.png){kind=icon}
返回
MM-Net: A Multi-Modal Approach Toward Automatic Modulation Classification
.png){kind=icon}
PRE
AI
DOI：10.1109/LCOMM.2023.3342604.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Automatic Modulation Classification (AMC) has become an important component in communication systems for both civil and defense applications. The shortcomings of traditional approaches to AMC have led researchers to develop complex machine learning (ML)-based approaches. In this work, inspired by multi-modal approaches for general Computer Vision tasks like Semantic Segmentation, we propose MM-Net, a multimodal approach to AMC that uses domain-specific features in the form of Higher Order Cumulants (HOCs) to improve classification performance. Furthermore, we explore the usage of HOCs in existing Deep Learning (DL)-based applications for AMC. Simulation results show that for eight modulation classification, MM-Net achieves high classification accuracy even at low SNRs, demonstrating the robustness of the multimodal approach even under challenging channel conditions, while existing methods are improved by utilizing HOCs, especially at low SNR values.
Keyword:
Feature extraction
Modulation
Task analysis
Signal to noise ratio
Convolutional neural networks
Computer architecture
Robustness
Automatic modulation classification
machine learning
deep learning
cumulants
convolutional neural networks
transfer learning
期刊
IF:
4.4
论文数:
1.3W
被引数:
2.2W
机构
引用论文
MCNet: An Efficient CNN Architecture for Robust Automatic Modulation ClassificationMCNet: 一种用于鲁棒自动调制分类的高效CNN架构
PREAI
分享收藏Structural Systematics of Rare Earth Complexes. XVI (‘Maximally’) Hydrated Rare Earth(III) Bromides
PREAI
分享收藏Automatic Modulation Classification Using Compressive Convolutional Neural Network基于压缩卷积神经网络的自动调制分类.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAI分享收藏Identifying modulation formats through 2D Stokes planes with deep neural networksOPTICS EXPRESSOAAI
OPTICS EXPRESSIF3.3
OAAI分享收藏
查看更多内容