[WP-A] Poster Session 2

[WP-A-3] Dynamic RWA for Programmable Filterless Optical Networks Based on Deep Reinforcement Learning

zhaoyang liu¹, taoning zheng¹, tingyi yao¹, xiangyu ge¹, yi fang¹, ○bitao pan¹ (1. Beijing University of Posts and Telecommunications (China))

Keywords:

Artificial intelligence and machine learning for optical network design,control,and management

We propose a graph convolutional network enhanced reinforcement learning framework for dynamic resource allocation in programmable filterless optical networks. It realized 9.2% and 19% blocking rate reductions compared to two benchmarks with lower wavelength waste.