Machine-learning-based impairment-aware dynamic RMSCA in multi-core elastic optical networks

IF 4 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Journal of Optical Communications and Networking Pub Date : 2024-09-11 DOI:10.1364/JOCN.530035

Jaya Lakshmi Ravipudi;Maite Brandt-Pearce

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引用次数: 0

Abstract

This paper presents a routing, modulation, spectrum, and core assignment (RMSCA) algorithm for space-division-multiplexing-based elastic optical networks (SDM-EONs) comprising multi-core links. A network state-dependent route and core selection method is proposed using a deep neural network (DNN) classifier. The DNN is trained using a metaheuristic optimization algorithm to predict lightpath suitability, considering the quality of transmission and resource availability. Physical layer impairments, including inter-core crosstalk, amplified spontaneous emission, and Kerr fiber nonlinearities, are considered, and a random forest (RF)-based link noise estimator is proposed. A feature importance selection analysis is provided for all the features considered for the DNN classifier and the RF link noise estimator. The proposed machine-learning-enabled RMSCA approach is evaluated on three network topologies, USNET, NSFNET, and COST-239 with 7-core and 12-core fiber links. It is shown to be superior in terms of blocking probability, bandwidth blocking probability, and acceptable computational speed compared to the standard and published benchmarks at different traffic loads.

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多核弹性光网络中基于机器学习的损伤感知动态 RMSCA

本文介绍了一种路由、调制、频谱和核心分配（RMSCA）算法，适用于由多核心链路组成的基于空间分复用的弹性光网络（SDM-EON）。利用深度神经网络（DNN）分类器，提出了一种与网络状态相关的路由和核心选择方法。使用元启发式优化算法对 DNN 进行训练，以便在考虑传输质量和资源可用性的情况下预测光路的适用性。考虑了物理层损伤，包括内核间串扰、放大自发辐射和克尔光纤非线性，并提出了基于随机森林（RF）的链路噪声估计器。针对 DNN 分类器和射频链路噪声估计器所考虑的所有特征，提供了特征重要性选择分析。在 USNET、NSFNET 和 COST-239 三种网络拓扑结构（7 芯和 12 芯光纤链路）上对所提出的机器学习 RMSCA 方法进行了评估。结果表明，在不同流量负载下，该方法在阻塞概率、带宽阻塞概率和可接受的计算速度方面均优于标准基准和已发布基准。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Optical Communications and Networking 工程技术-电信学

CiteScore

9.40

自引率

16.00%

发文量

104

审稿时长

4 months

期刊介绍： The scope of the Journal includes advances in the state-of-the-art of optical networking science, technology, and engineering. Both theoretical contributions (including new techniques, concepts, analyses, and economic studies) and practical contributions (including optical networking experiments, prototypes, and new applications) are encouraged. Subareas of interest include the architecture and design of optical networks, optical network survivability and security, software-defined optical networking, elastic optical networks, data and control plane advances, network management related innovation, and optical access networks. Enabling technologies and their applications are suitable topics only if the results are shown to directly impact optical networking beyond simple point-to-point networks.