End-to-end learning has become a popular method for joint transmitter and receiver optimization in optical communication systems. Such approach may require a differentiable channel model, thus hindering the optimization of links based on directly modulated lasers (DMLs). This is due to the DML behavior in the large-signal regime, for which no analytical solution is available. In this paper, this problem is addressed by developing and comparing differentiable machine learning-based surrogate models. The models are quantitatively assessed in terms of root mean square error and training/testing time. Once the models are trained, the surrogates are then tested in a numerical equalization setup, resembling a practical end-to-end scenario. Based on the numerical investigation conducted, the convolutional attention transformer is shown to outperform the other models considered.

翻译：端到端学习已成为光通信系统中联合发射器与接收器优化的常用方法。然而，此类方法通常需要可微分的信道模型，从而阻碍了基于直接调制激光器（DML）的链路优化。其难点在于DML在大信号工况下的行为特性无法通过解析解表达。本文通过开发并比较基于可微机器学习代理模型来解决这一问题。我们从均方根误差与训练/测试时间两个维度对模型进行定量评估。模型训练完成后，在模拟实际端到端场景的数字均衡设置中对代理模型进行测试。基于所开展的数值研究，卷积注意力Transformer的性能优于其他被考察模型。