Data-Driven Modeling of a Direct Detection System With an Electro-Absorption Modulated Laser

Accurate physical layer modeling for optical fiber communication systems is one of the key aspects of implementing an intelligent optical network. In this paper, we propose and study a data-driven model enabled by digital signal processing (DSP) methods for predicting the electrical and optical behavior of an intensity modulation direct detection (IM/DD) system based on an electro-absorption modulated laser (EML). This model captures both the linear and nonlinear characteristics of an IM/DD system with an adaptive finite impulse response (FIR) filter and a look-up table (LUT). Similar to conventional supervised machine learning problems, the proposed model is measurement-informed by iterating through a PAM-M signal training dataset. Additionally, we demonstrate that the proposed model parameters can be generalized by applying 2D piecewise bilinear interpolation across different driving voltages and received optical powers. To highlight the model advantages, we benchmark the proposed model against an existing equivalent circuit model and a deep neural network (DNN) model using multiple performance metrics, including signal root mean square error (RMSE), bit error rate (BER), training time, and model complexity. We demonstrate that the proposed FIR-LUT model outperforms the conventional equivalent circuit model using the PAM-2 dataset by achieving 0.021 sample RMSE and 0.018 symbol RMSE. Similarly, a 0.020 sample RMSE and 0.015 symbol RMSE are achieved with the PAM-4 dataset. Compared with the DNN alternative, the proposed FIR-LUT model delivers comparable accuracy but significantly reduced model complexity. The FIR-LUT model achieves $34\times$ PAM-2 and $24\times$ PAM-4 training phase speed-up and $486\times$ PAM-2 and $283\times$ PAM-4 inference phase speed-up when compared with the DNN model.