论文标题
建模软易碎演化，以触发及时维修的QoT边缘及时修复
Modeling Soft-Failure Evolution for Triggering Timely Repair with Low QoT Margins
论文作者
论文摘要
在这项工作中，用编码器学习框架的能力被利用，以预测长期未来的范围。这使得在发生昂贵的硬失败之前，可以通过低质量（QOT）利润来触发及时的维修操作，最终降低了维修操作的频率和相关的运营费用。具体而言，结果表明，所提出的方案能够在预期的艰难日期前几天触发维修操作，与使用基于规则的固定QOT边缘的软失效检测方案相反，这可能会导致过早维修操作（即，在硬性失败发生前几个月，或者在艰难的事件发生前几个月，或者是在迟到的情况下都发生了。对在弹性光学网络中建立的LightPath进行了评估并比较两个框架，可以通过分析在相干接收器监测的位误差信息来建模软性磁性演变。
In this work, the capabilities of an encoder-decoder learning framework are leveraged to predict soft-failure evolution over a long future horizon. This enables the triggering of timely repair actions with low quality-of-transmission (QoT) margins before a costly hard-failure occurs, ultimately reducing the frequency of repair actions and associated operational expenses. Specifically, it is shown that the proposed scheme is capable of triggering a repair action several days prior to the expected day of a hard-failure, contrary to soft-failure detection schemes utilizing rule-based fixed QoT margins, that may lead either to premature repair actions (i.e., several months before the event of a hard-failure) or to repair actions that are taken too late (i.e., after the hard failure has occurred). Both frameworks are evaluated and compared for a lightpath established in an elastic optical network, where soft-failure evolution can be modeled by analyzing bit-error-rate information monitored at the coherent receivers.
