Deep learning for wireless modulation classification based on discrete wavelet transform

Al‐Makhlasawy, Rasha M.; Ghanem, Hanan S.; Kassem, Hossam M.; Elsabrouty, Maha; Hamed, Hesham F. A.; El‐Samie, Fathi E. Abd; Salama, Gerges M.

doi:10.1002/dac.4980

Cited by 2 publications

(2 citation statements)

References 23 publications

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“…Another approach used in modulation recognition is that given by the use of the wavelet transform (Li et al, 2019;Al-Makhlasawy et al, 2021). Figure 19 shows the scalograms of the three signals, in the determination of which a 10th order Daubechies wavelet generator is used.…”

Section: Figure 13mentioning

confidence: 99%

Spread spectrum modulation recognition based on phase diagram entropy

Stanescu

Digulescu

Ioana

et al. 2023

Front. Signal Process.

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Wireless communication technologies are undergoing intensive study and are experiencing accelerated progress which leads to a large increase in the number of end-users. Because of this, the radio spectrum has become more crowded than ever. These previously mentioned aspects lead to the urgent need for more reliable and intelligent communication systems that can improve the spectrum efficiency. Specifically, modulation scheme recognition occupies a crucial position in the civil and military application, especially with the emergence of Software Defined Radio (SDR). The modulation recognition is an indispensable task while performing spectrum sensing in Cognitive Radio (CR). Spread spectrum (SS) techniques represent the foundation for the design of Cognitive Radio systems. In this work, we propose a new method of characterization of Spread spectrum modulations capable of providing relevant information for the process of recognition of this type of modulations. Using the proposed approach, results higher than 90% are obtained in the modulation classification process, thus bringing an advantage over the classical methods, whose performance is below 75%.

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Section: Figure 13mentioning

confidence: 99%

Spread spectrum modulation recognition based on phase diagram entropy

Stanescu

Digulescu

Ioana

et al. 2023

Front. Signal Process.

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“…Al‐Makhlasawy et al 12 investigated the AlexNet, VGG‐16, and VGG‐19 for AMC in wireless communication systems. They considered both the constellation diagrams and their wavelet transforms.…”

Section: Related Workmentioning

confidence: 99%

Automatic modulation classification with 2D transforms and convolutional neural network

Ghanem

Shoaib

El‐Gazar

et al. 2022

Trans Emerging Tel Tech

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This article focuses on automatic modulation classification (AMC) in wireless communication systems. A convolutional neural network (CNN) with three layers is introduced for the AMC process. Over degraded channels, it is assumed that the constellation diagrams of received signals do not show sharp points as in the case of pure signals. Instead, the points spread to constitute circle‐shaped objects. With more deterioration in channel conditions, these circle‐shaped objects begin to show overlapping. This behavior motivates us to use object detection, when dealing with the modulation classification task. The selection of the adopted transforms in this article is made from the object detection perspective. Different 2D transforms are considered on the constellation diagrams and compared for better classification performance. These transforms are the Radon transform (RT), the curvelet transform, and the phase congruency (PC). They are applied on the 2D constellation diagrams prior to the classification task with the CNN. The classification of the modulation format at different signal‐to‐noise ratios (SNRs) is considered in this article from the constellation diagrams, and the preprocessed constellation diagrams using RT, curvelet transform, and PC. Seven types of modulation formats are considered in this study to represent both spread and dense constellation diagram patterns, and the study extends from −10 to 10 dB. Analysis of the results indicating the most suitable preprocessor according to the constellation type and the SNR involved is provided.

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Deep learning for wireless modulation classification based on discrete wavelet transform

Cited by 2 publications

References 23 publications

Spread spectrum modulation recognition based on phase diagram entropy

Spread spectrum modulation recognition based on phase diagram entropy

Automatic modulation classification with 2D transforms and convolutional neural network

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