计算机科学
卷积神经网络
电子战
图形
调制(音乐)
信号(编程语言)
频率调制
信号处理
语音识别
人工智能
计算机工程
机器学习
无线电频率
实时计算
数据挖掘
理论计算机科学
电信
美学
哲学
程序设计语言
雷达
作者
Krasimir Tonchev,Nikolay Neshov,Antoni Ivanov,Agata Manolova,Vladimir Poulkov
标识
DOI:10.1109/wpmc55625.2022.10014833
摘要
Recognition of the radio signal's modulating scheme is becoming increasingly important in civil and military applications. It can potentially alleviate the electromagnetic signal congestion in $5\mathrm{G}$ networks by utilization of dynamic spectrum access or perform friend/foe identification in electronic military warfare as well as to support the detection of cyber-security related attacks. The recent advances in graph-convolutional networks (GCN) reveal a potential for usage in applications such as automatic modulation classification (AMC). Considering the structure of the modulated signal in time and frequency, this work proposes GCN architecture for AMC in various signal to noise (SNR) levels. The experimental results reveal that such approach delivers comparable results to other approaches published in the literature.
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