Digital Modulation Identification by Wavelet Analysis

Chen, Jian; Kuo, Yan-Fu; Li, Jiandong; Fu, Fenglin; Ma, Yubao

doi:10.1109/iccima.2005.23

Cited by 5 publications

(1 citation statement)

References 6 publications

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“…To demonstrate the effectiveness of the proposed AMC method based on GCN, we compared the performance of the proposed method with those state-of-the-art AMC methods. The achieved methods include deep learning methods (basic CNN [44], InceptionV3 [45], GAN [29], VGGnet [30], ResNet [46,47], LSTM [48,49], deep complex network (DCN) [1]), and feature extraction methods (HOC [3,4] using an SVM classifier, CS [50] with a neural network classifier, and continuous wavelet transform (CWT) [11,51] with an SVM classifier). We carried out the comparison experiments in Ch1 and Ch2, respectively.…”

Section: The Analysis Of the Influence Of The Different Featuresmentioning

confidence: 99%

Feature Fusion Based on Graph Convolution Network for Modulation Classification in Underwater Communication

Yao

Yang

Sheng

2023

Entropy

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Automatic modulation classification (AMC) of underwater acoustic communication signals is of great significance in national defense and marine military. Accurate modulation classification methods can make great contributions to accurately grasping the parameters and characteristics of enemy communication systems. While a poor underwater acoustic channel makes it difficult to classify the modulation types correctly. Feature extraction and deep learning methods have proven to be effective methods for the modulation classification of underwater acoustic communication signals, but their performance is still limited by the complex underwater communication environment. Graph convolution networks (GCN) can learn the graph structured information of the data, making it an effective method for processing structured data. To improve the stability and robustness of AMC in underwater channels, we combined the feature extraction and deep learning methods by fusing the multi-domain features and deep features using GCN. The proposed method takes the relationships among the different multi-domain features and deep features into account. Firstly, a feature graph was built using the properties of the features. Secondly, multi-domain features were extracted from the received signals and deep features were extracted from the signals using a deep neural network. Thirdly, we constructed the input of GCN using these features and the graph. Then, the multi-domain features and deep features were fused by the GCN. Finally, we classified the modulation types using the output of GCN by way of a softmax layer. We conducted the experiments on a simulated dataset and a real-world dataset, respectively. The results show that the AMC based on GCN can achieve a significant improvement in performance compared to the current state-of-the-art methods. Our approach is robust in underwater acoustic channels.

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Section: The Analysis Of the Influence Of The Different Featuresmentioning

confidence: 99%

Feature Fusion Based on Graph Convolution Network for Modulation Classification in Underwater Communication

Yao

Yang

Sheng

2023

Entropy

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A Minimal Fuzzy Radial Basis Function Neural Network For Recognition Of Digitally Modulated Signals Of Multilevel Magnitude

Khurshid¹,

Gokhale

2008

2008 IEEE Region 10 and the Third International Conference on Industrial and Information Systems

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Digital Modulation Identification in terms of Software Defined Radio for Traffic Management System

Bakhraiba

Khatun

Noordin

et al. 2008

2008 6th National Conference on Telecommunication Technologies and 2008 2nd Malaysia Conference on Photonics

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Nowadays, a lot of efforts have been given to reduce hardware and software development cost. Many of today's military and commercial communication systems use Software Defined Radio (SDR) technology to create various different hardware combinations in term of software control. In this paper, we present an approach to design a SDR based traffic management system by tuning between two different digital modulation schemes: ASK and FSK. The wavelet transform technique has been used for modulation identification. The performance of the identification scheme is investigated through simulations, for different signal to noise ratio (SNR). The system identification performance is improved more than 95% when SNR is more than 9dB.

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Digital Modulation Identification by Wavelet Analysis

Cited by 5 publications

References 6 publications

Feature Fusion Based on Graph Convolution Network for Modulation Classification in Underwater Communication

Feature Fusion Based on Graph Convolution Network for Modulation Classification in Underwater Communication

A Minimal Fuzzy Radial Basis Function Neural Network For Recognition Of Digitally Modulated Signals Of Multilevel Magnitude

Digital Modulation Identification in terms of Software Defined Radio for Traffic Management System

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