Nonlinear Impairment-Aware RMSA under the Sliding Scheduled Traffic Model for EONs Based on Deep Reinforcement Learning

• Published in: Journal of lightwave technology Vol. 41; no. 22; pp. 1 - 11
• Main Authors: Zou, Yucong, Cai, Xiaofeng, Zhu, Min, Gu, Jiahua, Wang, Yunwu, Zhao, Guo, Shi, Chenglin, Zhang, Jiao, Cai, Yuancheng, Lei, Mingzheng
• Format: Journal Article
• Language: English
• Published: New York IEEE 15.11.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Blocking probability; Deep learning; Deep reinforcement learning; Heuristic algorithms; Impairment; Interference; Modulation; Nonlinear impairment; Optical communication; Optical fiber networks; Provisioning; Resource allocation; Resource management; Routing; Signal to noise ratio; Slide scheduling; Sliding; Topology; Traffic models

The elastic optical network (EON) can accommodate dynamic and diverse demands of next-generation applications by provisioning flexible lightpaths for them. Consequently, blocking probability of these requests can be reduced especially in sliding scheduled traffic, where requests are scheduled to sta...