## 2 Provisioning MB Optical Connections
### OCATA for MB Optical Transmission  
The classical OCATA architecture for the C-band [18; 19; 20] (see Figure 2a) considers DNN models that are independent of the specific channel; a reference channel (RCh) in the middle of the C-band is considered. Incoming optical signals generated using the modulation format \(m\)-QAM are processed in the time domain. Therefore, incoming in-phase (I) and quadrature (Q) optical constellations with \(m\) distinct constellation points (CP) are processed. Specifically, an input IQ optical constellation (\(X\)) consists of a set of symbols \(x{\in}X\), where each \(x=[x^{i},\,x^{Q}]\) belongs to a CP. A features extraction (FeX) module finds features for each CP of the received signal by applying Gaussian mixture model (GMM) fitting [28], which characterizes the CP as a set of bivariate Gaussian distributions. The output of the FeX module is a set of features \(Y\), which contains five features for each CP, i.e., \(Y^{i}=(\mu^{I},\,\mu^{Q},\,\sigma^{I},\,\sigma^{Q},\,\sigma^{IQ})^{i}\) is the vector of features for CP \(i\), where \(\mu^{I}\) and \(\mu^{Q}\) represent the mean position for \(i\) over the \(I\) and \(Q\) axes, respectively, \(\sigma^{I}\) and \(\sigma^{Q}\) are the variance of \(i\) over the axes, and \(\sigma^{IQ}\) is the covariance between the axes. It is worth noting that the dispersion of symbols that belong to a certain CP provides valuable insight into the level of noise affecting the signal, both linear (LI) and NLI noise. As the total noise increases, dispersion of the symbols also increases. The FeX module is followed by a set of DNNs modeling the propagation of the signal on the optical components in the route of the lightpath. DNNs are pre-trained for the RCh, so the concatenated DNN representing the signal propagation can  
Figure 1: Overview of the considered MB scenario (**a**) and illustrative performance of MB optical transmission (**b**). be very quickly composed by selecting individual DNN models from a repository. Such propagation increases the LI and NLI noise, resulting in higher values for \(\sigma^{I}\) and \(\sigma^{Q}\) at the output of the DNNs, which can be related to QoT-related indicators like pre-FEC BER and SNR [20]. For scalability reasons, the DNN models consider the propagation of the features for a subset of selected CPs only (_selCP_). Precisely, two exterior and two interior CPs are selected, as proposed in [19], and from them a Constellation Reconstruction block reconstructs the features for the non-propagated CPs with high accuracy, i.e.,  
\[Y^{i}=F^{i}\big{(}[Y^{j},\forall j\in selCP]\big{)}\quad\forall i\in CP \tag{1}\]  
The Constellation Reconstruction block can be implemented as a DNN that takes as input propagated features [\(Y^{j}\), \(\forall j\in selCP\)] and outputs non-propagated features, as proposed in [19]. The OCATA architecture for the C-band, however, is difficult to use directly in MB scenarios, since the ISRS impacts the channels differently, and therefore the number of pre-trained DNN models in the repository will be too high. Because of scalability reasons, the architecture of the novel OCATA-MB (see Figure 2b) considers a few RChs representing the specifics of different areas in the spectrum. Because OCATA models CP features propagation, the RChs are selected based on the different behavior of such features for the selected CPs. To decide which channels become RChs, a RCh selection procedure fairly selects them, making a balance between the complexity of the models and the accuracy of the final results. However, the target channel is not necessarily one of the selected RChs. Therefore, OCATA-MB includes a feature composition block to estimate the CP features for any channel in the C+L+S bands based on the output of the DNN models propagating the features of the RChs. The output of the feature composition block can then be used for QoT estimation, as in the classical OCATA architecture [18; 19; 20].