Deep RMCSA for Resource Allocation in Spectrally-Spatially Flexible Optical Networks

被引：0
|
作者：
Wong, Josh [1 ]
Doan, Natalie [1 ]
Aibin, Michal [1 ]
机构：
[1] British Columbia Inst Technol, Dept Comp, Vancouver, BC, Canada
来源：
2021 IEEE 12TH ANNUAL UBIQUITOUS COMPUTING, ELECTRONICS & MOBILE COMMUNICATION CONFERENCE (UEMCON) | 2021年
关键词：
optical networks; deep learning; routing;
D O I：
10.1109/UEMCON53757.2021.9666523
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
A gradual transition from traditional fixed frequency networks towards Spectrally-Spatially Flexible Optical Networks (SS-FONs) will ensure that networks continue to meet increasing Internet bandwidth demands. The DeepRMCSA algorithm, proposed in this paper, uses deep reinforcement learning to determine the optimal policies for solving the Routing, Modulation, Core and Spectrum Assignment problem in SS-FONs. We evaluate the performance of our algorithm by comparing it with other approaches used in the literature.
引用
收藏
页码：851 / 853
页数：3
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