Low-Complexity Samples Versus Symbols-Based Neural Network Receiver for Channel Equalization
Author:
Affiliation:
1. DTU Electro, Technical University of Denmark, Kongens Lyngby, Denmark
2. Nokia Bell Labs, Stuttgart, Germany
Funder
Villum Fonden's YIP OPTIC-AI
ERC CoG FRECOM
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/50/10604663/10502373.pdf?arnumber=10502373
Reference26 articles.
1. Machine learning for short reach optical fiber systems
2. Advanced optical access technologies for next-generation (5G) mobile networks [Invited]
3. Performance and Complexity Analysis of Conventional and Deep Learning Equalizers for the High-Speed IMDD PON
4. Artificial neural networks for linear and non-linear impairment mitigation in high-baudrate IM/DD systems;Estaran,2016
5. Low-Complexity Multi-Task Learning Aided Neural Networks for Equalization in Short-Reach Optical Interconnects
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1. Multi-Symbol Reservoir Computing-Based Equalization for PAM-4 IM/DD Transmission;IEEE Photonics Technology Letters;2024-07-01
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