Low Complexity Neural Network Equalization Based on Multi-Symbol Output Technique for 200+ Gbps IM/DD Short Reach Optical System
Author:
Affiliation:
1. Key Laboratory for Information Science of Electromagnetic Waves and State Key Laboratory of ASIC and System, Fudan University, Shanghai, China
Funder
National Key R&D Program of China
National Natural Science Foundation of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Atomic and Molecular Physics, and Optics
Link
http://xplorestaging.ieee.org/ielx7/50/9763567/09697352.pdf?arnumber=9697352
Reference21 articles.
1. 135-GHz D-Band 60-Gbps PAM-8 Wireless Transmission Employing a Joint DNN Equalizer With BP and CMMA
2. Neural Turbo Equalization: Deep Learning for Fiber-Optic Nonlinearity Compensation
3. Mitigation of SOA-Induced Nonlinearity With the Aid of Deep Learning Neural Networks
4. 56 Gbps IM/DD PON based on 10G-Class Optical Devices with 29 dB Loss Budget Enabled by Machine Learning
5. Feedforward and Recurrent Neural Network-Based Transfer Learning for Nonlinear Equalization in Short-Reach Optical Links
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2. Photonics-aided D-band 64-QAM MMW transmission utilizing modified multi-symbol output neural network equalization;Optical Fiber Technology;2024-10
3. Low-Complexity Samples Versus Symbols-Based Neural Network Receiver for Channel Equalization;Journal of Lightwave Technology;2024-08-01
4. Advanced Neural Network-Based Equalization in Intensity-Modulated Direct-Detection Optical Systems: Current Status and Future Trends;Photonics;2024-07-28
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