Machine Learning Adaptive Receiver for PAM-4 Modulated Optical Interconnection Based on Silicon Microring Modulator

Abstract

Modulation nonlinearity can severely distort multilevel modulation, and signal processing to mitigate the distortion is highly desirable. In this paper, we demonstrated a machine learning method for adaptive detection of 4-level pulse amplitude modulation (PAM-4) signals modulated by silicon micro-ring modulator (Si-MRM). The very limited linear modulation range of Si-MRM leads to serious modulation nonlinearity distortion for high-level modulations like PAM-4 with the consideration of wavelength drift. Our approach is based on the support vector machine (SVM) method, which can learn the nonlinear distortion of Si-MRM during PAM-4 modulation. Thus, the detection can be made adaptive for PAM-4 signals with nonlinear levels and level dependent noise. The modulation nonlinearity distortion of PAM-4 has been characterized in terms of level deviation (LD) with respect to wavelength drift. Up to 2.7-dB receiver sensitivity gain is obtained at about 26% LD by using the proposed SVM machine learning method. The receiver sensitivity-float range can be squeezed to be within 0.3 dB even with up to 30% LD, which indicates a stable detection of PAM-4 signals along with wavelength drift. Up to 3.63-dB receiver sensitivity improvement has been experimentally achieved at 50 Gbps for PAM-4 signals modulated by a Si-MRM and after 2-km standard single mode fiber transmission. The stable operation of Si-MRM is very difficult and very important. The proof-of-concept results indicate the very promising capability of machine learning method for stable detection of PAM-4 signals modulated by Si-MRM, which is of great significance for practical application of Si-MRM in optical interconnection.