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

被引：1
|
作者：
Dikhaminjia, N. [1 ]
Tsintsadze, G. [1 ]
Kiguradze, Z. [2 ]
He, J. [2 ]
Tsiklauri, M. [2 ]
Drewniak, J. [2 ]
Chada, A. [3 ]
Mutnury, B. [3 ]
机构：
[1] Ilia State Univ, GE-0162 Tbilisi, Georgia
[2] Missouri Univ Sci & Technol, Electromagnet Compatibil Lab, Rolla, MO 65409 USA
[3] Dell, Enterprise Prod Grp, One Dell Way, Round Rock, TX 78682 USA
来源：
2021 JOINT IEEE INTERNATIONAL SYMPOSIUM ON ELECTROMAGNETIC COMPATIBILITY, SIGNAL & POWER INTEGRITY, AND EMC EUROPE (EMC+SIPI AND EMC EUROPE) | 2021年
基金：
美国国家科学基金会;
关键词：
Equalization; DFE; FFE; Tap coefficients; Joint optimization; Bayesian Optimization; Covariant Function; Cost Function;
D O I：
10.1109/EMC/SI/PI/EMCEurope52599.2021.9559157
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
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.
引用
收藏
页码：48 / 52
页数：5
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