.png){kind=icon}
返回
Deep Learning-Based Automatic Modulation Classification Using Robust CNN Architecture for Cognitive Radio Networks
.png){kind=icon}
OA
AI
DOI：10.3390/s23239467.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Automatic modulation classification (AMC) is an essential technique in intelligent receivers of non-cooperative communication systems such as cognitive radio networks and military applications. This article proposes a robust automatic modulation classification model based on a new architecture of a convolutional neural network (CNN). The basic building convolutional blocks of the proposed model include asymmetric kernels organized in parallel combinations to extract more meaningful and powerful features from the raw I/Q sequences of the received signals. These blocks are connected via skip connection to avoid vanishing gradient problems. The experimental results reveal that the proposed model performs well in classifying nine different modulation schemes simulated with different real wireless channel impairments, including AWGN, Rician multipath fading, and clock offset. The performance of the proposed system systems shows that it outperforms its best rivals from the literature in recognizing the modulation type. The proposed CNN architecture remarkably improves classification accuracy at low SNRs, which is appropriate in realistic scenarios. It achieves 86.1% accuracy at -2 dB SNR. Furthermore, it reaches an accuracy of 96.5% at 0 dB SNR and 99.8% at 10 dB SNR. The proposed architecture has strong feature extraction abilities that can effectively recognize 16QAM and 64QAM signals, the challenging modulation schemes of the same modulation family, with an overall average accuracy of 81.02%.
Keyword:
automatic modulation classification
raw IQ sequences
deep learning
convolutional neural network
and wireless channel impairments
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:
3.5
论文数:
7.2W
被引数:
20.9W
机构
引用论文
Synthesis, crystal structure, magnetic and electrochemical studies of two copper complexes with carboxylate rich dinucleating ligand富羧酸根双核配体的两种铜配合物的合成、晶体结构、磁性和电化学研究
PREAI
分享收藏Automatic Modulation Classification Using Hybrid Data Augmentation and Lightweight Neural Network基于混合数据增强和轻量级神经网络的自动调制分类.png)
SENSORSOAAI
SENSORSIF3.5
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Structural study of lanthanides(III) in aqueous nitrate and chloride solutions by EXAFS通过EXAFS对硝酸盐和氯化物水溶液中镧系元素 (III) 的结构研究
PREAI
分享收藏Deep-Learning-Based Classification of Digitally Modulated Signals Using Capsule Networks and Cyclic Cumulants使用胶囊网络和循环累积量对数字调制信号进行基于深度学习的分类SENSORSOAAI
SENSORSIF3.5
OAAI分享收藏Lightweight Deep Learning Model for Automatic Modulation Classification in Cognitive Radio Networks认知无线电网络中用于自动调制分类的轻量级深度学习模型IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏
查看更多内容