Decision Feedback Equalizer (DFE) Taps Estimation with Machine Learning Methods

Bobi Shi, Yixuan Zhao, Hanzhi Ma, Thong Nguyen, Er Ping Li, Andreas C. Cangellaris, Jose Schutt-Aine

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this work, a direct surrogate model from channel geometry to decision feedback equalization taps is constructed by four different machine learning methods, namely Polynomial Regression, Feed-forward Neural Network, Support Vector Regression, and Polynomial Chaos. They will be used to replace computational heavy simulations done by electromagnetic solver and channel simulation. Overall, all methods can offer 1% relative error rate of the prediction in this numerical example.

Original languageEnglish (US)
Title of host publication2021 IEEE Electrical Design of Advanced Packaging and Systems, EDAPS 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665466134
DOIs
StatePublished - 2021
Event2021 IEEE Electrical Design of Advanced Packaging and Systems, EDAPS 2021 - Urbana, United States
Duration: Dec 13 2021Dec 15 2021

Publication series

NameIEEE Electrical Design of Advanced Packaging and Systems Symposium
Volume2021-December
ISSN (Print)2151-1225
ISSN (Electronic)2151-1233

Conference

Conference2021 IEEE Electrical Design of Advanced Packaging and Systems, EDAPS 2021
Country/TerritoryUnited States
CityUrbana
Period12/13/2112/15/21

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Automotive Engineering
  • Computer Science(all)

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