Quality of transmission estimator retraining for dynamic optimization in optical networks
| Autor: | Salvatore Spadaro, Ankush Mahajan, Raul Munoz, Konstantinos Christodoulopoulos, Ricardo Martinez |
|---|---|
| Přispěvatelé: | Universitat Politècnica de Catalunya. Doctorat en Teoria del Senyal i Comunicacions, Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions, Universitat Politècnica de Catalunya. GCO - Grup de Comunicacions Òptiques |
| Rok vydání: | 2021 |
| Předmět: |
Optimization
Mathematical optimization Monitoring Computer Networks and Communications Iterative method Computer science Iterative methods Optical fibre networks 02 engineering and technology Dynamic priority scheduling Network topology 01 natural sciences 010309 optics 020210 optoelectronics & photonics Closed loop systems Optical fiber networks 0103 physical sciences 0202 electrical engineering electronic engineering information engineering Heuristic algorithms Comunicacions òptiques Optimisation Physical layer Optical communications Telecommunication control Process (computing) Dynamic scheduling Power optimization Enginyeria de la telecomunicació::Telecomunicació òptica [Àrees temàtiques de la UPC] Transmission (telecommunications) Control system Task analysis Light transmission |
| Zdroj: | Journal of Optical Communications and Networking UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) r-CTTC: Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC) Centre Tecnològic de Telecomunicacions de Catalunya (CTTC) r-CTTC. Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC) instname |
| ISSN: | 1943-0620 |
| DOI: | 10.1364/jocn.411524 |
| Popis: | Optical network optimization involves an algorithm and a physical layer model (PLM) to estimate the quality of transmission of connections while examining candidate optimization operations. In particular, the algorithm typically calculates intermediate solutions until it reaches the optimum, which is then configured to the network. If it uses a PLM that was aligned once to reflect the starting network configuration, then the algorithm within its intermediate calculations can project the network into states where the PLM suffers from low accuracy, resulting in a suboptimal optimization. In this paper, we propose to solve dynamic multivariable optimization problems with an iterative closed control loop process, where after certain algorithm steps we configure the intermediate solution so that we monitor and realign/retrain the PLM to follow the projected network states. The PLM is used as a digital twin, a digital representation of the real system, which is realigned during the dynamic optimization process. Specifically, we study the dynamic launch power optimization problem, where we have a set of established connections, and we optimize their launch powers while the network operates. We observed substantial improvements in the sum and the lowest margin when optimizing the launch powers with the proposed approach over optimization using a one-time trained PLM. The proposed approach achieved near-to-optimum solutions as found by optimizing and continuously probing and monitoring the network, but with a substantial lower optimization time. Funding: Horizon 2020 Framework Programme (765275). This work is a part of the Future Optical Networks for Innovation, Research and Experimentation (ONFIRE) project (https://h2020-onfire.eu/), supported by the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie Actions. |
| Databáze: | OpenAIRE |
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