Applications of Machine Learning in Optical Communications

Dear Colleagues,

Machine learning has recently entered the field of optical communications and is expected to become a key enabler of the development of future intelligent networks. Powerful machine learning techniques have been proposed to allow cognitive operations at all network levels from the physical layer and the development of adaptive, low complexity transceiver units with advanced signal equalization, monitoring and digital encoding capabilities, to higher layer node architectures and network topologies, which have the ability to predict future network conditions, traffic demands, connectivity failures and security threats, and accordingly to self-optimize their operation.

In this Special Issue of Applied Sciences, we wish to present the most recent advances of this field and stimulate discussion on new and unexplored topics. Topics of interest include, but are not limited, to the following application areas of machine learning:

• Intelligent network architectures and topologies;
• Network design and routing;
• QoT estimation;
• Network operation and traffic prediction;
• Failure management;
• Data center applications;
• Digital signal processing and impairment mitigation;
• Modulation format recognition;
• Neuromorphic computing.

Prof. Stylianos Sygletos
Dr. Mariia Sorokina
Guest Editors

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• machine learning
• artificial intelligence
• optical communications and networking
• digital signal processing
• non-linear signal processing
• optical fibre transmission
• network management and control
• intelligent routing