Pinto-Rios, J., Calderon, F., Leiva, A., Hermosilla, G., Beghelli, A., Borquez-Paredes, D., et al. (2023). Resource Allocation in Multicore Elastic Optical Networks: A Deep Reinforcement Learning Approach. Complexity, 2023, 4140594.
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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