Having ubiquitous optical monitors in dense wavelength-division multiplexing (DWDM) or flex-grid networks allows the estimation in real time of crucial parameters. Such monitoring would be even more important in disaggregated optical networks, to inspect performance issues related to inter-vendor interoperability. Several important parameters can be retrieved using optical spectrum analyzers (OSAs). However, omnipresent OSAs represent an infeasible solution. Nevertheless, the advent of new, relatively cheap, compact and medium-resolution optical channel monitors (OCMs) enable a more intensive deployment of these devices. In this paper, we identify two main scenarios for the placement of such monitors: at the ingress and at the egress of the optical nodes. In the ingress scenario, we can directly estimate the parameters related to the signals, but not those related to the filters. On the contrary, in the egress scenario, the filter-related parameters can be easily detected, but not those related to amplified spontaneous emission. Therefore, we present two methods that, leveraging a curve fitting and a machine learning regression algorithm, allow detection of the missing parameters. We verify the proposed solutions with spectral data acquired in simulation and experimental setups. We obtained good estimation accuracy for both setups and for both studied placement scenarios. It is noteworthy that in the experimental assessment of the ingress scenario, we achieved a maximum absolute error (MAE) lower than 1 GHz in filter bandwidth estimation and a MAE lower than 0.5 GHz in filter frequency shift estimation. In addition, by comparing the relative errors of the considered parameters, we identified the ingress scenario as the more beneficial. In particular, we estimated the filter central frequency shift with 84% and the filter 6 dB bandwidth with 75% higher accuracy, with respect to datasheet/reference values. This translates into a total reduction of the estimated signal-to-noise ratio (SNR) penalty, introduced by a single optical filter, of 0.24 dB.

中文翻译：

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光谱处理技术可有效监测光网络

在密集波分复用（DWDM）或柔性网格网络中拥有无处不在的光学监控器，可以实时估算关键参数。对于检查与供应商之间的互操作性有关的性能问题，这种监控在分解的光网络中将更加重要。可以使用光谱分析仪（OSA）检索几个重要参数。但是，无所不在的OSA代表了不可行的解决方案。尽管如此，新的，相对便宜的，紧凑型和中分辨率的光通道监视器（OCM）的出现使这些设备的部署更加密集。在本文中，我们确定了放置此类监视器的两种主要情况：在光节点的入口和出口。在进入场景中，我们可以直接估计与信号相关的参数，但与过滤器无关的那些。相反，在出口场景中，可以轻松检测到与过滤器相关的参数，但不能检测到与放大的自发发射相关的参数。因此，我们提出了两种方法，利用曲线拟合和机器学习回归算法，可以检测缺失的参数。我们用在仿真和实验设置中获得的光谱数据验证了提出的解决方案。对于这两种设置以及所研究的两种放置方案，我们都获得了良好的估计精度。值得注意的是，在进入场景的实验评估中，我们在滤波器带宽估计中实现了低于1 GHz的最大绝对误差（MAE），在滤波器频移估计中实现了低于0.5 GHz的MAE。另外，通过比较考虑的参数的相对误差，我们确定了入侵场景更有利。特别是，相对于数据表/参考值，我们估计滤波器的中心频率偏移为84％，滤波器的6 dB带宽精度为75％。这意味着单个光学滤波器引入的估计信噪比（SNR）损失总共减少了0.24 dB。