PhD Thesis Defense: Machine Learning Assisted QoT Estimation for Optical Networks Optimization
In the context of this PhD Thesis defense, Ankush Mahajan presents his work realized at CTTC.
The tremendous increase in data traffic has spurred a rapid evolution of the optical networks for a reliable, affordable, cost effective and scalable network infrastructure. For effective optical network planning, operation and optimization, it is necessary to estimate the Quality of Transmission (QoT) of the connections. Typically, QoT estimation is performed using a Physical Layer Model (PLM) which is included in the QoT estimation tool or Qtool. A design margin is generally included in a Qtool to account for the modeling and parameter inaccuracies, to re-assure an acceptable performance. PLM accuracy is highly important as modeling errors translate into a higher design margin which in turn translate into wasted capacity or unwanted regeneration. The first part of the thesis focuses on the machine learning (ML) assisted accurate QoT estimation techniques with reduced design margin. In this regard, we developed models that uses monitoring information from an operating network combined with supervised ML regression techniques to understand the network conditions. In particular, we model the generated penalties due to i). EDFA gain ripple effect, and ii). filter spectral shape uncertainties at ROADM nodes. Furthermore, with the aim of improving the Qtool estimation accuracy in multi-vendor networks, we propose PLM extensions. In particular, we introduce four TP vendor dependent performance factors that capture the performance variations of multi-vendor TPs. In consequence, the last part of this thesis aims at investigating and solving the issue of accuracy limitation of Qtool in dynamic optimization tasks. To keep the models aligned to the real conditions, the digital twin (DT) concept is gaining significant attention in the research community. Based on the DT fundamentals, we devised and implemented an iterative closed control loop process that, after several intermediate iterations of the optimization algorithm, configures the network, monitors, and retrains the Qtool. The key advantage of this novel scheme is that whilst the network operates, the Qtool parameters are retrained according to the monitored information with the adopted ML model.