DACNN-aided nonlinear equalizer for a probabilistic shaping coherent optical communication system
The probabilistic shaping (PS) technique is a key technology for fiber optic communication systems to further approach the Shannon limit. To solve the problem that nonlinear equalizers are ineffective for probabilistic shaping optical communication systems with non-uniform distribution, a distribution alignment convolutional neural network (DACNN)-aided nonlinear equalizer is proposed. The approach calibrates the equalizer using the probabilistic shaping prior distribution, which reduces the training complexity and improves the performance of the equalizer simultaneously. Experimental results show nonlinear equalization of 120 Gb/s PS 64QAM signals in a 375 km transmission scenario. The proposed DACNN equalizer improves the receiver sensitivity by 2.6 dB and 1.1 dB over the Volterra equalizer and convolutional neural network (CNN) equalizer, respectively. Meanwhile, DACNN converges with fewer training epochs than CNN, which provides great potential for mitigating the nonlinear distortion of PS signals in fiber optic communication systems.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:63 |
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| Enthalten in: |
Applied optics - 63(2024), 7 vom: 01. März, Seite 1881-1887 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Li, Yuzhe [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Revised 04.03.2024 published: Print Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1364/AO.517521 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM369271637 |
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| 100 | 1 | |a Li, Yuzhe |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a DACNN-aided nonlinear equalizer for a probabilistic shaping coherent optical communication system |
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| 500 | |a published: Print | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a The probabilistic shaping (PS) technique is a key technology for fiber optic communication systems to further approach the Shannon limit. To solve the problem that nonlinear equalizers are ineffective for probabilistic shaping optical communication systems with non-uniform distribution, a distribution alignment convolutional neural network (DACNN)-aided nonlinear equalizer is proposed. The approach calibrates the equalizer using the probabilistic shaping prior distribution, which reduces the training complexity and improves the performance of the equalizer simultaneously. Experimental results show nonlinear equalization of 120 Gb/s PS 64QAM signals in a 375 km transmission scenario. The proposed DACNN equalizer improves the receiver sensitivity by 2.6 dB and 1.1 dB over the Volterra equalizer and convolutional neural network (CNN) equalizer, respectively. Meanwhile, DACNN converges with fewer training epochs than CNN, which provides great potential for mitigating the nonlinear distortion of PS signals in fiber optic communication systems | ||
| 650 | 4 | |a Journal Article | |
| 700 | 1 | |a Chang, Huan |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Qi |e verfasserin |4 aut | |
| 700 | 1 | |a Gao, Ran |e verfasserin |4 aut | |
| 700 | 1 | |a Tian, Feng |e verfasserin |4 aut | |
| 700 | 1 | |a Tian, Qinghua |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Yongjun |e verfasserin |4 aut | |
| 700 | 1 | |a Rao, Lan |e verfasserin |4 aut | |
| 700 | 1 | |a Guo, Dong |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Fu |e verfasserin |4 aut | |
| 700 | 1 | |a Zhou, Sitong |e verfasserin |4 aut | |
| 700 | 1 | |a Xin, Xiangjun |e verfasserin |4 aut | |
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