# Binarized ResNet: Enabling Robust Automatic Modulation Classification at the resource-constrained Edge

arXiv: https://arxiv.org/abs/2110.14357 · HTML: https://mlsanity.com/paper/2110.14357/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 66 references that are in the map.
- First posted on arXiv 27 Oct 2021.

## Builds on (most cited first, 20 of 20)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Learning both Weights and Connections for Efficient Neural Networks](https://mlsanity.com/paper/1506.02626/) (2015)
- [XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks](https://mlsanity.com/paper/1603.05279/) (2016)
- [Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations](https://mlsanity.com/paper/1609.07061/) (2016)
- [Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation](https://mlsanity.com/paper/1404.0736/) (2014)
- [Ensemble deep learning: A review](https://mlsanity.com/paper/2104.02395/) (2021)
- [Over the Air Deep Learning Based Radio Signal Classification](https://mlsanity.com/paper/1712.04578/) (2017)
- [Convolutional Radio Modulation Recognition Networks](https://mlsanity.com/paper/1602.04105/) (2016)
- [Rotated Binary Neural Network](https://mlsanity.com/paper/2009.13055/) (2020)
- [Adversarial Attacks on Deep-Learning Based Radio Signal Classification](https://mlsanity.com/paper/1808.07713/) (2018)
- [Distributed Deep Learning Models for Wireless Signal Classification with Low-Cost Spectrum Sensors](https://mlsanity.com/paper/1707.08908/) (2017)
- [Binary Ensemble Neural Network: More Bits per Network or More Networks per Bit?](https://mlsanity.com/paper/1806.07550/) (2018)
- [Attacking Binarized Neural Networks](https://mlsanity.com/paper/1711.00449/) (2017)
- [Unsupervised Representation Learning of Structured Radio Communication Signals](https://mlsanity.com/paper/1604.07078/) (2016)
- [Black-box Adversarial ML Attack on Modulation Classification](https://mlsanity.com/paper/1908.00635/) (2019)
- [Automatic Modulation Classification Using Involution Enabled Residual Networks](https://mlsanity.com/paper/2108.10001/) (2021)
- [Realizing Neural Decoder at the Edge with Ensembled BNN](https://mlsanity.com/paper/2106.09925/) (2021)

## Built on by (most cited first, 3 of 3)

- [MCEL: Margin-Based Cross-Entropy Loss for Error-Tolerant Quantized Neural Networks](https://mlsanity.com/paper/2603.05048/) (2026)
- [Golden Ratio Search: A Low-Power Adversarial Attack for Deep Learning based Modulation Classification](https://mlsanity.com/paper/2409.11454/) (2024)
- [Federated Learning of Binary Neural Networks: Enabling Low-Cost Inference](https://mlsanity.com/paper/2603.15507/) (2026)

## Abstract (arXiv)

Recently, deep neural networks (DNNs) have been used extensively for automatic modulation classification (AMC), and the results have been quite promising. However, DNNs have high memory and computation requirements making them impractical for edge networks where the devices are resource-constrained. They are also vulnerable to adversarial attacks, which is a significant security concern. This work proposes a rotated binary large ResNet (RBLResNet) for AMC that can be deployed at the edge network because of low memory and computational complexity. The performance gap between the RBLResNet and existing architectures with floating-point weights and activations can be closed by two proposed ensemble methods: (i) multilevel classification (MC), and (ii) bagging multiple RBLResNets while retaining low memory and computational power. The MC method achieves an accuracy of 93.39% at 10dB over all the 24 modulation classes of the Deepsig dataset. This performance is comparable to state-of-the-art (SOTA) performances, with 4.75 times lower memory and 1214 times lower computation. Furthermore, RBLResNet also has high adversarial robustness compared to existing DNN models. The proposed MC method with RBLResNets has an adversarial accuracy of 87.25% over a wide range of SNRs, surpassing the robustness of all existing SOTA methods to the best of our knowledge. Properties such as low memory, low computation, and the highest adversarial robustness make it a better choice for robust AMC in low-power edge devices.