物理层
计算机科学
转发器(航空)
概率逻辑
边距(机器学习)
传输(电信)
还原(数学)
电缆密封套
网络规划与设计
千兆位
电子工程
实时计算
电信
无线
工程类
数学
人工智能
机器学习
几何学
航空航天工程
作者
Oleg Karandin,Alessio Ferrari,Francesco Musumeci,Yvan Pointurier,Massimo Tornatore
出处
期刊:Journal of Optical Communications and Networking
[The Optical Society]
日期:2023-06-02
卷期号:15 (7): C129-C129
被引量:1
摘要
Analytical models for quality of transmission (QoT) estimation require safety design margins to account for uncertain knowledge of input parameters. We propose and evaluate a design procedure that gradually decreases these margins in the presence of multiple physical-layer uncertainties (namely, connector loss, erbium-doped fiber amplifier gain ripple, and fiber type) by leveraging monitoring data to build a probabilistic machine-learning-based QoT regressor. We evaluate the savings from margin reduction in terms of occupied spectrum and number of installed transponders in the C and C+L bands and demonstrate that 4%–12% transponder/spectrum savings can be achieved in realistic network instances by simply leveraging the SNR monitored at receivers and paying off a low increment in the lightpath disruption probability (at most 1%–4%).
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