Busyness level-based deep reinforcement learning method for routing, modulation, and spectrum assignment of elastic optical networks

Chengsheng Liang; Yuqi Tu; Yue-Cai Huang

doi:10.1117/12.3007879

21 December 2023 Busyness level-based deep reinforcement learning method for routing, modulation, and spectrum assignment of elastic optical networks

Chengsheng Liang, Yuqi Tu, Yue-Cai Huang

Author Affiliations +

Proceedings Volume 12966, AOPC 2023: AI in Optics and Photonics ; 1296624 (2023) https://doi.org/10.1117/12.3007879
Event: Applied Optics and Photonics China 2023 (AOPC2023), 2023, Beijing, China

Abstract

Deep reinforcement learning (DRL) has been introduced in routing, modulation and spectrum assignment (RMSA) of the elastic optical networks. Since the DRL agent’s learning is based on the state it observes and the reward it receives, key information should be embedded in the state and the reward. In previous studies, the observed and feedback information is limited. In this paper, we propose a busyness level-based DRL method for the RMSA of the elastic optical networks. Since the busyness of the links or transmission paths highly affects the performance, we believe the busyness information should be perceived by the agent to learn a good RMSA policy. Specifically, we define two indicators to quantify busyness level, and then combine these two indicators into the design of reward and state. Simulation results show that our approach works better than the case that busyness is not

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Chengsheng Liang, Yuqi Tu, and Yue-Cai Huang "Busyness level-based deep reinforcement learning method for routing, modulation, and spectrum assignment of elastic optical networks", Proc. SPIE 12966, AOPC 2023: AI in Optics and Photonics , 1296624 (21 December 2023); https://doi.org/10.1117/12.3007879

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KEYWORDS

Design and modelling

Modulation

Machine learning

Optical networks

Deep learning

Optical transmission

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