Low-Bitwidth Convolutional Neural Networks for Wireless Interference Identification

Abstract

Wireless interference identification (WII) is critical for non-cooperative communication systems in both civilian and military scenarios. Recently, deep learning (DL) based WII methods have been proposed with impressive performances. However, these methods did not consider the quantization problem for DL based methods of WII, which is an indispensable process when deploying deep neural networks into hardware units. This paper addresses the problem of training quantized convolutional neural networks (CNNs), with low-precision weights as well as activations for WII. Optimizing a low-bit width network is very challenging due to the non-differentiable quantization function. To mitigate the difficulty of training, we propose three effective approaches. Firstly, we propose to train the quantized network with the guidance of the full-precision counterpart. The quantized network can learn from the full-precision counterpart. Unfortunately, training an extra full-precision network to assist a quantized model is cumbersome and computationally expensive. To this end, we further propose a training mechanism which makes use of the manually designed probability distributions to provide a virtual full-precision counterpart for guiding the training of the quantization network without extra computational cost. Thirdly, to make the gradient back-propagates more easily, we propose novel auxiliary output modules, which can be seamlessly incorporated into the proposed quantization networks. Experimental results validate the effectiveness of the proposed methods. Furthermore, it is shown that training 3-bit and 4-bit precision networks with the proposed methods leads to performance improvement as compared to their full precision counterparts with standard network architectures.