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Author Pinto-Rios, J.; Calderon, F.; Leiva, A.; Hermosilla, G.; Beghelli, A.; Borquez-Paredes, D.; Lozada, A.; Jara, N.; Olivares, R.; Saavedra, G. doi  openurl
  Title Resource Allocation in Multicore Elastic Optical Networks: A Deep Reinforcement Learning Approach Type
  Year 2023 Publication Complexity Abbreviated Journal Complexity  
  Volume 2023 Issue Pages (up) 4140594  
  Keywords SPECTRUM ASSIGNMENT; ARCHITECTURE; CROSSTALK; EFFICIENT; CORE  
  Abstract 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.  
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  Series Volume Series Issue Edition  
  ISSN 1076-2787 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000947244300001 Approved  
  Call Number UAI @ alexi.delcanto @ Serial 1772  
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