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Machine learning for intelligent optical networks: A comprehensive survey
Journal of Network and Computer Applications ( IF 8.7 ) Pub Date : 2020-02-11 , DOI: 10.1016/j.jnca.2020.102576
Rentao Gu , Zeyuan Yang , Yuefeng Ji

With the rapid development of Internet and communication systems, both in the aspect of services and technologies, communication networks have been suffering increasing complexity. It is imperative to improve intelligence in communication networks, and several aspects have been incorporating with Artificial Intelligence (AI) and Machine Learning (ML). The optical network, which plays an important role both in core and access network in communication networks, also faces great challenges of system complexity and the requirement of manual operations. To overcome the current limitations and address the issues of future optical networks, it is essential to deploy more intelligence capability to enable autonomous and flexible network operations. ML techniques are proved to have superiority on solving complex problems, and thus recently, ML techniques have been used for many optical network applications. In this paper, a detailed survey of existing applications of ML for intelligent optical networks is presented. The applications of ML are classified in terms of their use cases, which are categorised into optical network control and resource management, and optical network monitoring and survivability. These applications are analyzed and compared according to the used ML techniques. Besides, a tutorial for ML applications is provided from the aspects of the introduction of common ML algorithms, paradigms of ML, and motivations of applying ML. Lastly, challenges and possible solutions of ML application in intelligent optical networks are also discussed, which intends to inspire future innovations in leveraging ML to build intelligent optical networks.



中文翻译:

智能光网络的机器学习:全面调查

随着因特网和通信系统的快速发展,无论是在服务还是技术方面,通信网络的复杂性都在不断增加。必须提高通信网络中的智能,并且人工智能(AI)和机器学习(ML)已融合了多个方面。在通信网络的核心网和接入网中都扮演着重要角色的光网络,还面临着系统复杂性和手动操作需求的巨大挑战。为了克服当前的局限性并解决未来的光网络问题,必不可少的是部署更多的情报功能以实现自主和灵活的网络运行。事实证明,机器学习技术在解决复杂问题方面具有优势,因此最近,ML技术已用于许多光网络应用。在本文中,将详细介绍ML在智能光网络中的现有应用。ML的应用按照用例进行分类,分为光网络控制和资源管理,光网络监视和生存能力。这些应用程序将根据使用的ML技术进行分析和比较。此外,还从常见的ML算法的介绍,ML的范例以及应用ML的动机等方面提供了ML应用的教程。最后,还讨论了ML在智能光网络中的应用所面临的挑战和可能的解决方案,旨在激发未来利用ML构建智能光网络的创新。

更新日期:2020-02-11
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