Deep Reinforcement Learning for Resource Allocation in Multi-Band Optical Networks

被引：0

作者：

Ben Terki, Abdennour ^{[1
]}

Pedro, Joao ^{[2
]}

Eira, Antonio ^{[2
]}

Napoli, Antonio ^{[3
]}

Sambo, Nicola ^{[1
]}

机构：

[1] Scuola Super Sant Anna, Pisa, Italy

[2] Infinera Unipessoal Lda, Carnaxide, Portugal

[3] Infinera, Munich, Germany

来源：

PROCEEDINGS OF THE 2024 INTERNATIONAL CONFERENCE ON OPTICAL NETWORK DESIGN AND MODELING, ONDM 2024 | 2024年

关键词：

routing and spectrum assignment; multi-band optical networks; blocking probability; deep reinforcement learning; optical performance; SPECTRUM ASSIGNMENT;

D O I：

10.23919/ONDM61578.2024.10582635

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Routing and Spectrum Assignment (RSA) is key to an efficient resource usage in optical networks. Although this problem is known to be complex, an even more complex version arises when considering multi-band (MB) optical networks, where the spectrum-dependency of performance becomes significantly more pronounced. This paper proposes a Deep Reinforcement Learning (DRL)-based strategy for RSA in MB optical networks leveraging the GNPy library for accurate estimation of optical performance. Simulation results show that DRL-RSA reduces blocking by up to 80% when comparing to state-of-the-art RSA strategies.

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页数：4

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