LAUSR

Wireless interference identification (WII) is one of the most promising technologies in anti-interference communication systems and non-cooperative communication systems. Recently, deep learning (DL) based methods have achieved striking performance for WII. However, existing works have not researched hardware-friendly network quantization for WII, which is essential for deploying model on resource-constrained devices. In this letter, we optimize binarized neural networks (BNNs) for WII, which are the most extreme form of quantization by constraining weights and activations to be binary value. Specifically, to overcome the difficulty in propagating gradient during back-propagation due to the non-differentiable quantization function, we introduce a novel approximation for gradients calculation, which bridges the accuracy gap between the BNN and the full-precision counterpart. Additionally, to solve the bottleneck of serious performance degradation of BNNs, we propose two techniques to minimize quantization noise and create multiple routes to update the parameters of BNNs, resulting in further improvement on performance. Experiments demonstrate that the proposed BNN achieves accuracy near the full-precision counterpart with substantially reduced resource consumption for WII.