###### Abstract
However, the larger impact of the ISRS effect on MB transmission and the large number of signals being transmitted increase the complexity of estimating the QoT. This in turn demands solutions that can provide similar accuracy but at lower computational complexity [13; 14]. Due to the high complexity and limited time available for QoT estimation during the provisioning phase, the application of Machine Learning (ML) techniques [15] can provide significant advantages [16]. In addition, ML models can be used to construct a digital twin (DT) of the optical network [17]. In fact, the use of a DT is seen as an essential tool for network automation and zero-touch networking. The authors of [18; 19; 20; 21] proposed OCATA, a DT for the optical time domain based on the application of Deep Neural Networks (DNN) modeling optical components, and experimentally showed that it can be used as a reliable and low-complex approach for accurately estimating the QoT, specifically the pre-Forward Error Correction (FEC) Bit Error Rate (BER), of an optical connection. The main drawback to using ML techniques is the availability of large datasets for training the models. This is especially important in the case of training DNNs. In the case of optical transmission, the datasets can be generated from experiments and from simulation. However, few experimental data are available for MB optical transmission, and simulation tools require a long running time to produce data as a result of the large number of channels that need to be simulated. For that very reason, the authors in [22] proposed a method for solving the generalized nonlinear Schrodinger equation that runs much faster than the traditional split-step Fourier method, thus providing an efficient and accurate method for large training dataset generation. In addition to QoT estimation, recent advancements in Digital Signal Processing (DSP) and coherent detection make it possible to configure optical transponders to operate with different sets of transmission parameters, named operational modes, including the modulation format (MF), symbol rate (SR), and FEC [23]. The authors in [24] proposed a complete workflow for connection provisioning that includes selection of the operational mode that better fits the candidate lightpath. This, however, can result in the set-up of several lightpaths that together provide the required capacity [25]. Some other works in the literature have proposed nonlinear mitigation techniques to improve the QoT by adapting the detection areas in the receiver (Rx) using ML techniques (see e.g., [26]). Such approaches are difficult to implement in practice, since the algorithms need to run in the DSP itself. In our previous paper [27], we proposed using the OCATA DT to accurately estimate the pre-FEC BER of the different channels during lightpath provisioning, as well as to mitigate the NLI noise in the Rx by optimizing the detection areas of the in-phase (I) and quadrature (Q) constellation points of the higher-order quadrature amplitude modulation (QAM) optical signals. Note that the proposed nonlinear mitigation is computed at provisioning time, so the DSP uses the optimized detection areas as a simple map. In this paper, we extend [27] and present a complete methodology to adapt OCATA for MB optical transmission and provide the needed support to the RSA algorithm in charge of the selection of a route and channel assignment running in the software-defined networking (SDN) controller. Specifically, the contributions of this paper are:  
* The OCATA-MB methodology sketched in Section 2, which enables the application of digital twinning solutions for accurate, fast, and scalable QoT estimation and nonlinear mitigation. Such applications allow the DT to be part of the lightpath provisioning process in the SDN controller, in particular for channel assignment. * The models and algorithmic approaches to implement the OCATA-MB methodology and the on-line MB-RSA, described in Section 3.