Autmomatic Modulation Classification Using Combination of Genetic Programming and KNN

Aslam, Muhammad Waqar; Zhu, Zhechen; Nandi, Asoke K.

doi:10.1109/twc.2012.060412.110460

Cited by 226 publications

(113 citation statements)

References 30 publications

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“…These describing vectors are used for recognition by using threshold decision. Furthermore, after ψ 2,0 and ψ 5,1 features computation, intrapulse modulation signals are described by matrices, which are used for recognition by using KNN classifier [22].…”

Section: System Modelmentioning

confidence: 99%

“…The pseudo-Zernike moments have opened a wider set of applications for radar signal recognition in recent years, because the moments can provide potentially useful invariance properties such as translation, scale, and rotational invariance [20,21]. In [22], Jarmo Lundén, et al examined the suitability of pseudo-Zernike moments as features for radar waveform recognition, In [23], a new radar classification algorithm based on STFT and pseudo-Zernike moments features is proposed. Inspired by the aforesaid background, the pseudo-Zernike moments are beneficial to realize intrapulse modulation recognition in scenarios with large variation in the signal parameters.…”

Section: Introductionmentioning

confidence: 99%

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Intra-pulse modulation recognition using short-time ramanujan Fourier transform spectrogram

Liu

Shan

2017

EURASIP J. Adv. Signal Process.

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Intra-pulse modulation recognition under negative signal-to-noise ratio (SNR) environment is a research challenge. This article presents a robust algorithm for the recognition of 5 types of radar signals with large variation range in the signal parameters in low SNR using the combination of the Short-time Ramanujan Fourier transform (ST-RFT) and pseudo-Zernike moments invariant features. The ST-RFT provides the time-frequency distribution features for 5 modulations. The pseudo-Zernike moments provide invariance properties that are able to recognize different modulation schemes on different parameter variation conditions from the ST-RFT spectrograms. Simulation results demonstrate that the proposed algorithm achieves the probability of successful recognition (PSR) of over 90% when SNR is above -5 dB with large variation range in the signal parameters: carrier frequency (CF) for all considered signals, hop size (HS) for frequency shift keying (FSK) signals, and the time-bandwidth product for Linear Frequency Modulation (LFM) signals.

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Section: System Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intra-pulse modulation recognition using short-time ramanujan Fourier transform spectrogram

Liu

Shan

2017

EURASIP J. Adv. Signal Process.

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“…In [17], the -nearest neighbor clustering classifier was used for AMC modulation classification. Under ideal conditions, better PCC based on -nearest neighbor clustering classifier was only limited to ideal conditions.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

“…Literature [18,20] implemented modulation classification combined with GA algorithm. On this basis, [17] proposed a KNN algorithm, which was used to improve PCC.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Modulation Classification Based on Extensible Neural Networks

Yang

2017

Mathematical Problems in Engineering

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A deep learning architecture based on Extensible Neural Networks is proposed for modulation classification in multipath fading channel. Expanded Neural Networks (ENN) are established based on energy natural logarithm model. The model is set up using hidden layers. Modulation classification based on ENN is implemented through the amplitude, phase, and frequency hidden network, respectively. In order to improve Probability of Correct classification (PCC), one or more communication signal features are extracted using hidden networks. Through theoretical proof, ENN learning network is demonstrated to be effective in improving PCC using amplitude, phase, and the frequency weight subnetwork, respectively. Compared with the traditional algorithms, the simulation results show that the proposed ENN has higher PCC than traditional algorithm for modulation classification within the same training sequence and Signal to Noise Ratio (SNR).

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“…Genetic programing has been used for classification problems other than modulation classification. It is recently used in combination with K-nearest neighbor for modulation classification in [1]. In [11] the continuous wavelet transform (CWT) is proposed to be used in distinguishing between phase shift keying (PSK) and FSK signals.…”

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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Autmomatic Modulation Classification Using Combination of Genetic Programming and KNN

Cited by 226 publications

References 30 publications

Intra-pulse modulation recognition using short-time ramanujan Fourier transform spectrogram

Intra-pulse modulation recognition using short-time ramanujan Fourier transform spectrogram

Modulation Classification Based on Extensible Neural Networks

Distinguishing CPFSK from QAM and PSK Modulations

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