Optimization of Joint Equalization of High-Speed Signals using Bayesian Machine Learning


The paper elaborates an efficient algorithm for optimization of joint Feed-Forward Equalization (FFE) and Decision Feedback Equalization (DFE) for non-return-to-zero (NRZ) and 4 level pulse amplitude modulation (PAM-4) signals using Bayesian Machine Learning approach previously introduced for NRZ by authors and expanded for PAM-4. A new optimal covariant function and hyper-parameters has been selected for the Bayesian optimization. Cost function for the Bayesian optimization is chosen based on eye height. The proposed method was compared to the conventional Least Mean Square (LMS) method and showed significant improvement. Test cases were performed for several data rates of NRZ and PAM-4 signals with crosstalk and injected jitter. Test results show that the proposed algorithm is the more effective the higher data rates are considered.

Meeting Name

2021 IEEE International Joint Electromagnetic Compatibility Signal and Power Integrity and EMC Europe Symposium, EMC/SI/PI/EMC Europe 2021 (2021: Jul. 26-Aug. 13, Raleigh, NC)


Electrical and Computer Engineering

Research Center/Lab(s)

Electromagnetic Compatibility (EMC) Laboratory


National Science Foundation, Grant IIP-1916535

Keywords and Phrases

Bayesian Optimization; Cost Function; Covariant Function; DFE; Equalization; FFE; Joint Optimization; Tap Coefficients

International Standard Book Number (ISBN)


Document Type

Article - Conference proceedings

Document Version


File Type





© 2021 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

13 Aug 2021