| Autor: |
Juan Pinto-Ríos, Felipe Calderón, Ariel Leiva, Gabriel Hermosilla, Alejandra Beghelli, Danilo Bórquez-Paredes, Astrid Lozada, Nicolás Jara, Ricardo Olivares, Gabriel Saavedra |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Complexity, Vol 2023 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
1099-0526 |
| DOI: |
10.1155/2023/4140594 |
| Popis: |
A deep reinforcement learning (DRL) approach is applied, for the first time, to solve the routing, modulation, spectrum, and core allocation (RMSCA) problem in dynamic multicore fiber elastic optical networks (MCF-EONs). To do so, a new environment was designed and implemented to emulate the operation of MCF-EONs - taking into account the modulation format-dependent reach and intercore crosstalk (XT) - and four DRL agents were trained to solve the RMSCA problem. The blocking performance of the trained agents was compared through simulation to 3 baselines RMSCA heuristics. Results obtained for the NSFNet and COST239 network topologies under different traffic loads show that the best-performing agent achieves, on average, up to a four-times decrease in blocking probability with respect to the best-performing baseline heuristic method. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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