Feature Fusion Convolution-Aided Transformer for Automatic Modulation Recognition-Reference-Cited by-同舟云学术

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Feature Fusion Convolution-Aided Transformer for Automatic Modulation Recognition

Published:2023-10 Issue:10 Volume:27 Page:2643-2647
ISSN:1089-7798
Container-title:IEEE Communications Letters
language:
Short-container-title:IEEE Commun. Lett.

Author:

Hu Mutian¹^ORCID,Ma Jitong²^ORCID,Yang Zhengyan³,Wang Jie²^ORCID,Lu Jingjing¹^ORCID,Wu Zhanjun⁴

Affiliation:

1. School of Automation, Guangxi University of Science and Technology, Liuzhou, China

2. College of Information Science and Technology, Dalian Maritime University, Dalian, China

3. College of Transportation Engineering, Dalian Maritime University, Dalian, China

4. State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian, China

Funder

National Natural Science Foundation of China

China Postdoctoral Science Foundation

Fundamental Research Funds for the Central Universities

Doctoral Fund Project of Guangxi University of Science and Technology

Specific Research Project of Guangxi for Research Bases and Talents

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Modeling and Simulation

Link

http://xplorestaging.ieee.org/ielx7/4234/10275739/10195842.pdf?arnumber=10195842

Reference20 articles.

1. Automatic Modulation Classification Using CNN-LSTM Based Dual-Stream Structure

2. A Spatiotemporal Multi-Channel Learning Framework for Automatic Modulation Recognition

3. A Transformer-based CTDNN Structure for Automatic Modulation Recognition

4. MCformer: A Transformer Based Deep Neural Network for Automatic Modulation Classification

5. Muscle Fatigue Analysis by Visualization of Dynamic Surface EMG Signals Using Markov Transition Field

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Effects of diagram plane on neural network based modulation recognition;Applied Soft Computing;2024-03

2. ShuffleFormer: An efficient shuffle meta framework for automatic modulation classification;Physical Communication;2023-12

3. Multi-Component Feature Extraction for Few-Sample Automatic Modulation Classification;IEEE Communications Letters;2023-11

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