Efficient Convolutional Networks for Robust Automatic Modulation Classification in OFDM-Based Wireless Systems-Reference-Cited by-同舟云学术

Efficient Convolutional Networks for Robust Automatic Modulation Classification in OFDM-Based Wireless Systems

Published:2023-03 Issue:1 Volume:17 Page:964-975
ISSN:1932-8184
Container-title:IEEE Systems Journal
language:
Short-container-title:IEEE Systems Journal

Author:

Huynh-The Thien¹^ORCID,Nguyen Toan-Van²^ORCID,Pham Quoc-Viet³^ORCID,da Costa Daniel Benevides⁴^ORCID,Kwon Gi-Hyeob¹^ORCID,Kim Dong-Seong¹^ORCID

Affiliation:

1. ICT Convergence Research Center and the Department of IT Convergence, Kumoh National Institute of Technology, Gumi, South Korea

2. Department of Electrical and Computer Engineering, Utah State University, Logan, UT, USA

3. Korean Southeast Center for the 4th Industrial Revolution Leader Education, Pusan National University, Yangsan, South Korea

4. Technology Innovation Institute, Abu Dhabi, United Arab Emirates

Funder

National Research Foundation of Korea

Ministry of Education, Science and Technology

NRF

Ministry of Education

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Control and Systems Engineering,Computer Networks and Communications,Computer Science Applications,Information Systems

Link

http://xplorestaging.ieee.org/ielx7/4267003/10051137/09908294.pdf?arnumber=9908294

Reference53 articles.

1. A Faster Maximum-Likelihood Modulation Classification in Flat Fading Non-Gaussian Channels

2. Continuous Phase Modulation Classification via Baum-Welch Algorithm

3. Blind Modulation Classification over Fading Channels Using Expectation-Maximization

4. Automatic Modulation Classification Using Interacting Multiple Model Kalman Filter for Channel Estimation

5. DeepEdgeBench: Benchmarking Deep Neural Networks on Edge Devices

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