Maximum-likelihood modulation classification for PSK/QAM

Sills, J.A.

doi:10.1109/milcom.1999.822675

Cited by 142 publications

(85 citation statements)

References 15 publications

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“…The result for the ratios R g , R 8 , R 16 and R 32 for different SNR values shows (see Figures 6 to 9) that the modulation type may be easily distinguished by just evaluating the ratios R(MOD, SNR). The Figure 6 presents the ratios R g , R 8 , R 16 and R 32 versus SNR (for, respectively, GMSK, 8-PSK, 16-QAM and 32-QAM modulations), for APD 0 = 0.1, in the case of an AWGN channel. The number of transmitted symbols has been set here to 10,000, and an oversampling factor of 8 has been used.…”

Section: Techniques Based On the Observation Of Amplitude Histograms:mentioning

confidence: 96%

“…These APDs show a marked difference in their shapes, so that the observations may be used to detect the modulation type of the received signal. For this purpose ratios (or normalized APDs) R g , R 8 , R 16 and R 32 , relative, respectively, to GMSK, …”

Section: Techniques Based On the Observation Of Amplitude Histograms:mentioning

confidence: 99%

“…This is the maximum-likelihood (ML) criterion and the conditional probability P(r |H α ) is called the likelihood or decision function. The logarithm of the likelihood function of the joint probability densities of phase and amplitude for QAM signals can be written as follows [16,25]:…”

Section: Maximum Likelihood Techniquementioning

confidence: 99%

“…The features, which have been used for classification of digital modulation, include moments and higher order cumulants of extracted phase [7][8][9][10][11], constellation shape, envelope or zero crossings observation [12][13][14], wavelets transforms [15], or more recently the joint probability densities of phases and amplitudes [16][17][18][19]. Other approaches, using neural nets [20], fuzzy logic [21] or particle filtering [22], have also been proposed.…”

Section: Introductionmentioning

confidence: 99%

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A Blind Modulation Type Detector for DPRS Standard

Dayoub

Okassa-M'foubat

Mvone

et al. 2006

Wireless Pers Commun

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In this paper, some methods allowing automatic discrimination between the types of modulations proposed by the DPRS communication standard are presented. In a non-cooperative context, the receiver must first be able to detect the presence of a modulated signal before eventually carrying out its demodulation (i.e. having access to the information). For this purpose a receiver capable of identifying digital modulations of type M-QAM, 8-PSK and GMSK, proposed by the ETSI-DPRS standard, has been studied. Three detection algorithms have been investigated. The first is based on the observation of the amplitude histograms, the second on the continuous wavelet transform and the third on the maximum likelihood for the joint probability densities of phases and amplitudes. Their performance has been evaluated against signal to noise ratio.

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Section: Techniques Based On the Observation Of Amplitude Histograms:mentioning

confidence: 96%

Section: Techniques Based On the Observation Of Amplitude Histograms:mentioning

confidence: 99%

Section: Maximum Likelihood Techniquementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Blind Modulation Type Detector for DPRS Standard

Dayoub

Okassa-M'foubat

Mvone

et al. 2006

Wireless Pers Commun

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“…With four phases, QPSK can encode two bits per symbol. Constellation Diagram of QPSK also uses both an in-phase carrier and quadrature [7]. The developed MATLAB code for QPSK constellation diagram is shown below:…”

Section: Constellation Diagram Of Qpskmentioning

confidence: 99%

Development of MATLAB Code for m-ary PSK Constellation Diagram

Thuneibat¹

2016

IOSRJEN

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-The constellation diagram is a representation of a signals modulated by a digital passband modulation scheme It displays the signal as a two-dimensional X-Y plane scatter diagram in the complex plane at symbol sampling instants. In a more abstract sense, it represents the possible symbols that may be selected by a given modulation scheme as points in the complex plane. Measured constellation diagrams can be used to recognize the type of interference and distortion in a signal. In this paper we used MATLAB environment to generate the constellation diagram of M-ary PSK and QAM modulation systems.

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Likelihood‐based Classifiers

Zhu

Nandi

2014

Automatic Modulation Classification

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Maximum-likelihood modulation classification for PSK/QAM

Cited by 142 publications

References 15 publications

A Blind Modulation Type Detector for DPRS Standard

A Blind Modulation Type Detector for DPRS Standard

Development of MATLAB Code for m-ary PSK Constellation Diagram

Likelihood‐based Classifiers

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