Machine-Learning-Based Optimization of Tx Equalization Parameters of a High-Speed Channel

We propose a machine-learning-based optimization approach for the Tx equalization. Our target is practical high-speed channels used in systems requiring industrial communication protocols. We adopted Random Forest as our basic machine learning algorithm and applied it to low, medium, and high loss c...

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Bibliographic Details
Published in2023 IEEE Symposium on Electromagnetic Compatibility & Signal/Power Integrity (EMC+SIPI) pp. 683 - 687
Main Authors Ling, Feng, Dan, Yufeng, Wan, Changhua, Cai, Kevin, Sen, Bidyut
Format Conference Proceeding
LanguageEnglish
Published IEEE 29.07.2023
Subjects
artificial intelligence
equalization
eye diagram
high-speed channel simulation
Industrial communication
machine learning
Machine learning algorithms
Optical signal processing
Protocols
Receivers
Robustness
Search methods
signal integrity
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Summary:We propose a machine-learning-based optimization approach for the Tx equalization. Our target is practical high-speed channels used in systems requiring industrial communication protocols. We adopted Random Forest as our basic machine learning algorithm and applied it to low, medium, and high loss channels with 3-tap and 5tap Tx equalization architectures. Our machine-learning-based optimization results showed the outstanding efficiency and accuracy of the approach.
DOI:10.1109/EMCSIPI50001.2023.10241426