Machine learning classifier based on FE-KNN enabled high-capacity PAM-4 and NRZ transmission with 10-G class optics

Abstract

Modified by special feature engineering, a powerful and low-order equalizer based on K-nearest neighbors (KNN) classifier is applied to improve performance of high-speed system with bandwidth-limited optics. The feature construction and feature weighting are specially designed to conduct an appropriate a feature engineering-based KNN (FE-KNN) scheme, which contains more data characteristics to enhance the equalization performance. Experimental comparisons of KNN classifier with/without feature engineering, decision feedback equalizer (DFE) and feed-forward equalizer (FFE) are implemented to prove the feasibility of our scheme in both 25-Gb/s NRZ and 50-Gb/s PAM-4 transmission experiments with 10-G optics system. The corresponding results show that, without the feature engineering, the performance achieved by the common KNN is not improved even in the case of hard decision (HD). In contrast, compared to the common 11-taps DFE, the performance achieved by FE-KNN with only 5 taps is improved by 1-dB at KP4-FEC threshold (BER=2.2E-4) for 25-Gb/s NRZ transmission. While, for 50-Gb/s PAM-4 case, 0.5-dB sensitivity improvement is achieved by our scheme compared to the common 11-taps DFE under the HD-FEC limit (BER=3.8E-3).