Paper Abstract and Keywords |
Presentation |
2024-02-16 10:30
Anomaly Detection on Radio over Fiber Gap filler using Machine Learning Koichiro Nakamura, Hiroto Yorioka, Takeshi Higashino, Minoru Okada (NAIST) |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Optical fiber-based gap filler is used to reduce terrestrial broadcasting dead-zone for mitigating the digital divide problem. Although the path-through type transmission enables us to simple configuration of the remote node, the methodology for detecting the fault has not been discussed in such as semiconductor fiber laser, optical fiber link, photodetector and electrical amplifiers. This article proposes an anomaly detection method for optical fiber-based Gap-filler using the machine learning technique. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Digital terrestrial television broadcasting / Radio over Fiber / Gap filler / Machine learning / Anomaly detection / / / |
Reference Info. |
ITE Tech. Rep., vol. 48, no. 5, BCT2024-26, pp. 21-24, Feb. 2024. |
Paper # |
BCT2024-26 |
Date of Issue |
2024-02-08 (BCT) |
ISSN |
Online edition: ISSN 2424-1970 |
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