TDECQ-Based Optimization of Nonlinear Digital Pre-Distorters for VCSEL-MMF Optical Links Using End-to-End Learning

Abstract

We investigate in this paper the use of nonlinear digital pre-distorters (DPDs) for improving the performance of optical transmitters (TX) employing vertical-cavity surfaceemitting lasers (VCSELs), according to the standard transmitter and dispersion eye closure quaternary (TDECQ) compliance test for short-reach intra data center interconnects (DCI) using PAM4 over multi-mode fibers (MMF). We present a convolutional neural network (CNN) approach for nonlinear DPD optimization, suitable for training the pre-distorters using either a direct learning architecture (DLA) or an end-to-end (E2E) learning system. Then, we focus on a novel E2E architecture based on the reference TDECQ specifications for MMF optical links at net 100 Gbps per wavelength (λ). We experimentally implement the proposed methodology over a VCSEL-MMF setup compliant to the TDECQ test requirements. We evaluate the TDECQ performance of an optical TX employing a commercial 850 nm VCSEL at 107.2 Gbps driven at several nonlinear conditions, comparing nonlinear DPDs optimized using both DLA and TDECQ-based E2E approaches. Experimental results show that nonlinear DPD significantly enhances TDECQ performance, enabling compliance with the IEEE P802.3db TM requirements for net 100 Gbps/λ even in scenarios in which, without nonlinear DPD, the TDECQ test would fail due to VCSEL nonlinear distortions. In particular, nonlinear DPDs trained using the TDECQ-based E2E approach exhibit a consistent 0.8 dB gain in terms of TDECQ with respect to using the DLA.