Dynamic RWA for Programmable Filterless Optical Networks Based on Deep Reinforcement Learning

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.

Authors

Xiangyu Ge (ORCID: https://orcid.org/0000-0001-5956-8870)
Taixiong Zheng (ORCID: https://orcid.org/0000-0003-1605-7573)
Tianhao Yao (ORCID: https://orcid.org/0000-0002-3736-1964)
Bitao Pan (ORCID: https://orcid.org/0000-0003-3461-2075)
Yi Fang
Zhaoyang Liu (ORCID: https://orcid.org/0000-0002-9595-715X)

Institutions

Beijing University of Posts and Telecommunications (CN)

Publication Details

Published: 2025-06-29
DOI: https://doi.org/10.23919/oecc/psc62146.2025.11109482
Primary Topic: Optical Network Technologies
Type: article
Field-Weighted Citation Impact: 0.00

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article

Dynamic RWA for Programmable Filterless Optical Networks Based on Deep Reinforcement Learning

Xiangyu Ge, Taixiong Zheng, Tianhao Yao, Bitao Pan et al.

Jun 29, 2025

Optical Network Technologies

article

Dynamic RWA for Programmable Filterless Optical Networks Based on Deep Reinforcement Learning

Xiangyu Ge, Taixiong Zheng, Tianhao Yao, Bitao Pan, Yi Fang, Zhaoyang Liu

June 29, 2025article en

Abstract

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.

Beijing University of Posts and Telecommunications (CN)

Openalex Percentile: Top 80%

Optical Network Technologies

DOI Source OpenAlex

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