Resource allocation in multicore elastic optical networks: a deep reinforcement learning approach
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 …
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 …
Resource Allocation in Multicore Elastic Optical Networks: A Deep Reinforcement Learning Approach
J Pinto-RÃos, F Calderón, A Leiva, G Hermosilla… - …, 2023 - econpapers.repec.org
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 …
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 …
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