Name
Abstract | ||
|---|---|---|
To achieve next-generation high-speed data transmission standards, e.g., IEEE 802.3bs and PCIe6.0, four-level pulse amplitude modulation (PAM-4) data formats are adopted. Although PAM-4 signaling is spectrally efficient in mitigating the inter-symbol interference (ISI) caused by the bandwidth-limited wire channels, PAM-4 is more sensitive compared with conventional non-return-to-zero (NRZ) binary signaling. In this paper, to evaluate the received signal quality for adaptive coefficients setting of an equalizer for PAM-4 data transmission, a novel eye-opening monitoring (EOM) technique based on machine learning is proposed. The monitoring technique uses a Gaussian mixture model (GMM) to classify the received PAM-4 symbols. Moreover, simulation and experimental results of the coefficients adjustment using the method are presented. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ISMVL49045.2020.00-14 | 2020 IEEE 50th International Symposium on Multiple-Valued Logic (ISMVL) |
Keywords | DocType | ISSN |
Multi-valued signaling,PAM-4,Eye-opening monitoring,Machine learning,Gaussian mixture model | Conference | 0195-623X |
ISBN | Citations | PageRank |
978-1-7281-5407-7 | 1 | 0.37 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yosuke Iijima | 1 | 19 | 4.61 |
| Keigo Taya | 2 | 1 | 0.37 |
| Yasushi Yuminaka | 3 | 53 | 14.45 |