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

arXiv: https://arxiv.org/abs/2503.18375 · HTML: https://mlsanity.com/paper/2503.18375/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 50 references that are in the map.
- First posted on arXiv 24 Mar 2025.

## Builds on (most cited first, 14 of 14)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination](https://mlsanity.com/paper/1805.01978/) (2018)
- [An Empirical Survey of Data Augmentation for Time Series Classification with Neural Networks](https://mlsanity.com/paper/2007.15951/) (2020)
- [Over the Air Deep Learning Based Radio Signal Classification](https://mlsanity.com/paper/1712.04578/) (2017)
- [RadioDiff: An Effective Generative Diffusion Model for Sampling-Free Dynamic Radio Map Construction](https://mlsanity.com/paper/2408.08593/) (2024)
- [Deep Architectures for Modulation Recognition](https://mlsanity.com/paper/1703.09197/) (2017)
- [An Efficient Deep Learning Model for Automatic Modulation Recognition Based on Parameter Estimation and Transformation](https://mlsanity.com/paper/2110.04980/) (2021)
- [Self-Contrastive Learning based Semi-Supervised Radio Modulation Classification](https://mlsanity.com/paper/2203.15932/) (2022)
- [AMC-Net: An Effective Network for Automatic Modulation Classification](https://mlsanity.com/paper/2304.00445/) (2023)
- [SigT: An Efficient End-to-End MIMO-OFDM Receiver Framework Based on Transformer](https://mlsanity.com/paper/2211.09712/) (2022)
- [Multiple Access Techniques for Intelligent and Multi-Functional 6G: Tutorial, Survey, and Outlook](https://mlsanity.com/paper/2401.01433/) (2024)
- [Fast Geometric Learning of MIMO Signal Detection over Grassmannian Manifolds](https://mlsanity.com/paper/2406.10453/) (2024)
- [Failure-Resilient Distributed Inference with Model Compression over Heterogeneous Edge Devices](https://mlsanity.com/paper/2406.14185/) (2024)

## Abstract (arXiv)

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.