Abstract

Elastic Optical Networking enables efficient use of spectral resources at the cost of a large parameter space which needs to be optimized to maximize transmission bandwidth. By formulating this optimization problem as a Markov Decision Process, we show that by using a state of the art Reinforcement Learning algorithm an agent can be trained, which is able to select near optimal parameters for different link conditions within seconds. Furthermore, the trained agent is able to generalize to unseen conditions, removing the need to optimize and train for every possible link scenario.

Deep reinforcement learning for proactive spectrum defragmentation in elastic optical networks

Ehsan Etezadi, Carlos Natalino, Renzo Diaz, Anders Lindgren, Stefan Melin, Lena Wosinska, Paolo Monti, and Marija Furdek
J. Opt. Commun. Netw. 15(10) E86-E96 (2023)

Leveraging double-agent-based deep reinforcement learning to global optimization of elastic optical networks with enhanced survivability

Xiao Luo, Chen Shi, Liqian Wang, Xue Chen, Yang Li, and Tao Yang
Opt. Express 27(6) 7896-7911 (2019)

Pre- and post-processing techniques for reinforcement-learning-based routing and spectrum assignment in elastic optical networks

Takafumi Tanaka and Masayuki Shimoda
J. Opt. Commun. Netw. 15(12) 1019-1029 (2023)

Reinforcement-learning-based path planning in multilayer elastic optical networks [Invited]

Takafumi Tanaka
J. Opt. Commun. Netw. 16(1) A68-A77 (2024)

Techniques for applying reinforcement learning to routing and wavelength assignment problems in optical fiber communication networks

Josh W. Nevin, Sam Nallaperuma, Nikita A. Shevchenko, Zacharaya Shabka, Georgios Zervas, and Seb J. Savory
J. Opt. Commun. Netw. 14(9) 733-748 (2022)