An efficient modulation classification method using signal constellation diagrams with convolutional neural networks, Gabor filtering, and thresholding-Reference-Cited by-同舟云学术

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

Published:2022-03-03 Issue:5 Volume:33 Page:
ISSN:2161-3915
Container-title:Transactions on Emerging Telecommunications Technologies
language:en
Short-container-title:Trans Emerging Tel Tech

Author:

Abdel‐Moneim Mohamed A.¹^ORCID,Al‐Makhlasawy Rasha M.²,Abdel‐Salam Bauomy Nariman³^ORCID,El‐Rabaie El‐Sayed M.⁴,El‐Shafai Walid⁴⁵^ORCID,Farghal Ahmed E. A.⁶^ORCID,Abd El‐Samie Fathi E.⁴

Affiliation:

1. Department of Telecommunication, Faculty of Engineering Egyptian Russian University Cairo Egypt

2. Department of Computers and Systems Electronics Research Institute El Nozha ElGededa Egypt

3. Department of Electronics and Communications Engineering, Faculty of Engineering Canadian International College (CIC) Cairo Governorate Egypt

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

5. Security Engineering Laboratory, Department of Computer Science Prince Sultan University Riyadh Saudi Arabia

6. Department of Electrical Engineering, Faculty of Engineering Sohag University Sohag Egypt

Publisher

Wiley

Subject

Electrical and Electronic Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/ett.4459

Reference53 articles.

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2. SINR‐based adaptive MIMO FBMC/OQAM transmission with automatic modulation classification

3. Modulation recognition for radar emitter signals based on convolutional neural network and fusion features

4. A Comprehensive Survey on Spectrum Sensing in Cognitive Radio Networks: Recent Advances, New Challenges, and Future Research Directions

5. A Comprehensive Survey of “Metamaterial Transmission-Line Based Antennas: Design, Challenges, and Applications”

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