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.

中文翻译：

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用于无线干扰识别的低位宽卷积神经网络


无线干扰识别（WII）对于民用和军用场景中的非合作通信系统至关重要。最近，基于深度学习（DL）的 WII 方法被提出，并具有令人印象深刻的性能。然而，这些方法没有考虑 WII 基于深度学习的方法的量化问题，而量化问题是将深度神经网络部署到硬件单元中时必不可少的过程。本文解决了使用低精度权重以及 WII 激活来训练量化卷积神经网络 (CNN) 的问题。由于量化函数不可微，优化低位宽网络非常具有挑战性。为了减轻训练的难度，我们提出了三种有效的方法。首先，我们建议在全精度对应模型的指导下训练量化网络。量化网络可以向全精度对应网络学习。不幸的是，训练额外的全精度网络来辅助量化模型非常麻烦且计算成本昂贵。为此，我们进一步提出了一种训练机制，利用手动设计的概率分布来提供虚拟的全精度对应物，用于指导量化网络的训练，而无需额外的计算成本。第三，为了使梯度反向传播更容易，我们提出了新颖的辅助输出模块，它可以无缝地合并到所提出的量化网络中。实验结果验证了所提出方法的有效性。此外，结果表明，与使用标准网络架构的全精度网络相比，使用所提出的方法训练 3 位和 4 位精度网络可以提高性能。