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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2412.11614 |
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| _version_ | 1866916926388699136 |
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| author | Xia, Ruiyang Gao, Guanjun Zhao, Zanshan Wang, Haoyu Wen, Kun Wang, Daobin |
| author_facet | Xia, Ruiyang Gao, Guanjun Zhao, Zanshan Wang, Haoyu Wen, Kun Wang, Daobin |
| contents | The enhanced Gaussian noise (EGN) model, which accounts for inter-channel stimulated Raman scattering (ISRS), has been extensively utilized for evaluating nonlinear interference (NLI) within the C+L band. Compared to closed-form expressions and machine learning-based NLI evaluation models, it demonstrates broader applicability and its accuracy is not dependent on the support of large-scale datasets. However, its high computational complexity often results in lengthy computation times. Through analysis, the high-frequency oscillations of the four-wave mixing (FWM) efficiency factor integrand were identified as a primary factor limiting the computational speed of the ISRS EGN model. To address this issue, we propose an approximation method to derive a closed-form expression for the FWM efficiency factor, which provides both high accuracy and high computational efficiency. Numerical results demonstrate that the method proposed in this work could achieve low error levels under high ISRS influence levels, with an absolute mean error (MAE) of approximately 0.0033 dB. Furthermore, a parallel computation strategy targeting independent regions within the integration domain is proposed and optimized, which further significantly improves computing efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_11614 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Acceleration and Parallelization Methods for ISRS EGN Model Xia, Ruiyang Gao, Guanjun Zhao, Zanshan Wang, Haoyu Wen, Kun Wang, Daobin Signal Processing The enhanced Gaussian noise (EGN) model, which accounts for inter-channel stimulated Raman scattering (ISRS), has been extensively utilized for evaluating nonlinear interference (NLI) within the C+L band. Compared to closed-form expressions and machine learning-based NLI evaluation models, it demonstrates broader applicability and its accuracy is not dependent on the support of large-scale datasets. However, its high computational complexity often results in lengthy computation times. Through analysis, the high-frequency oscillations of the four-wave mixing (FWM) efficiency factor integrand were identified as a primary factor limiting the computational speed of the ISRS EGN model. To address this issue, we propose an approximation method to derive a closed-form expression for the FWM efficiency factor, which provides both high accuracy and high computational efficiency. Numerical results demonstrate that the method proposed in this work could achieve low error levels under high ISRS influence levels, with an absolute mean error (MAE) of approximately 0.0033 dB. Furthermore, a parallel computation strategy targeting independent regions within the integration domain is proposed and optimized, which further significantly improves computing efficiency. |
| title | Acceleration and Parallelization Methods for ISRS EGN Model |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2412.11614 |