Abstract
As the Internet of Things (IoT) develops, applying machine learning on optical communications has become a prospective field of research. Scholars have mostly concentrated on algorithmic techniques or specific applications but have been unable to address the distribution of machine-learning technologies and the development of its applications in optical communications from a macro perspective. Therefore, in this paper, machine-learning patents in optical communications are taken as the analytical basis for constructing a patent technology network. The study results revealed that key technologies were primarily in data input and output devices, data-processing methods, wireless communication networks, and the transmission of digital information in optical communications. Such technologies were also applied to perform measurement for diagnostic purposes and medical diagnoses. The technology network model proposed in this paper explores the technological development trends of machine learning in optical communications and serves as a reference for allocating research and development resources.
Highlights
Optical communications have greatly advanced in signal serial communication speed, agile channel spacing, modulation formats, and coding schemes
Before performing technology network analysis, the patent retrieval results were analyzed to gain a preliminary overview of technological development
The findings revealed that among data input and output devices, data-processing wireless communication networks, and digital information transmission
Summary
Optical communications have greatly advanced in signal serial communication speed, agile channel spacing, modulation formats, and coding schemes. Relevant technologies have yet to fully meet the complexity and performance requirements of future optical communication system networks. Machine-learning technologies play a significant role in network planning, failure prediction, and optical performance monitoring in optical communication systems [2,3,4]. Intelligent optical communication system networks will be automated and adaptive and become capable of predicting traffic demands to maximize performance. To achieve this goal, the integration of machine-learning mathematics, programming, and algorithms is necessary in optical communications, and these are the key directions of future optical communication development
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