Dr. Alexander Y. Grebeshkov Profile

Dr. Alexander Y. Grebeshkov

at PSUTI

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Proceedings Article | 22 May 2020 Paper

Optical transport network management via machine learning and ontology-based technique

Alexander Grebeshkov

Proceedings Volume 11516, 1151602 (2020) https://doi.org/10.1117/12.2556375

KEYWORDS: Optical networks, Machine learning, Data modeling, Artificial intelligence

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Modern optical transport network is a highly complex object and introduces a challenge to managing and exchanging of management information in an accurate and recognize manner. Different optical technologies and service delivering rules increase the complexity of the network management activities. This paper analyzes machine-learning techniques of the optical network management with taking into account the knowledge representation based on the object-oriented ontology. Object-oriented ontology can be defined in general as a formal specification of a machine-readable vocabulary which is result of experts analyze. Object-oriented ontology is formulated in the terms of classes or entities and associations between them, to describe knowledge about the contents of the optical transport network management and another related object as quality of transmission, wavelength routing, flexible grid. Object-oriented ontology can be mapped for a machine learning models in the context of general management networks areas as fault management, performance management, configuration management, security management, business process management. Some machine-learning techniques for the optical network test and management are examined. Main entities of optical transport network management and their relations are discussed.

Proceedings Article | 6 April 2017 Paper

Cognitive optical networks: architectures and techniques

Alexander Grebeshkov

Proceedings Volume 10342, 1034206 (2017) https://doi.org/10.1117/12.2270294

KEYWORDS: Optical networks, Network architectures, Control systems, Radio optics, Networks, Digital signal processing, Modulation, Cognitive modeling, Optical network architecture, Interfaces

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This article analyzes architectures and techniques of the optical networks with taking into account the cognitive methodology based on continuous cycle “Observe–Orient–Plan–Decide–Act–Learn” and the ability of the cognitive systems adjust itself through an adaptive process by responding to new changes in the environment. Cognitive optical network architecture includes cognitive control layer with knowledge base for control of software–configurable devices as reconfigurable optical add-drop multiplexers, flexible optical transceivers, software–defined receivers. Some techniques for cognitive optical networks as flexible–grid technology, broker–oriented technique, machine learning are examined. Software defined optical network and integration of wireless and optical networks with radio over fiber technique and fiber–wireless technique in the context of cognitive technologies are discussed.

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