Simulation and modeling of C+L+S multiband optical transmission for the OCATA time domain digital twin
(English) This thesis focuses on implementing more robust control and management strategies such as those based on machine learning to enhance intelligence and drive towards autonomous operation is crucial for the future of optical communications. In this regard, this thesis aims at three specific o...
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| Format: | doctoral thesis |
| Status: | Published version |
| Publication Date: | 2025 |
| Country: | España |
| Institution: | CBUC, CESCA |
| Repository: | TDR. Tesis Doctorales en Red |
| OAI Identifier: | oai:www.tdx.cat:10803/695089 |
| Online Access: | http://hdl.handle.net/10803/695089 https://dx.doi.org/10.5821/dissertation-2117-439920 |
| Access Level: | Open access |
| Keyword: | Multiband optical transmission Signal propagation modelling Digital twin modelling. Àrees temàtiques de la UPC::Enginyeria de la telecomunicació 621.3 - Enginyeria elèctrica. Electrotècnia. Telecomunicacions |
| Summary: | (English) This thesis focuses on implementing more robust control and management strategies such as those based on machine learning to enhance intelligence and drive towards autonomous operation is crucial for the future of optical communications. In this regard, this thesis aims at three specific objectives: The first objective is to develop a multiband optical transmission simulator. It can be challenging to conduct simulations on a fully loaded MB system with hundreds of channels. In addition, in MB optical transmission, the Inter-channel Stimulated Raman Scattering (ISRS) becomes a major effect, which adds more complexity. In view of that, the Fourth Order Runge-Kutta in Interaction Picture (RK4IP) method is evaluated as an alternative to reduce time complexity, which is complemented with an adaptive step size algorithm to further reduce the computation time. We show that RK4IP provides an accuracy comparable to that of SSFM with reduced computation time, which enables its application for MB optical transmission simulation. The second objective focuses on developing models for C+L+S Multiband Optical Transmission System In this objective of this thesis, we focus on modelling MB optical transmission to provided fast and accurate QoT estimation and propose Machine Learning (ML) approaches based on neural networks, which can be easily integrated into an Optical Layer Digital Twin (DT) solution. We start by considering approaches that can be used for accurate signal propagation modelling. Even though solutions like the Splitstep Fourier method (SSFM) for solving the non-linear Schrödinger equation (NLSE) cannot be used for QoT estimation due to their very high time complexity. Therefore, ML modelling approaches are considered to be integrated in the OCATA DT, where models predict optical signal propagation in the time domain. Being able to predict the optical signal in the time domain, as it will be received after propagation, opens opportunities for automating network operation, including connection provisioning and failure management. The third objective of this thesis is to develop a semi-analytical model for measuring the gain profile of amplifiers in both fully loaded and partially loaded conditions of the metro- access network. The power imbalance is one of the issues associated with transitioning to a metro-access merged network. Additionally, maintaining these two separate networks (metro and access) is both complicated and costly. The node that interconnects the two networks must perform O-E-O conversions on the data traversing between them. The current network system uses ROADM in nodes, which is all optical and requires no O-E-O conversion. Therefore, this goal primarily focuses on characterizing the parameters of EDFA present in reconfigurable optical add-drop multiplexers (ROADMs) with the aim of achieving a balance between flexibility, complexity, and cost. These nodes must be thoroughly characterized regarding optical losses, power consumption, and other metrics. Once evaluated, the performance of these nodes can be modelled to enable the SDN controller to incorporate them into the network. |
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