Modulation Order Reduction Method for Improving the Performance of AMC Algorithm Based on Sixth–Order Cumulants

Zemun, Pajić, M., Poljoprivredni fakultet, Beograd; Veinović, Mladen; Peric, Miroslav

doi:10.1109/access.2020.3000358

Cited by 12 publications

(9 citation statements)

References 21 publications

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“…This approach suffers from the high complexity involved in estimating the cumulants for all received signals. To allow good performance with the sixth‐order cumulants, Pajic et al 6 presented a preprocessing step to reduce the modulation format first prior to the classification process. They added a new step prior to higher‐order cumulant estimation, which corresponds directly to an additional computational burden.…”

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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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

View full text Add to dashboard Cite

show abstract

“…Considering the complementary information of time-domain, the instantaneous amplitude (phase) statistics and higher-order cumulants are extracted as the statistical features for fusion, which can reveal statistical time-varying information [ 26 ]. Meanwhile, the higher-order cumulants are insensitive to Gaussian noise and robust to phase rotation, see [ 27 ] and [ 28 ].…”

Section: The Proposed Frameworkmentioning

confidence: 99%

Automatic Modulation Classification Based on Deep Feature Fusion for High Noise Level and Large Dynamic Input

Han

Ren

et al. 2021

Sensors

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Automatic modulation classification (AMC) is playing an increasingly important role in spectrum monitoring and cognitive radio. As communication and electronic technologies develop, the electromagnetic environment becomes increasingly complex. The high background noise level and large dynamic input have become the key problems for AMC. This paper proposes a feature fusion scheme based on deep learning, which attempts to fuse features from different domains of the input signal to obtain a more stable and efficient representation of the signal modulation types. We consider the complementarity among features that can be used to suppress the influence of the background noise interference and large dynamic range of the received (intercepted) signals. Specifically, the time-series signals are transformed into the frequency domain by Fast Fourier transform (FFT) and Welch power spectrum analysis, followed by the convolutional neural network (CNN) and stacked auto-encoder (SAE), respectively, for detailed and stable frequency-domain feature representations. Considering the complementary information in the time domain, the instantaneous amplitude (phase) statistics and higher-order cumulants (HOC) are extracted as the statistical features for fusion. Based on the fused features, a probabilistic neural network (PNN) is designed for automatic modulation classification. The simulation results demonstrate the superior performance of the proposed method. It is worth noting that the classification accuracy can reach 99.8% in the case when signal-to-noise ratio (SNR) is 0 dB.

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“…The instantaneous amplitude, phase, and frequency features have poor anti-noise ability, and the effect is not good when used alone [5]. High-order statistics can effectively suppress the interference of Gaussian noise by taking advantage of the property that the high second-order cumulant of Gaussian noise is equal to zero [6]. It is suitable for amplitude or phase modulation signals, but it needs to extract the symbol sequence synchronously.…”

Section: Introductionmentioning

confidence: 99%

Modulation Recognition of Communication Signals Based on Multimodal Feature Fusion

Zhang

Gong

et al. 2022

Sensors

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Modulation recognition is the indispensable part of signal interception analysis, which has always been the research hotspot in the field of radio communication. With the increasing complexity of the electromagnetic spectrum environment, interference in signal propagation becomes more and more serious. This paper proposes a modulation recognition scheme based on multimodal feature fusion, which attempts to improve the performance of modulation recognition under different channels. Firstly, different time- and frequency-domain features are extracted as the network input in the signal preprocessing stage. The residual shrinkage building unit with channel-wise thresholds (RSBU-CW) was used to construct deep convolutional neural networks to extract spatial features, which interact with time features extracted by LSTM in pairs to increase the diversity of the features. Finally, the PNN model was adapted to make the features extracted from the network cross-fused to enhance the complementarity between features. The simulation results indicated that the proposed scheme has better recognition performance than the existing feature fusion schemes, and it can also achieve good recognition performance in multipath fading channels. The test results of the public dataset, RadioML2018.01A, showed that recognition accuracy exceeds 95% when the signal-to-noise ratio (SNR) reaches 8 dB.

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Modulation Order Reduction Method for Improving the Performance of AMC Algorithm Based on Sixth–Order Cumulants

Cited by 12 publications

References 21 publications

Automatic modulation classification with 2D transforms and convolutional neural network

Automatic modulation classification with 2D transforms and convolutional neural network

Automatic Modulation Classification Based on Deep Feature Fusion for High Noise Level and Large Dynamic Input

Modulation Recognition of Communication Signals Based on Multimodal Feature Fusion

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