On classifiers for blind feature‐based automatic modulation classification over multiple‐input–multiple‐output channels-Reference-Cited by-同舟云学术

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On classifiers for blind feature‐based automatic modulation classification over multiple‐input–multiple‐output channels

Published:2016-05 Issue:7 Volume:10 Page:790-795
ISSN:1751-8636
Container-title:IET Communications
language:en
Short-container-title:IET Communications

Author:

Kharbech Sofiane¹,Dayoub Iyad²,Zwingelstein‐Colin Marie²,Simon Eric Pierre³

Affiliation:

1. Department of Communication and Information Technology (STIC)Higher Institute of Technological Studies of GabesGabes6011Tunisia

2. DOAE Laboratory – UMR CNRS 8520IEMNUniversity of Valenciennes and Hainaut‐CambresisValenciennes59313France

3. TELICE Laboratory – UMR CNRS 8520IEMNUniversity of Lille 1Lille59100France

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering,Computer Science Applications

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/iet-com.2015.1124

Reference21 articles.

1. Hierarchical digital modulation classification using cumulants

2. A novel robust feature of modulation classification for reconfigurable software radio

3. Modulation Recognition for MIMO Relaying Broadcast Channels with Direct Link

4. Statistical Pattern Recognition

5. Blind Digital Modulation Identification for Spatially-Correlated MIMO Systems

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1. Automatic Modulation Classification in Impulsive Noise: Log-Domain 3-D Constellation Diagrams and Multiscale Dual-Convolutional 3DCNN;IEEE Transactions on Cognitive Communications and Networking;2024-08

2. Transfer Learning for Automatic Modulation Recognition Using a Few Modulated Signal Samples;IEEE Transactions on Vehicular Technology;2023-09

3. Semi-supervised-based automatic modulation classification with domain adaptation for wireless IoT spectrum monitoring;Frontiers in Physics;2023-03-31

4. Digital Modulation Classification Based on Automatic K-Nearest Neighbors Classifier;Communications in Computer and Information Science;2023

5. Improving Modulation Recognition Using Time Series Data Augmentation via a Spatiotemporal Multi-Channel Framework;Electronics;2022-12-26

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