Machine learning (ML) provides effective means to learn from spectrum data and solve
complex tasks involved in wireless communications. Supported by recent advances in …

Deep learning-based automatic modulation classification (AMC) models are susceptible to
adversarial attacks. Such attacks inject specifically crafted wireless interference into …

With the evolutionary development of modern communications technology, automatic
modulation classification (AMC) has played an increasing role in the complex wireless …

Automatic modulation recognition (AMR) of radio signal is an important research topic in the
area of non-cooperative communication and cognitive radio. Recently deep learning (DL) …

Automatic modulation classification (AMC) is a crucial stage in the spectrum management,
signal monitoring, and control of wireless communication systems. The accurate …

Currently, deep learning methods with stacking small size convolutional filters are widely
used for automatic modulation classification (AMC). In this report, we find some experienced …

Artificial intelligence (AI), and specifically machine and deep learning, are emerging as
essential enabling techniques for the design of future generations of wireless networks for …

对调制识别的对抗样本攻击这一研究热点进行了综述, 首先给出调制识别中对抗样本的的相关
概述和专业术语, 将对抗样本攻击和防御方法的相关研究成果进行梳理回顾 …

利用深度学习解决无线通信识别问题的研究越来越多. 为了建立安全可靠的深度神经网络模型,
有必要研究其对抗攻击技术. 在介绍对抗样本概念和攻击算法的基础上, 提出了无线通信智能 …

Automatic modulation recognition (AMR) is a vital stage in spectrum regulation and
information security of wire-less communication systems. To explore implicit characteristics …