Physical Layer-Aware Route and Spectrum Allocation in Optical Networks by Multi-Objective Deep Reinforcement Learning

We propose a multi-objective Reinforcement Learning approach for RSA in optical networks, integrating awareness of signal quality, delay, and load-balancing. It outperforms traditional algorithms with 0.7 dB SNR improvements, 15.2% latency and 68.3% blocking rate reduction.

Authors

Jiajia Xie (ORCID: https://orcid.org/0000-0002-9759-9931)
Yucheng Song (ORCID: https://orcid.org/0009-0009-4987-1783)
Danshi Wang (ORCID: https://orcid.org/0000-0001-9815-4013)
Shengnan Li (ORCID: https://orcid.org/0009-0001-9657-074X)
Min Zhang (ORCID: https://orcid.org/0000-0002-8230-300X)
Yao Zhang

Institutions

Beijing University of Posts and Telecommunications (CN)
State Key Laboratory of Information Photonics and Optical Communications

Publication Details

Published: 2024-11-02
DOI: https://doi.org/10.1109/acp/ipoc63121.2024.10809683
Citations: 3
Primary Topic: Advanced Photonic Communication Systems
Type: article
Field-Weighted Citation Impact: 0.63

Funders

National Natural Science Foundation of China
Beijing Nova Program

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article

Physical Layer-Aware Route and Spectrum Allocation in Optical Networks by Multi-Objective Deep Reinforcement Learning

Jiajia Xie, Yucheng Song, Danshi Wang, Shengnan Li et al.

Nov 2, 2024

3 citations

Advanced Photonic Communication Systems

0.63

article

Physical Layer-Aware Route and Spectrum Allocation in Optical Networks by Multi-Objective Deep Reinforcement Learning

Jiajia Xie, Yucheng Song, Danshi Wang, Shengnan Li, Min Zhang, Yao Zhang

November 2, 2024article en

3 citations

Abstract

We propose a multi-objective Reinforcement Learning approach for RSA in optical networks, integrating awareness of signal quality, delay, and load-balancing. It outperforms traditional algorithms with 0.7 dB SNR improvements, 15.2% latency and 68.3% blocking rate reduction.

Beijing University of Posts and Telecommunications (CN), State Key Laboratory of Information Photonics and Optical Communications

National Natural Science Foundation of China, Beijing Nova Program

Openalex Percentile: Top 30%

Advanced Photonic Communication Systems

0.63

DOI Source OpenAlex

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Physical Layer-Aware Route and Spectrum Allocation in Optical Networks by Multi-Objective Deep Reinforcement Learning — Jiajia Xie, Yucheng Song, et al. · (2024) | TGRS Research Map | TGRS