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

TL;DR

This paper introduces a low-resource, binarized ResNet model for automatic modulation classification at the edge, achieving high accuracy and robustness with significantly reduced memory and computation.

Contribution

It proposes a rotated binary ResNet architecture with ensemble methods, enabling robust AMC on resource-constrained edge devices with competitive accuracy and adversarial resilience.

Findings

Achieves 93.39% accuracy at 10dB on Deepsig dataset

Uses 4.75 times less memory and 1214 times less computation than SOTA

Surpasses existing methods in adversarial robustness

Abstract

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 $10$dB 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.