Automatic modulation classification with <scp>2D</scp> transforms and convolutional neural network-Reference-Cited by-同舟云学术

Automatic modulation classification with 2D transforms and convolutional neural network

Published:2022-08-22 Issue:12 Volume:33 Page:
ISSN:2161-3915
Container-title:Transactions on Emerging Telecommunications Technologies
language:en
Short-container-title:Trans Emerging Tel Tech

Author:

Ghanem Hanan S.¹,Shoaib Mohamed R.²,El‐Gazar Safaa¹^ORCID,Emara Heba²,El‐Shafai Walid²³^ORCID,El‐Moneim Samia A.²⁴,El‐Fishawy Adel S.²,Taha Taha E.²,Hamed Hesham F.A.¹⁵,El‐Banby Ghada M.⁶,Elsabrouty Maha⁷,El‐Rabaie El‐Sayed M.²,Abd El‐Samie Fathi E.²,Salama Gerges M.¹

Affiliation:

1. Department of Electrical Engineering, Faculty of Engineering Minia University Minia Egypt

2. Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering Menoufia University Menouf Egypt

3. Security Engineering Lab, Computer Science Department Prince Sultan University Riyadh Saudi Arabia

4. High Institute of Engineering and Technology Tanta Egypt

5. Department of Electrical Engineering, Faculty of Engineering Egyptian‐Russian University Cairo Egypt

6. Department of Industrial Electronics and Control Engineering, Faculty of Electronic Engineering Menoufia University Menouf Egypt

7. Department of Electronics and Communication Egypt‐Japan University of Science and Technology Alexandria Egypt

Publisher

Wiley

Subject

Electrical and Electronic Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/ett.4623

Reference24 articles.

1. An efficient modulation classification method using signal constellation diagrams with convolutional neural networks, Gabor filtering, and thresholding

2. A hybrid attention mechanism for blind automatic modulation classification

3. Automatic Modulation Classification: A Deep Architecture Survey

4. Real-time Modulation Classification Based On Maximum Likelihood

5. Automatic modulation classification algorithm using higher-order cumulants under real-world channel conditions

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