# ARMONIA: A Unified Access and Metro Network Architecture
## 3 ARMONIA Algorithmic Mechanisms
### QoT Estimation for Metro Networks  
It is to the network operator's advantage to estimate the Quality of Transmission (QoT) of a new lightpath before its establishment or before modifying an existing one for a number of reasons: (a) avoid a trial-and-error approach of establishing several lightpaths; (b) reduce the operating margins, and thus reduce network costs; (c) enable the dynamic operation of the network, by estimating the QoT of a candidate set of reconfigured lightpaths.  
Many recent research papers have studied various methods to estimate the QoT using machine learning (ML) [21]. There are a number of different approaches for the task at hand. One is to leverage QoT data (e.g., from OPMs) to train an agnostic (in terms of the transmission model) ML algorithm. The algorithm can either estimate a raw QoT value (regression) [22], or estimate whether a lightpath has acceptable QoT (classification) [23]. Another approach is to leverage QoT data to improve the accuracy of the parameters' values of an existing QoT model based on theoretical formulas [22; 24].  
In our work, we designed a ML QoT estimator (Figure 5) that takes into account the specific characteristics of the metro topologies such as flexible amplifier placement (some spans may not have an amplifier), and high number of links that make it difficult to acquire sufficient training data. We consider the following features for each lightpath: number of spans, number of amplifiers, number of links, total path length, baud rate, and the mean load of a lightpath's links. We employ both the number of spans and the number of amplifiers, because in metro networks links' lengths are shorter than in core networks, and some spans may not have amplifiers. We also consider the number of links that a path contains, to capture the intermediate (e.g., insertion) losses that Reconfigurable Optical Add/Drop Multiplexers (ROADMs) and other devices may incur. We use the load metric to approximate the actual load of the network, instead of making a worst-case assumption for the nonlinear noise when all the neighboring channels are utilized. We avoid the use of individual features for each link of the network. In this way, the proposed ML algorithm can be adequately trained even with small sized training data. Therefore, the proposed algorithm can provide an estimation of the QoT of a new lightpath, even in the absence of training samples for some links.  
The objective of the proposed ML algorithm is the estimation of the lightpath's SNR. To do so, we use linear regression to train the ML model, assuming that the inverse of the SNR is approximately additive per link (GN model [25]). After the estimation of the SNR, the BER can be calculated by taking into account the modulation format of each lightpath. The BER is in turn compared to a threshold specified by the selected forward error correction (FEC) code. The result of this comparison is used to determine whether a lightpath is acceptable or not. We used the GN model to approximate the behavior of the physical layer and obtain the SNR estimates of certain lightpaths. These SNR estimates are in  
Figure 5: Block diagram of the quality of transmission estimator.  
essence the synthetic data that the ML model will leverage for training. More specifically, we used Equations 120 and 127-129 of [25] to calculate the power spectral density (PSD) of the non-linear noise. Then, we used Equation 15 to calculate the PSD of the amplified spontaneous emission noise, and Equation 11 to obtain the SNR. In practice the estimator can use the actual monitoring values from the OPMs for the training purposes. In Section 4, we describe the GN model parameters we considered to generate the data and we examine the accuracy of the QoT estimator.