VCSEL-based PAM-4 transmission system emulator: A data-driven deep learning perspective

TL;DR

This paper presents a data-driven deep learning framework using Bi-LSTM networks to efficiently emulate high-speed VCSEL-based PAM-4 optical interconnects, significantly reducing computation time while maintaining high accuracy.

Contribution

It introduces a novel Bi-LSTM-based emulator that leverages experimental data and transfer learning to model optical links more efficiently than traditional methods.

Findings

Achieved normalized mean squared error below 0.04.

Reduced computation time by 20-fold with transfer learning.

Provided a rapid, accurate tool for optical link design.

Abstract

We demonstrate a data-driven framework for emulating high-speed VCSEL-based 4-level Pulse Amplitude Modulation (PAM-4) optical interconnects using bidirectional Long Short-Term Memory (Bi-LSTM) networks. Unlike conventional rate-equation models, which are computationally intensive and often require difficult parameter tuning, our approach utilizes experimental waveforms to learn the end-to-end system dynamics. By employing transfer learning and weight interpolation, we extend the model to new operating regimes with a 20-fold reduction in computation time compared to independent training, while maintaining normalized mean squared error below 0.04. This emulator provides a rapid, accurate tool for the design and optimization of short-reach optical links.