TY - GEN
T1 - Decision Feedback Equalizer (DFE) Taps Estimation with Machine Learning Methods
AU - Shi, Bobi
AU - Zhao, Yixuan
AU - Ma, Hanzhi
AU - Nguyen, Thong
AU - Li, Er Ping
AU - Cangellaris, Andreas C.
AU - Schutt-Aine, Jose
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85124125484&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85124125484&partnerID=8YFLogxK
U2 - 10.1109/EDAPS53774.2021.9656986
DO - 10.1109/EDAPS53774.2021.9656986
M3 - Conference contribution
AN - SCOPUS:85124125484
T3 - IEEE Electrical Design of Advanced Packaging and Systems Symposium
BT - 2021 IEEE Electrical Design of Advanced Packaging and Systems, EDAPS 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE Electrical Design of Advanced Packaging and Systems, EDAPS 2021
Y2 - 13 December 2021 through 15 December 2021
ER -