Core, Mode, and Spectrum Assignment Based on Machine Learning in Space Division Multiplexing Elastic Optical Networks
Recently, network traffic has been growing exponentially and almost reached the physical capacity limit of single mode fibers. Space division multiplexing (SDM) is a promising technology to overcome the looming fiber capacity crunch. Especially, few-mode multi-core fibers (FM-MCFs) can aggregate mul...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2018-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8307057/ |
| _version_ | 1818877150566023168 |
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| author | Qiuyan Yao Hui Yang Ruijie Zhu Ao Yu Wei Bai Yuanlong Tan Jie Zhang Hongyun Xiao |
| author_facet | Qiuyan Yao Hui Yang Ruijie Zhu Ao Yu Wei Bai Yuanlong Tan Jie Zhang Hongyun Xiao |
| author_sort | Qiuyan Yao |
| collection | DOAJ |
| description | Recently, network traffic has been growing exponentially and almost reached the physical capacity limit of single mode fibers. Space division multiplexing (SDM) is a promising technology to overcome the looming fiber capacity crunch. Especially, few-mode multi-core fibers (FM-MCFs) can aggregate multiple cores into one fiber and two or more modes can be transmitted in one core, which can greatly increase the capacity yet introduce crosstalk constraints including inter- and intra-core crosstalk. To our best knowledge, there is no accurate crosstalk calculation model study in SDM optical networks with FM-MCFs. To address this issue, we first introduce the machine learning into the crosstalk prediction phase and propose a novel crosstalk estimation model (CEM) exploiting the beam propagation method called CEM-beam propagation method (BPM)-machine learning (ML), which can be used to evaluate the crosstalk during the design for the resource allocation scheme. Then, a crosstalk aware core, mode, and spectrum assignment (CA-CMSA) strategy is presented. The simulation results for crosstalk estimation at the wavelength level indicate that the crosstalk at lower frequencies is less than that at higher frequencies. Thus, the lower frequencies are always the first choice in the spectrum resource assignment phase. In addition, for our specific training set, the Levenberg-Marquardt (LM) algorithm based on machine learning performs better on the training, including regression values measurement and time consumption. The simulation results of the proposed CA-CMSA scheme also show that the resource allocation algorithm based on LM can improve resource utilization without increasing total connection set-up time. Thus, it will be the best choice for the resource assignment in SDM networks with FM-MCFs. |
| first_indexed | 2024-12-19T13:53:43Z |
| format | Article |
| id | doaj.art-b9eb930146774861a258f599c3b950a7 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T13:53:43Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-b9eb930146774861a258f599c3b950a72022-12-21T20:18:38ZengIEEEIEEE Access2169-35362018-01-016158981590710.1109/ACCESS.2018.28117248307057Core, Mode, and Spectrum Assignment Based on Machine Learning in Space Division Multiplexing Elastic Optical NetworksQiuyan Yao0https://orcid.org/0000-0001-8753-6489Hui Yang1Ruijie Zhu2Ao Yu3https://orcid.org/0000-0002-1520-1097Wei Bai4Yuanlong Tan5Jie Zhang6Hongyun Xiao7State Key Laboratory of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications, Beijing, ChinaState Key Laboratory of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications, Beijing, ChinaState Key Laboratory of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications, Beijing, ChinaState Key Laboratory of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications, Beijing, ChinaState Key Laboratory of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications, Beijing, ChinaDepartment of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, USAState Key Laboratory of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications, Beijing, ChinaZTE Corporation, Shenzhen, ChinaRecently, network traffic has been growing exponentially and almost reached the physical capacity limit of single mode fibers. Space division multiplexing (SDM) is a promising technology to overcome the looming fiber capacity crunch. Especially, few-mode multi-core fibers (FM-MCFs) can aggregate multiple cores into one fiber and two or more modes can be transmitted in one core, which can greatly increase the capacity yet introduce crosstalk constraints including inter- and intra-core crosstalk. To our best knowledge, there is no accurate crosstalk calculation model study in SDM optical networks with FM-MCFs. To address this issue, we first introduce the machine learning into the crosstalk prediction phase and propose a novel crosstalk estimation model (CEM) exploiting the beam propagation method called CEM-beam propagation method (BPM)-machine learning (ML), which can be used to evaluate the crosstalk during the design for the resource allocation scheme. Then, a crosstalk aware core, mode, and spectrum assignment (CA-CMSA) strategy is presented. The simulation results for crosstalk estimation at the wavelength level indicate that the crosstalk at lower frequencies is less than that at higher frequencies. Thus, the lower frequencies are always the first choice in the spectrum resource assignment phase. In addition, for our specific training set, the Levenberg-Marquardt (LM) algorithm based on machine learning performs better on the training, including regression values measurement and time consumption. The simulation results of the proposed CA-CMSA scheme also show that the resource allocation algorithm based on LM can improve resource utilization without increasing total connection set-up time. Thus, it will be the best choice for the resource assignment in SDM networks with FM-MCFs.https://ieeexplore.ieee.org/document/8307057/Coremodespectrum assignmentcrosstalkmachine learningelastic optical networks |
| spellingShingle | Qiuyan Yao Hui Yang Ruijie Zhu Ao Yu Wei Bai Yuanlong Tan Jie Zhang Hongyun Xiao Core, Mode, and Spectrum Assignment Based on Machine Learning in Space Division Multiplexing Elastic Optical Networks IEEE Access Core mode spectrum assignment crosstalk machine learning elastic optical networks |
| title | Core, Mode, and Spectrum Assignment Based on Machine Learning in Space Division Multiplexing Elastic Optical Networks |
| title_full | Core, Mode, and Spectrum Assignment Based on Machine Learning in Space Division Multiplexing Elastic Optical Networks |
| title_fullStr | Core, Mode, and Spectrum Assignment Based on Machine Learning in Space Division Multiplexing Elastic Optical Networks |
| title_full_unstemmed | Core, Mode, and Spectrum Assignment Based on Machine Learning in Space Division Multiplexing Elastic Optical Networks |
| title_short | Core, Mode, and Spectrum Assignment Based on Machine Learning in Space Division Multiplexing Elastic Optical Networks |
| title_sort | core mode and spectrum assignment based on machine learning in space division multiplexing elastic optical networks |
| topic | Core mode spectrum assignment crosstalk machine learning elastic optical networks |
| url | https://ieeexplore.ieee.org/document/8307057/ |
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