Delay aware routing and spectrum allocation based on Q-learning

Jiangnan Zhao; Wei Hong; Ningfeng Bai

doi:10.1117/12.3023675

18 March 2024 Delay aware routing and spectrum allocation based on Q-learning

Jiangnan Zhao, Wei Hong, Ningfeng Bai

Proceedings Volume 13104, Advanced Fiber Laser Conference (AFL2023); 131045G (2024) https://doi.org/10.1117/12.3023675
Event: Advanced Fiber Laser Conference (AFL2023), 2023, Shenzhen, China

Abstract

This study proposes Q-learning-based dynamic routing algorithms to address routing and spectrum allocation challenges in elastic optical networks (EONs). An adaptive reinforcement learning framework is employed to enable real-time learning and decision-making under varying network conditions. The proposed Q-learning algorithm takes into consideration both the available spectrum and delay constraints in real-time to make informed decisions during routing and spectrum allocation, resulting in improved network capacity and reduced blocking rates. Following the Q-learning routing algorithm, two commonly used spectrum allocation methods, namely first fit and last fit, are applied. Simulation results demonstrate that the proposed method yields a lower blocking probability compared to using a combination of K shortest path routing and classical spectrum allocation strategies.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jiangnan Zhao, Wei Hong, and Ningfeng Bai "Delay aware routing and spectrum allocation based on Q-learning", Proc. SPIE 13104, Advanced Fiber Laser Conference (AFL2023), 131045G (18 March 2024); https://doi.org/10.1117/12.3023675

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