Modulation Classification of MFSK Modulated Signals Using Spectral Centroid

Baris, Burcu; Cek, Mehmet Emre; Kuntalp, Damla

doi:10.1007/s11277-021-08236-2

Cited by 11 publications

(11 citation statements)

References 18 publications

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“…In order to demonstrate the contribution of this study, the table needs to be examined carefully. It can be concluded that -Some of the works consider either only low order modulation schemes [14,29,35] and/or limited number of modulation schemes [15,16,30,31,32,36,37]. -Many [15,16,24,30,31,32,33,34,35,36,37] use simulated channel effects with computer generated data rather than OTA.…”

Section: Resultsmentioning

confidence: 99%

“…It can be concluded that -Some of the works consider either only low order modulation schemes [14,29,35] and/or limited number of modulation schemes [15,16,30,31,32,36,37]. -Many [15,16,24,30,31,32,33,34,35,36,37] use simulated channel effects with computer generated data rather than OTA. -SVM based classifications [16,34,32,35] generally achieve 90-95% accuracy but only for limited number of modulation classes, and/or with synthetically simulated channel.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

On the Classification of Modulation Schemes Using Higher Order Statistics and Support Vector Machines

Coruk

Gokdogan

Benzaghta

et al. 2022

Wireless Pers Commun

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The recognition of modulation schemes in military and civilian applications is a major task for intelligent receiving systems. Having no previous knowledge of the transmitted signal as well as uncertainties in the channel and the receiver makes the identification of the modulation scheme a difficult task. Various Automatic Modulation Classification (AMC) algorithms have been developed to overcome this challenging task. However, classification with low computational complexity as well as reasonable processing time is still a challenge, especially, for modulation types with similar constellations under realistic channel conditions. In this paper, a feature-based approach along with various classifiers is employed based on statistical features as well as higherorder moments and cumulants. First, some well-known classifiers including Decision Trees (DT), Support Vector Machines (SVM), K-Nearest Neighbors (KNN) and ensemble are evaluated at different SNR values. Then, various forms of SVMs have been utilized. An over-the-air (OTA) recorded dataset consisting of four analog and ten digital modulation schemes are used for test-

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

On the Classification of Modulation Schemes Using Higher Order Statistics and Support Vector Machines

Coruk

Gokdogan

Benzaghta

et al. 2022

Wireless Pers Commun

View full text Add to dashboard Cite

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

On the Classification of Modulation Schemes using Higher Order Statistics and Support Vector Machines

Coruk

Yalcinkaya

Benzaghta

et al. 2021

Preprint

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The recognition of modulation schemes in military and civilian applications is a major task for intelligent receiving systems. Having no previous knowledge of the transmitted signal as well as uncertainties in the channel and the receiver makes the identification of the modulation scheme a difficult task. Various Automatic Modulation Classification (AMC) algorithms have been developed to overcome this challenging task. However, classification with low computational complexity as well as reasonable processing time is still a challenge, especially, for modulation types with similar constellations under realistic channel conditions. In this paper, a feature-based approach along with various classifiers is employed based on statistical features as well as higher-order moments and cumulants. First, some well-known classifiers including Decision Trees (DT), Support Vector Machines (SVM), K-Nearest Neighbors (KNN) and ensemble are evaluated at different SNR values. Then, various forms of SVMs have been utilized. An over-the-air (OTA) recorded dataset consisting of four analog and ten digital modulation schemes are used for testing the proposed method. The classification performance is compared against SNR (0-20 dB). The overall accuracy for quadratic SVM is found to be as high as 98% at 10 dB. This has been comparable with the reported performance based on simulated dataset. Overall, the comparison of the results with those published in the literature indicates that this is the first paper presenting such a high accuracy with OTA dataset consisting of fourteen modulation schemes.

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“…Two random erasing-based data augmentation strategies were also included to improve the model’s generalization capability and robustness. The problem of enhancing the M-FSK signal modulation classification performance in the context of an AWGN was investigated in [ 41 ]. In [ 42 ], an optimal modulation classification technique was presented, which combined Gabor feature extraction and cuckoo search optimization (CSO).…”

Section: Related Workmentioning

confidence: 99%

Automatic Modulation Recognition Based on the Optimized Linear Combination of Higher-Order Cumulants

Hussain

Alam

Ghauri

et al. 2022

Sensors

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Automatic modulation recognition (AMR) is used in various domains—from general-purpose communication to many military applications—thanks to the growing popularity of the Internet of Things (IoT) and related communication technologies. In this research article, we propose an innovative idea of combining the classical mathematical technique of computing linear combinations (LCs) of cumulants with a genetic algorithm (GA) to create super-cumulants. These super-cumulants are further used to classify five digital modulation schemes on fading channels using the K-nearest neighbor (KNN). Our proposed classifier significantly improves the percentage recognition accuracy at lower SNRs when using smaller sample sizes. A comparison with existing techniques manifests the supremacy of our proposed classifier.

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Modulation Classification of MFSK Modulated Signals Using Spectral Centroid

Cited by 11 publications

References 18 publications

On the Classification of Modulation Schemes Using Higher Order Statistics and Support Vector Machines

On the Classification of Modulation Schemes Using Higher Order Statistics and Support Vector Machines

On the Classification of Modulation Schemes using Higher Order Statistics and Support Vector Machines

Automatic Modulation Recognition Based on the Optimized Linear Combination of Higher-Order Cumulants

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