Modulation Recognition in Continuous Phase Modulation Using Approximate Entropy

Pawar, S. U.; Doherty, J.F.

doi:10.1109/tifs.2011.2159000

Cited by 61 publications

(26 citation statements)

References 22 publications

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“…The eigenvalue equation can be defined as follow | -|= 0 λEB (8) where E is a unit matrix whose size is the same as the matrix B and B is a bispectral matrix whose size is 128×128. After doing a large numbers of experiments, we unexpectedly find that the eigenvalue of the matrix B has an interesting property that most of the eigenvalues are much smaller than the rest of the eigenvalues.…”

Section: Property Of Eigenvalue Of Modulation Signal's Bispectrummentioning

confidence: 99%

See 1 more Smart Citation

Extraction of Signal Waveform Feature Based on Bispectrum

Wang¹,

Zhao²,

Zhang³

2015

IJCCE

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This paper presents a new method of feature extraction for signal waveform. Selection of a feature extraction method is probably the single most important factor in achieving high recognition performance. Over the past four decades, considerable work has been done in the area of power spectrum estimation. However, a problem with this method is that it is phase blind. Situations arise in science and engineering whereby signal analysts are required to look beyond second-order statistics and analyze a signal's Higher-Order Statistics (HOS). In this paper, bispectrum is used to extract the feature of signal. Feature of the signal can be extracted by selecting the eigenvector whose corresponding eigenvalue's module is the largest as the template of recognition. The experiment being made by our research group suggests that recognition accuracy rate of bispectrum-method can be no less than 90 percent in additive white Gaussian noise channel when SNR (Signal to noise rate) is no less than 8dB .

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Section: Property Of Eigenvalue Of Modulation Signal's Bispectrummentioning

confidence: 99%

“…While second-order statistics [7] (e.g., correlation function, power spectrum, etc.) do not provide any information about the phase of the signal, third-order statistics [8] (e.g., triple correlation, bispectrum, etc.) allow the recovery of the phase of the signal.…”

Section: Introductionmentioning

confidence: 99%

Extraction of Signal Waveform Feature Based on Bispectrum

Wang¹,

Zhao²,

Zhang³

2015

IJCCE

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show abstract

“…Apart from low training requirement of SVM (50 realizations) for a particular value of SNR and except for carrier frequencies, no a priori information is required about carrier amplitude, carrier phase, carrier offset, symbol rate, pulse shape and initial symbol phase (timing offset). In [22] approximate entropy is employed to separate CPFSK signals using different instantaneous frequency non-rectangular pulses. Neural network classifier is used there that is trained on 500 realizations of 8000 samples each.…”

Section: Introductionmentioning

confidence: 99%

Distinguishing CPFSK from QAM and PSK Modulations

Bari

Doroslovački

2015

Circuits Syst Signal Process

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Digital modulation classification is important for many civilian as well as military applications. In this paper, we propose a simple and robust feature to distinguish continuous-phase FSK from QAM and PSK modulations. The feature is based on product of two consecutive signal values and on time averaging of imaginary part of the product. Conditional probability density functions of the feature given modulation type are determined. In order to overcome the complexity of calculating probability density functions, central limit theorem for strictly stationary m-dependent sequences is used to obtain Gaussian approximations. After calculating probability density functions, thresholds are determined based on minimization of total probability of misclassification. Since threshold-based results are valid for special cases requiring knowledge of some parameters, we resort to usage of support vector machines for classification, which require little training and no a priori information except for carrier frequency. Following that joint effects on the performance of carrier offset, fast fading, and non-synchronized sampling are studied in the presence of additive white Gaussian noise. For comparison purposes, rectangular pulse shape is used. To prove practical usefulness, not only the performance is analyzed for root-raised cosine pulses but also for quite less oversampling of symbols than what is found in other approaches. In the course of doing that, the performance is compared with wavelet-based feature that uses support vector machines for modulation separation.

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“…In most present literature referring to modulation classification,non-continuous phase modulations are extensively discussed,while few papers cope with various continuous phase modulations [4], [5]. In [5],the author comes up with the idea taking approximate entropy(ApEn) as a feature to implement continuous phase modulation classification.The method works on the oversampled signals and no timing information is required,thus has an inherent advantage over previous symbol sampled based techniques.…”

Section: Introductionmentioning

confidence: 99%

“…In [5],the author comes up with the idea taking approximate entropy(ApEn) as a feature to implement continuous phase modulation classification.The method works on the oversampled signals and no timing information is required,thus has an inherent advantage over previous symbol sampled based techniques. Furthermore,the method is flexible considering there exist adjustable parameters when computing the ApEn indices.…”

Section: Introductionmentioning

confidence: 99%

Extended modulation classification scheme based on approximate entropy

Chen

Zhu

et al. 2013

2013 IEEE Symposium on Wireless Technology &Amp; Applications (ISWTA)

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This paper focuses on the modulation classification problem based on approximate entropy(ApEn). The original ApEn-based classification procedure is extended to a more comprehensive one. In the new procedure, preprocessing method of complex squaring is proposed to cancel out the negative effect introduced by noise. Simulation results demonstrate the effectiveness of our method to alleviate the demanding for extreme high signal-to-noise ratio(SNR). Specifically,almost 20dB gain can be obtained to achieve a reasonable high classification accuracy(≥ 90%) under the special occasion being studied. Furthermore,hybrid-c value scheme is proposed to ensure relatively good performance over the whole SNR range. In this scheme,two different c values are adopted simultaneously to form four-dimensional ApEn feature vectors for classification step,which combines each c value's good performance over the specific SNR range.

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Modulation Recognition in Continuous Phase Modulation Using Approximate Entropy

Cited by 61 publications

References 22 publications

Extraction of Signal Waveform Feature Based on Bispectrum

Extraction of Signal Waveform Feature Based on Bispectrum

Distinguishing CPFSK from QAM and PSK Modulations

Extended modulation classification scheme based on approximate entropy

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