Denoising Convolutional Neural Network for Wideband Frequency Modulation Signals Based on Microwave Photonic Down-Conversion-Reference-Cited by-同舟云学术

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Denoising Convolutional Neural Network for Wideband Frequency Modulation Signals Based on Microwave Photonic Down-Conversion

Published:2023-11-04 Issue: Volume: Page:1-5
ISSN:
Container-title:2023 Asia Communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings (ACP/POEM)
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Author:

Chen Shilin¹,Pu Tao¹,Wang Li²,Zheng Jilin¹,Wu Gengze¹,Li Jin¹,Zhang Xin³,Zhao Jiaqi¹

Affiliation:

1. College of Communications Engineering, Army Engineering University of PLA,Nanjing,China

2. Nanjing University of Science and Technology, ZiJin College,Computer Department,Nanjing,China

3. College of Information and Communication, National University of Defense Technology,Wuhan,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10368381/10368607/10369928.pdf?arnumber=10369928

Reference16 articles.

1. Deep-learning-powered photonic analog-to-digital conversion

2. Linearized wideband and multi-carrier link based on TL-ANN

3. Deep neural network-assisted high-accuracy microwave instantaneous frequency measurement with a photonic scanning receiver

4. Optimization of the Brillouin instantaneous frequency measurement using convolutional neural networks

5. Modified deep-learning-powered photonic analog-to-digital converter for wideband complicated signal receiving

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