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- Toward the Automatic Modulation Classification With Adaptive Wavelet . . .
Abstract: With the evolutionary development of modern communications technology, automatic modulation classification (AMC) has played an increasing role in the complex wireless communication environment Existing AMC schemes based on deep learning use a neural network to extract features and calculate feature maps, then feed them into fully
- Code for Towards the Automatic Modulation Classification with Adaptive . . .
Code for "Towards the Automatic Modulation Classification with Adaptive Wavelet Network" Jiawei Zhang, Tiantian Wang, ZhixiFeng , and Shuyuan Yang Xidian University
- ALWNN Empowered Automatic Modulation Classification: Conquering . . .
this paper innovatively proposes an automatic modulation classifi-cation model based on the Adaptive Lightweight Wavelet Neural Network (ALWNN) and the few-shot framework (MALWNN) The ALWNN model, by integrating the adaptive wavelet neural network and depth separable convolution, reduces the number of model parameters and computational complexity
- Toward the Automatic Modulation Classification With Adaptive Wavelet . . .
In this article, a novel end-to-end AMC model called a complex-valued depth-wise separable convolutional neural network (CDSCNN) is proposed, which adopts complex-valued operation
- Toward the Automatic Modulation Classification With Adaptive Wavelet . . .
AWN explores a novel AMC paradigm that efficiently integrates the inherent properties of the signal by introducing prior knowledge in the frequency domain Simulation results demonstrate that our proposed AMC scheme outperforms the benchmark scheme and has rather low computational complexity 随着现代通信技术的演进发展，自动调制分类（AMC）在复杂的无线通信环境中发挥着越来越大的作用。
- Toward the Automatic Modulation Classification With Adaptive Wavelet . . .
- "Toward the Automatic Modulation Classification With Adaptive Wavelet Network" Fig 3 AMC average accuracy on various SNRs comparisons of different baseline methods are conducted on the RML2016 10a (a), RML2016 10b (b)
- Automatic Modulation Classification Based on wavelet Image and . . .
The authors propose an end-to-end signal modulation classification method based on wavelet image and convolutional neural network, which characterizes the intra-pulse signal based on continuous wavelet transform(CWT), and design a convolutional neural network to extract and classify the characteristics of signal wavelet time-frequency images
- [2503. 18375] ALWNN Empowered Automatic Modulation Classification . . .
To tackle this issue, this paper innovatively proposes an automatic modulation classification model based on the Adaptive Lightweight Wavelet Neural Network (ALWNN) and the few-shot framework (MALWNN)
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