Modulation Format Identification Based on Channel-spatial Attention Modules and Deep Learning-Reference-Cited by-同舟云学术

Modulation Format Identification Based on Channel-spatial Attention Modules and Deep Learning

Published:2023-10-11 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 5th International Conference on Civil Aviation Safety and Information Technology (ICCASIT)
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Author:

Chen Qian¹,Zhang Qi²,Xin Xiangjun³,Cui Yi¹,Wang Fu²,Tian Feng²,Tian Qinghua²,Wang Yongjun²,Yang Leijing²

Affiliation:

1. Beijing University of Posts and Telecommunications,School of Electronic Engineering Beijing Key Laboratory of Space Ground Interconnection and Convergence,Beijing,China

2. Beijing University of Posts and Telecommunications,Beijing Key Laboratory of Space Ground Interconnection and Convergence State Key Laboratory of Information Photonics and Optical Communications,Beijing,China

3. Beijing Institute of Technology,School of Information and Electronics,Beijing,China

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10351493/10351494/10351587.pdf?arnumber=10351587

Reference7 articles.

1. Joint Multi-Task Offloading and Resource Allocation for Mobile Edge Computing Systems in Satellite IoT

2. 400 Gbit/s 4 mode transmission for IM/DD OAM mode division multiplexing optical fiber communication with a few-shot learning-based AffinityNet nonlinear equalizer

3. Data-defined naïve Bayes (DNB) based decision scheme for the nonlinear mitigation for OAM mode division multiplexed optical fiber communication

4. Modulation Format Identification Based on Signal Constellation Diagrams and Support Vector Machine

5. CutMix: Regularization Strategy to Train Strong Classifiers With Localizable Features