Application of Deep Convolutional Neural Network for Automatic Detection of Digital Optical Fiber Repeater
The digital optical fiber repeater (DOFR) is an important infrastructure in the LTE networks, which solve the problem of poor regional signal quality. Various types of conventional measurement data from the LTE network cannot indicate whether a working DOFR is present in the cell. Currently, the det...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/19/7257 |
| _version_ | 1797476962612019200 |
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| author | Xingkang Tian Fan Wu Cong Zhang Wenhao Fan Yuanan Liu |
| author_facet | Xingkang Tian Fan Wu Cong Zhang Wenhao Fan Yuanan Liu |
| author_sort | Xingkang Tian |
| collection | DOAJ |
| description | The digital optical fiber repeater (DOFR) is an important infrastructure in the LTE networks, which solve the problem of poor regional signal quality. Various types of conventional measurement data from the LTE network cannot indicate whether a working DOFR is present in the cell. Currently, the detection of DOFRs relies solely on maintenance engineers for field detection. Manual detection methods are not timely or efficient, because of the large number and wide geographical distribution of DOFRs. Implementing automatic detection of DOFR can reduce the maintenance cost for mobile network operators. We treat the DOFR detection problem as a classification problem and employ a deep convolutional neural network (DCNN) to tackle it. The measurement report (MR) we used in this paper are tabular data, which is not an ideal input for DCNN. We propose a novel MR representation method that takes the overall MR data of a cell as a sample rather than a single record in the table, and represents the MR data as a pseudo-image matrix (PIM). The PIM will be used as the input for training DCNN, and the trained DCNN will be used to perform DOFR detection tasks. We conducted a series of experiments on real MR data, and the classification accuracy can achieve 93%. The proposed AI-based method can effectively detect the DOFR in a cell. |
| first_indexed | 2024-03-09T21:11:14Z |
| format | Article |
| id | doaj.art-8d15d13b371e4f27bdc5d15a9e26e08a |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T21:11:14Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-8d15d13b371e4f27bdc5d15a9e26e08a2023-11-23T21:46:09ZengMDPI AGSensors1424-82202022-09-012219725710.3390/s22197257Application of Deep Convolutional Neural Network for Automatic Detection of Digital Optical Fiber RepeaterXingkang Tian0Fan Wu1Cong Zhang2Wenhao Fan3Yuanan Liu4School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100088, ChinaSchool of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100088, ChinaSchool of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100088, ChinaSchool of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100088, ChinaSchool of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100088, ChinaThe digital optical fiber repeater (DOFR) is an important infrastructure in the LTE networks, which solve the problem of poor regional signal quality. Various types of conventional measurement data from the LTE network cannot indicate whether a working DOFR is present in the cell. Currently, the detection of DOFRs relies solely on maintenance engineers for field detection. Manual detection methods are not timely or efficient, because of the large number and wide geographical distribution of DOFRs. Implementing automatic detection of DOFR can reduce the maintenance cost for mobile network operators. We treat the DOFR detection problem as a classification problem and employ a deep convolutional neural network (DCNN) to tackle it. The measurement report (MR) we used in this paper are tabular data, which is not an ideal input for DCNN. We propose a novel MR representation method that takes the overall MR data of a cell as a sample rather than a single record in the table, and represents the MR data as a pseudo-image matrix (PIM). The PIM will be used as the input for training DCNN, and the trained DCNN will be used to perform DOFR detection tasks. We conducted a series of experiments on real MR data, and the classification accuracy can achieve 93%. The proposed AI-based method can effectively detect the DOFR in a cell.https://www.mdpi.com/1424-8220/22/19/7257digital optical fiber repeaterautomatic detectionmeasurement report datadeep learning |
| spellingShingle | Xingkang Tian Fan Wu Cong Zhang Wenhao Fan Yuanan Liu Application of Deep Convolutional Neural Network for Automatic Detection of Digital Optical Fiber Repeater Sensors digital optical fiber repeater automatic detection measurement report data deep learning |
| title | Application of Deep Convolutional Neural Network for Automatic Detection of Digital Optical Fiber Repeater |
| title_full | Application of Deep Convolutional Neural Network for Automatic Detection of Digital Optical Fiber Repeater |
| title_fullStr | Application of Deep Convolutional Neural Network for Automatic Detection of Digital Optical Fiber Repeater |
| title_full_unstemmed | Application of Deep Convolutional Neural Network for Automatic Detection of Digital Optical Fiber Repeater |
| title_short | Application of Deep Convolutional Neural Network for Automatic Detection of Digital Optical Fiber Repeater |
| title_sort | application of deep convolutional neural network for automatic detection of digital optical fiber repeater |
| topic | digital optical fiber repeater automatic detection measurement report data deep learning |
| url | https://www.mdpi.com/1424-8220/22/19/7257 |
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