An Automatic Digital Modulation Classifier for Measurement on Telecommunication Networks

Grimaldi, D.; Rapuano, Sergio; Vito, Luca De

doi:10.1109/tim.2007.895675

Cited by 87 publications

(29 citation statements)

References 22 publications

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“…A few of the Gaussianity tests that have been discussed in the literature have been proposed for this task [8,15,[19][20][21]. Although there is a vast number of tests available, some of them, such as χ 2 test or Epps test, are not well suited for digital modulation due to their high noise sensitivity.…”

Section: Novel Modulation Classification Algorithmmentioning

confidence: 99%

I-Q diagram utilization in a novel modulation classification technique for cognitive radio applications

Azarmanesh

Bilén

2013

J Wireless Com Network

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We report here on the utilization of signal in-phase-quadrature (I-Q) diagrams in a novel modulation classification (MC) technique. This MC technique is able to classify linear digital single-carrier modulations as well as multi-carrier modulations. The method uses the waveforms' I-Q diagrams and, by employing a combination of k-center and k-means algorithms, determines the type of modulation. Implementation and refinement of the novel single-carrier modulation classification technique using the I-Q diagrams are discussed in detail. Further, a model for classification of multi-carrier signals is presented, including Gaussianity, cyclostationarity, and autocorrelation tests for further extracting orthogonal frequency division multiplexing signal parameters. Finally, results of this method are presented and compared to other classification methods, and the considerations for implementing the method in hardware are briefly discussed. As a future direction of this research, the performance of the algorithm in fading channels is an interesting topic to pursue.

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Section: Novel Modulation Classification Algorithmmentioning

confidence: 99%

I-Q diagram utilization in a novel modulation classification technique for cognitive radio applications

Azarmanesh

Bilén

2013

J Wireless Com Network

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“…Communication modulation scheme is one of the most important technology in communication reconnaissance and anti-reconnaissance, and it has been widely used in military and civil fields [4][5][6][7][8][9]. Hence, the research of automatic modulation recognition (AMR) of digital signals should be pay more attention.…”

Section: Introductionmentioning

confidence: 99%

Research on modulation recognition with ensemble learning

Liu

Guan

Lin

2017

J Wireless Com Network

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Modulation scheme recognition occupies a crucial position in the civil and military application. In this paper, we present boosting algorithm as an ensemble frame to achieve a higher accuracy than a single classifier. To evaluate the effect of boosting algorithm, eight common communication signals are yet to be identified. And five kinds of entropy are extracted as the training vector. And then, AdaBoost algorithm based on decision tree is utilized to confirm the idea of boosting algorithm. The results illustrate AdaBoost is always a superior classifier, while, as a weak estimator, decision tree is barely satisfactory. In addition, the performance of three diverse boosting members is compared by experiments. Gradient boosting has better behavior than AdaBoost, and xgboost creates optimal cost performance especially.

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“…In intra-pulse modulation recognition context, more interest has been focused on the study of the feature based (FB) algorithms [2][3][4][5]. Thereinto, as a significant means to FB, the time-frequency analysis has been developed because it allows description of the instantaneous characteristics of a signal in the two-dimensional (2D) timefrequency space [6][7][8][9][10][11][12][13][14][15].…”

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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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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An Automatic Digital Modulation Classifier for Measurement on Telecommunication Networks

Cited by 87 publications

References 22 publications

I-Q diagram utilization in a novel modulation classification technique for cognitive radio applications

I-Q diagram utilization in a novel modulation classification technique for cognitive radio applications

Research on modulation recognition with ensemble learning

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

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