Automatic Modulation Recognition for Secondary Modulated Signals

Chu, Peng; Xie, Lijin; Dai, Chuanjin; Chen, Yarong

doi:10.1109/lwc.2021.3051803

Cited by 15 publications

(5 citation statements)

References 12 publications

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“…The block diagram of the proposed MR process is shown in Figure . 2. Recent works have demonstrated that feature-based algorithms are very efficient and improve classification performance [15][16][17]23]. Higher-order cumulants (HOC) are retained in the present paper to provide enough information to efficiently recognize the modulation.…”

Section: Feature Extraction and Recognition Toolmentioning

confidence: 99%

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Automatic Modulation Recognition in Two- Way Relaying Channel with the Presence of Relay Transceiver Hardware Impairments

Tayakout

Bouchibane

Boutellaa

et al. 2023

Preprint

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Modulation recognition (MR) is well-known as one of the enabling tools, which aims to further afford efficient and secure communications in cognitive radio (CR) context for 5G and beyond networks. In this Paper, we propose a robust MR approach designed for two-way MIMO cooperative relaying network by specifically taking into account two crucial constraints: hardware impairments at relay transceiver and co-channel interference. To mitigate the effect of these impairments, feature-based artificial neural network (ANN) recognizer combined with a nonlinear design of the relay processing matrix with dirty paper coding (DPC) is investigated in this work. Simulations using two receive criterions, namely DPC-ZF and DPC-MMSE, have been carried out to validate the effectiveness of the proposed approach. It has been proved that both DPC-ZF and DPC-MMSE provide high modulation recognition, whatever the impairments level at the relay transceiver.

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Section: Feature Extraction and Recognition Toolmentioning

confidence: 99%

“…Under more extreme circumstances, only a few samples can be obtained and these methods will be not effective anymore. However, recent works have demonstrated that feature-based algorithms are still very efficient and improve classification performance with few training data [15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Automatic Modulation Recognition in Two- Way Relaying Channel with the Presence of Relay Transceiver Hardware Impairments

Tayakout

Bouchibane

Boutellaa

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Chu et al 10 have considered the problem of AMC of secondary modulated signals. This scenario comprises both analog and digital signals integrated together in a single signal format.…”

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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“…AMR is widely used in military and civilian fields, such as information interception, interference recognition, electronic countermeasures, and spectrum management. [2]. Therefore, AMR technology has been widely researched by domestic and foreign scholars [3].…”

Section: Introductionmentioning

confidence: 99%

Modulation recognition of communication signals based on cascade network

Hou,

Liu

2024

IEICE Trans. Commun.

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To improve the recognition rate of the end-to-end modulation recognition method based on deep learning, a modulation recognition method of communication signals based on a cascade network is proposed, which is composed of two networks: Stacked Denoising Auto Encoder (SDAE) network and DCELDNN(Dilated Convolution, ECA Mechanism, Long Short-Term Memory, Deep Neural Networks) network. SDAE network is used to denoise the data, reconstruct the input data through encoding and decoding, and extract deep information from the data. DCELDNN network is constructed based on the CLDNN (Convolutional, Long Short-Term Memory, Fully Connected Deep Neural Networks) network. In the DCELDNN network, dilated convolution is used instead of normal convolution to enlarge the receptive field and extract signal features, the Efficient Channel Attention (ECA) mechanism is introduced to enhance the expression ability of the features, the feature vector information is integrated by a Global Average Pooling (GAP) layer, and signal features are extracted by the DCELDNN network efficiently. Finally, end-to-end classification recognition of communication signals is realized. The test results on the RadioML2018.01a dataset show that the average recognition accuracy of the proposed method reaches 63.1% at SNR of -10 to 15dB, compared with CNN, LSTM, and CLDNN models, the recognition accuracy is improved by 25.8%, 12.3%, and 4.8% respectively at 10dB SNR.

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Automatic Modulation Recognition for Secondary Modulated Signals

Cited by 15 publications

References 12 publications

Automatic Modulation Recognition in Two- Way Relaying Channel with the Presence of Relay Transceiver Hardware Impairments

Automatic Modulation Recognition in Two- Way Relaying Channel with the Presence of Relay Transceiver Hardware Impairments

Automatic modulation classification with 2D transforms and convolutional neural network

Modulation recognition of communication signals based on cascade network

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