This paper proposes an evolutionary transfer learning approach (Evol-TL) for scalable quality-of-transmission (QoT) estimation in multi-domain elastic optical networks (MD-EONs). Evol-TL exploits a broker-based MD-EON architecture that enables cooperative learning between the broker plane (end-to-end) and domain-level (local) machine learning functions while securing the autonomy of each domain. We designed a genetic algorithm to optimize the neural network architectures and the sets of weights to be transferred between the source and destination tasks. We evaluated the performance of Evol-TL with three case studies considering the QoT estimation task for lightpaths with (i) different path lengths (in terms of the numbers of fiber links traversed), (ii) different modulation formats, and (iii) different device conditions (emulated by introducing different levels of wavelength-specific attenuation to the amplifiers). The results show that the proposed approach can reduce the average amount of required training data by up to $13 \times$13× while achieving an estimation accuracy above 95%.

中文翻译：

---

多域光网络中端到端QoT估计的进化转移学习性能研究[已邀请]

本文提出了一种演化转移学习方法（Evol-TL），用于多域弹性光网络（MD-EON）中的可伸缩传输质量（QoT）估计。Evol-TL利用基于代理的MD-EON体系结构，该体系结构允许在代理平面（端到端）和域级别（本地）机器学习功能之间进行协作学习，同时确保每个域的自治性。我们设计了一种遗传算法来优化神经网络体系结构以及在源任务和目标任务之间传递的权重集。我们通过三个案例研究评估了Evol-TL的性能，其中三个案例研究考虑了（i）不同路径长度（根据所经过的光纤链路数量），（ii）不同调制格式，（iii）不同的设备条件（通过对放大器引入不同级别的特定波长衰减来模拟）。结果表明，所提出的方法可以将所需训练数据的平均数量减少多达13美元x 13倍，同时达到95％以上的估计精度。