Binarized ResNet: Enabling Robust Automatic Modulation Classification at the Resource-Constrained Edge

Abstract

Recently, Deep Neural Networks (DNNs) have been used extensively for Automatic Modulation Classification (AMC). Due to their high complexity, DNNs are typically unsuitable for deployment at resource-constrained edge networks. 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 its low 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. 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 performances, with 4.75 times lower memory and 1214 times lower computation. Furthermore, RBLResNet exhibits high adversarial robustness compared to existing DNN models. The proposed MC method employing RBLResNets demonstrates a notable adversarial accuracy of 87.25% across a diverse spectrum of Signal-to-Noise Ratios (SNRs), outperforming existing methods and well-established defense mechanisms to the best of our knowledge. Low memory, low computation, and the highest adversarial robustness make it a better choice for robust AMC in low-power edge devices.