital amplitude-phase modulations
Wen Wei,Jerry M. Mendel +1 more
TL;DR: The study of asymptotic performance shows that the ML classifier is capable of classifying any finite set of distinctive constellations with zero error rate when the number of available data symbols goes to infinity.
Journal ArticleDOI
An Automatic Digital Modulation Classifier for Measurement on Telecommunication Networks
TL;DR: This method can recognize classical single- carrier modulations, as well as orthogonal frequency-division multiplexing modulations such as discrete mul- titone that is used for asymmetricdigital subscriber line and very high speed digital subscriber line standards and for power-line carrier transmissions.
Journal ArticleDOI
An expert Discrete Wavelet Adaptive Network Based Fuzzy Inference System for digital modulation recognition
TL;DR: A comparative study of implementation of feature extraction and classification algorithms based on discrete wavelet decompositions and Adaptive Network Based Fuzzy Inference System (ANFIS) for digital modulation recognition is presented.
References
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Book
Digital Processing of Speech Signals
TL;DR: This paper presents a meta-modelling framework for digital Speech Processing for Man-Machine Communication by Voice that automates the very labor-intensive and therefore time-heavy and expensive process of encoding and decoding speech.
Proceedings ArticleDOI
Automatic modulation recognition of digitally modulated signals
S.-Z. Hsue,S.S. Soliman +1 more
TL;DR: A modulation recognizer that automatically reports modulation types of constant-envelope modulated signals is developed using zero-crossing techniques, and results demonstrate that reasonable average probability of correct classification is achievable at CNR=15 dB and higher.
Proceedings ArticleDOI
Modulation Classification based on Statistical Moments
TL;DR: A modulation classification algorithm using statistical pattern recognition techniques has been developed and tested on numerically simulated signals and uses statistical moments of both the demodulated signal and the signal spectrum as the modulation identifying parameters.
Proceedings Article
Estimation of carrier frequency, modulation type and bit rate of an unknown modulated signal.
Proceedings ArticleDOI
Spectral-temporal decomposition of multicomponent signals
K.T. Assaleh,Richard J. Mammone +1 more
TL;DR: The method is based on autoregressive modeling of the composite signal with an adaptive filter bank and shows good results for speech signal reconstruction.
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Hierarchical digital modulation classification using cumulants
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