# SSwsrNet: A Semi-Supervised Few-Shot Learning Framework for Wireless Signal Recognition

arXiv: https://arxiv.org/abs/2404.02467 · HTML: https://mlsanity.com/paper/2404.02467/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2024.

## Built on by (most cited first, 4 of 4)

- [SpectrumFM: A Foundation Model for Intelligent Spectrum Management](https://mlsanity.com/paper/2505.06256/) (2025)
- [DenoMAE2.0: Improving Denoising Masked Autoencoders by Classifying Local Patches](https://mlsanity.com/paper/2502.18202/) (2025)
- [Revolution of Wireless Signal Recognition for 6G: Recent Advances, Challenges and Future Directions](https://mlsanity.com/paper/2503.08091/) (2025)
- [SpectrumFM: Redefining Spectrum Cognition via Foundation Modeling](https://mlsanity.com/paper/2508.02742/) (2025)

## Abstract (arXiv)

Wireless signal recognition (WSR) is crucial in modern and future wireless communication networks since it aims to identify properties of the received signal. Although many deep learning-based WSR models have been developed, they still rely on a large amount of labeled training data. Thus, they cannot tackle the few-sample problem in the practically and dynamically changing wireless communication environment. To overcome this challenge, a novel SSwsrNet framework is proposed by using the deep residual shrinkage network (DRSN) and semi-supervised learning. The DRSN can learn discriminative features from noisy signals. Moreover, a modular semi-supervised learning method that combines labeled and unlabeled data using MixMatch is exploited to further improve the classification performance under few-sample conditions. Extensive simulation results on automatic modulation classification (AMC) and wireless technology classification (WTC) demonstrate that our proposed WSR scheme can achieve better performance than the benchmark schemes in terms of classification accuracy. This novel method enables more robust and adaptive signal recognition for next-generation wireless networks.