ALWNN Empowered Automatic Modulation Classification: Conquering Complexity and Scarce Sample Conditions

• URL: http://arxiv.org/abs/2503.18375v1
• Date: Mon, 24 Mar 2025 06:14:33 GMT
• Title: ALWNN Empowered Automatic Modulation Classification: Conquering Complexity and Scarce Sample Conditions
• Authors: Yunhao Quan, Chuang Gao, Nan Cheng, Zhijie Zhang, Zhisheng Yin, Wenchao Xu, Danyang Wang,
• Abstract summary: This paper proposes an automatic modulation classification model based on the Adaptive Lightweight Wavelet Neural Network (ALWNN) and the few-shot framework (MALWNN)<n>The ALWNN model, by integrating the adaptive wavelet neural network and depth separable convolution, reduces the number of model parameters and computational complexity.<n> Experiments with MALWNN show its superior performance in few-shot learning scenarios compared to other algorithms.
• Score: 24.59462798452397
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In Automatic Modulation Classification (AMC), deep learning methods have shown remarkable performance, offering significant advantages over traditional approaches and demonstrating their vast potential. Nevertheless, notable drawbacks, particularly in their high demands for storage, computational resources, and large-scale labeled data, which limit their practical application in real-world scenarios. 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). The ALWNN model, by integrating the adaptive wavelet neural network and depth separable convolution, reduces the number of model parameters and computational complexity. The MALWNN framework, using ALWNN as an encoder and incorporating prototype network technology, decreases the model's dependence on the quantity of samples. Simulation results indicate that this model performs remarkably well on mainstream datasets. Moreover, in terms of Floating Point Operations Per Second (FLOPS) and Normalized Multiply - Accumulate Complexity (NMACC), ALWNN significantly reduces computational complexity compared to existing methods. This is further validated by real-world system tests on USRP and Raspberry Pi platforms. Experiments with MALWNN show its superior performance in few-shot learning scenarios compared to other algorithms.

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