Model-Aware XGBoost Method Towards Optimum Performance of Flexible Distributed Raman Amplifier
Toward the next-generation ultra-long-haul optical network, an extremely gradient boosting (XGBoost)-aided machine learning (ML) model is proposed to maximize the flexibility and uniformity in the performance of distributed Raman amplifier (DRA). In order to achieve an accurate prediction of desired...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Photonics Journal |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10152494/ |
| _version_ | 1797731244682772480 |
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| author | Anand Prakash Jaisingh Thangaraj Sharbani Roy Shaury Srivastav Jitendra K. Mishra |
| author_facet | Anand Prakash Jaisingh Thangaraj Sharbani Roy Shaury Srivastav Jitendra K. Mishra |
| author_sort | Anand Prakash |
| collection | DOAJ |
| description | Toward the next-generation ultra-long-haul optical network, an extremely gradient boosting (XGBoost)-aided machine learning (ML) model is proposed to maximize the flexibility and uniformity in the performance of distributed Raman amplifier (DRA). In order to achieve an accurate prediction of desired signal gain spectrum and bit error rate (BER), a novel decision-tree based system is employed against inconsistent dimensionality between pump frequency and power. The impact of various model evaluation techniques: mean squared error (MSE), coefficient of determination (R<sup>2</sup>), root mean square measured data ratio (RSR) and the Nash-Sutcliffe coefficient (NSE) are discussed in detail. It is shown that the proposed method can diagnose the fault within 2.3 ms with accuracy of 99.6% and has also the highest estimation and efficacy in comparison with other ML based tree models. The reported work transforms the successful implementation of XGBoost model to estimate the desired gain profile and BER of DRA in low-loss optical wavelength region (1260–1650 nm). |
| first_indexed | 2024-03-12T11:56:02Z |
| format | Article |
| id | doaj.art-b6535e61a1a7453eb19d02518cea6813 |
| institution | Directory Open Access Journal |
| issn | 1943-0655 |
| language | English |
| last_indexed | 2024-03-12T11:56:02Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Photonics Journal |
| spelling | doaj.art-b6535e61a1a7453eb19d02518cea68132023-08-30T23:00:05ZengIEEEIEEE Photonics Journal1943-06552023-01-0115411010.1109/JPHOT.2023.328627210152494Model-Aware XGBoost Method Towards Optimum Performance of Flexible Distributed Raman AmplifierAnand Prakash0https://orcid.org/0000-0003-2963-5301Jaisingh Thangaraj1Sharbani Roy2Shaury Srivastav3https://orcid.org/0009-0001-6527-0736Jitendra K. Mishra4https://orcid.org/0000-0003-3193-4765Department of Electronics Engineering, Indian Institute of Technology (Indian School of Mines) Dhanbad, Dhanbad, Jharkhand, IndiaDepartment of Electronics Engineering, Indian Institute of Technology (Indian School of Mines) Dhanbad, Dhanbad, Jharkhand, IndiaDepartment of Electronics and Communication Engineering, Ramgarh Engineering College, Ramgarh, Jharkhand, IndiaDepartment of Electronics and Communication Engineering, Indian Institute of Information Technology Ranchi, Ranchi, Jharkhand, IndiaDepartment of Electronics and Communication Engineering, Indian Institute of Information Technology Ranchi, Ranchi, Jharkhand, IndiaToward the next-generation ultra-long-haul optical network, an extremely gradient boosting (XGBoost)-aided machine learning (ML) model is proposed to maximize the flexibility and uniformity in the performance of distributed Raman amplifier (DRA). In order to achieve an accurate prediction of desired signal gain spectrum and bit error rate (BER), a novel decision-tree based system is employed against inconsistent dimensionality between pump frequency and power. The impact of various model evaluation techniques: mean squared error (MSE), coefficient of determination (R<sup>2</sup>), root mean square measured data ratio (RSR) and the Nash-Sutcliffe coefficient (NSE) are discussed in detail. It is shown that the proposed method can diagnose the fault within 2.3 ms with accuracy of 99.6% and has also the highest estimation and efficacy in comparison with other ML based tree models. The reported work transforms the successful implementation of XGBoost model to estimate the desired gain profile and BER of DRA in low-loss optical wavelength region (1260–1650 nm).https://ieeexplore.ieee.org/document/10152494/DRAMLXGBoostSVMRFAdaBoost |
| spellingShingle | Anand Prakash Jaisingh Thangaraj Sharbani Roy Shaury Srivastav Jitendra K. Mishra Model-Aware XGBoost Method Towards Optimum Performance of Flexible Distributed Raman Amplifier IEEE Photonics Journal DRA ML XGBoost SVM RF AdaBoost |
| title | Model-Aware XGBoost Method Towards Optimum Performance of Flexible Distributed Raman Amplifier |
| title_full | Model-Aware XGBoost Method Towards Optimum Performance of Flexible Distributed Raman Amplifier |
| title_fullStr | Model-Aware XGBoost Method Towards Optimum Performance of Flexible Distributed Raman Amplifier |
| title_full_unstemmed | Model-Aware XGBoost Method Towards Optimum Performance of Flexible Distributed Raman Amplifier |
| title_short | Model-Aware XGBoost Method Towards Optimum Performance of Flexible Distributed Raman Amplifier |
| title_sort | model aware xgboost method towards optimum performance of flexible distributed raman amplifier |
| topic | DRA ML XGBoost SVM RF AdaBoost |
| url | https://ieeexplore.ieee.org/document/10152494/ |
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