【作者】 赵莹；

【导师】 牛斌；

【作者基本信息】 辽宁大学 ， 软件工程， 2020， 硕士

【摘要】 随着通信领域技术的发展,人们之间的交流越来越便捷,随之而来的是无线设备数量的大幅度增长,对频谱资源的需求也越来越高。与此同时,学者的研究方向除了如何提高频谱资源利用率外,保密通信也成为主要研究方向之一。为避免出现窃听等情况,发送方可随机更改信号的调制方式以保证通信的安全,因此在接受方则需要进行信号调制方式自适应分类。信号调制方式自适应分类在民用和军事的无线通信领域起着至关重要的作用。当前信号调制分类多是利用神经网络进行基于特征的分类,可通过对数据集和网络结构修改进一步提高分类准确率。首先,本文通过采用改进的Triple-GAN网络对原始的数据集进行扩展,增加训练样例,解决了由于训练数据不足和过拟合导致的卷积神经网络在信号调制分类方面受限的问题,而且新生成的样例可提供额外的辅助信息来训练分类网络。利用扩展后的数据集对信号调制方式分类网络进行训练,可以提高信号调制方式分类的准确率。并对数据集中的IQ向量进行计算,得到其所对应的瞬时幅度和相位,将计算后的结果作为网络的输入,来验证生成的数据是否具有真实意义。其次,本文通过对分类网络进行改进,将在分类方面具有卓越性能的ResNeXt网络应用到信号调制方式自适应分类网络中来提高网络的分类性能。同时因一些便携式设备受硬件限制,无法部署大型网络,故将改进后的网络通过知识蒸馏进行网络压缩,利用迁移学习,使简单的网络实现较高的分类性能。最后,采用了不同的信号调制方式分类网络进行实验对比,实验结果表明,经过改进的Triple-GAN网络对原始的数据集扩展后,将生成的伪样例加入原始数据集,可对信号的分类准确率起到促进作用。另一方面,改进后的网络实现了更高分类准确率,且在改进网络的引导下,简单的卷积网络也实现了较高的分类性能。更多还原

【Abstract】 The development of technology in the field of communication facilitates communication between people,which results in a large increase in the number of wireless devices and demand for spectrum resources.At the same time,the research direction of scholars is not only how to improve the utilization rate of spectrum resources,but also how to ensure secure communication.To avoid eavesdropping and other situations,the sender can randomly change the modulation method of the signal to ensure the safety of communication.Meanwhile,the receiver needs to perform signal modulation adaptive classification,which plays an important role in the field of civil and military wireless communication.At present,the research direction of signal adaptive modulation classification is to use neural network for feature-based classification,and the classification accuracy can be further improved by modifying the dataset and network structure.First of all,in this paper,an improved Triple-GAN network is used to expand the original dataset,which solves a problem that the convolutional neural network was limited in signal modulation classification due to insufficient training data and over-fitting.Furthermore,the newly generated samples provide additional auxiliary information to train the classification network.The accuracy of signal modulation classification can be improved by using the expanded dataset to train the signal modulation classification network.The IQ vectors in the expanded dataset are calculated to obtain the corresponding instantaneous amplitudes and phases,and the calculated results are taken as the input of the network to verify whether the generated data has a real meaning.Secondly,in this paper,by improving the classification network,the ResNeXt with excellent performance in classification is applied to the adaptive classification network to improve the classification performance of the network.However,some portable devices are limited by hardware,and cannot deploy large-scale networks.So the improved network is compressed through knowledge distillation,and through migration learning,the simple network achieves higher classification performance than before.Finally,different signal modulation classification networks are used for experimental comparison.The experimental results show that the expanded dataset using the improved Triple-GAN can promote the classification accuracy of the signal.In addition,the improved network achieves higher classification accuracy,and under the guidance of the improved network,the simple convolutional network also achieves higher classification performance.更多还原