.png){kind=icon}
返回
A complex-valued convolutional fusion-type multi-stream spatiotemporal network for automatic modulation classification
.png){kind=icon}
OA
AI
DOI：10.1038/s41598-024-73547-w.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Automatic Modulation Classification (AMC) is crucial in non-cooperative communication systems as it facilitates the identification of interference signals with minimal prior knowledge. Although there have been significant advancements in Deep Learning (DL) within the field of AMC, leveraging the inherent relationships between In-phase (I) and Quadrature-phase (Q) components, and enhance recognition accuracy under low signal-to-noise ratio (SNR) conditions remains a challenge. This study introduces a complex-valued convolutional fusion-type multi-stream spatiotemporal network (CC-MSNet) for AMC, which combines spatial and temporal feature extraction modules for modulation recognition. Experimental results demonstrate that the CC-MSNet performs well on three benchmark datasets, RML2016.10a, RML2016.10b, and RML2016.04c, with average recognition accuracy of 62.86%, 65.08%, and 71.12%. It also performs excellently in low SNR environments below 0dB, significantly outperforming other networks.
Keyword:
RECOGNITION
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.9
论文数:
27.8W
被引数:
83.5W
机构
引用论文
Time and phase features network model for automatic modulation classification用于自动调制分类的时间和相位特征网络模型
PREAI
分享收藏Surfactant-coated Tea Waste: Preparation, Characterization and its Application for Methylene Blue Adsorption from Aqueous Solution表面活性剂涂覆茶废料：制备、表征及其在模拟水溶液中吸附亚甲基蓝的应用OAAI
OAAI分享收藏Deep Learning Models for Wireless Signal Classification With Distributed Low-Cost Spectrum Sensors基于分布式低成本频谱传感器的无线信号分类深度学习模型OAAI
OAAI分享收藏Automatic Modulation Classification Using Deep Residual Neural Network with Masked Modeling for Wireless Communications基于掩蔽建模的深度残差神经网络在无线通信中的自动调制分类.png)
DRONES
OAAI
DRONESIF4.8
OAAI分享收藏
查看更多内容