Multi-Impairment Compensation for CO-OFDM System Based on Deep Learning and LDPC Code
In this article, we propose a multi-impairment compensation scheme based on deep learning and low-density parity-check (LDPC) code for the coherent optical orthogonal frequency division multiplexing (CO-OFDM) system. We first propose a multi-impairment compensation autoencoder (MICAE) based on deep...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Photonics Journal |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10449366/ |
| _version_ | 1797243415474208768 |
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| author | Cong Hu Yuanxiang Chen Ying Han Jianguo Yu |
| author_facet | Cong Hu Yuanxiang Chen Ying Han Jianguo Yu |
| author_sort | Cong Hu |
| collection | DOAJ |
| description | In this article, we propose a multi-impairment compensation scheme based on deep learning and low-density parity-check (LDPC) code for the coherent optical orthogonal frequency division multiplexing (CO-OFDM) system. We first propose a multi-impairment compensation autoencoder (MICAE) based on deep neural network (DNN). Then we combine the proposed MICAE with LDPC code and design a DNN-based decoder for LDPC decoding to replace the traditional belief propagation (BP) decoder. The proposed scheme can compensate for multiple impairments simultaneously and has faster decoding speed, greatly improving the performance of the CO-OFDM transmission system. We demonstrate the superiority of the proposed scheme through simulations in different CO-OFDM transmission systems. Simulation results show that the proposed scheme can effectively improve the Q-factor and reduce the bit error rate (BER) of the system, and is more suitable for complex long-distance and high-speed optical transmission scenarios. |
| first_indexed | 2024-04-24T18:54:45Z |
| format | Article |
| id | doaj.art-134465c38ce442b6926f7b1b3ea30914 |
| institution | Directory Open Access Journal |
| issn | 1943-0655 |
| language | English |
| last_indexed | 2024-04-24T18:54:45Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Photonics Journal |
| spelling | doaj.art-134465c38ce442b6926f7b1b3ea309142024-03-26T17:35:11ZengIEEEIEEE Photonics Journal1943-06552024-01-011621710.1109/JPHOT.2024.337019110449366Multi-Impairment Compensation for CO-OFDM System Based on Deep Learning and LDPC CodeCong Hu0https://orcid.org/0000-0002-5309-5985Yuanxiang Chen1Ying Han2Jianguo Yu3https://orcid.org/0000-0002-9736-8471School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, ChinaSchool of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, ChinaSchool of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, ChinaSchool of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, ChinaIn this article, we propose a multi-impairment compensation scheme based on deep learning and low-density parity-check (LDPC) code for the coherent optical orthogonal frequency division multiplexing (CO-OFDM) system. We first propose a multi-impairment compensation autoencoder (MICAE) based on deep neural network (DNN). Then we combine the proposed MICAE with LDPC code and design a DNN-based decoder for LDPC decoding to replace the traditional belief propagation (BP) decoder. The proposed scheme can compensate for multiple impairments simultaneously and has faster decoding speed, greatly improving the performance of the CO-OFDM transmission system. We demonstrate the superiority of the proposed scheme through simulations in different CO-OFDM transmission systems. Simulation results show that the proposed scheme can effectively improve the Q-factor and reduce the bit error rate (BER) of the system, and is more suitable for complex long-distance and high-speed optical transmission scenarios.https://ieeexplore.ieee.org/document/10449366/Coherent optical orthogonal frequency division multiplexing (CO-OFDM) systemdeep learninglow-density parity-check (LDPC) codemultiple impairments compensation |
| spellingShingle | Cong Hu Yuanxiang Chen Ying Han Jianguo Yu Multi-Impairment Compensation for CO-OFDM System Based on Deep Learning and LDPC Code IEEE Photonics Journal Coherent optical orthogonal frequency division multiplexing (CO-OFDM) system deep learning low-density parity-check (LDPC) code multiple impairments compensation |
| title | Multi-Impairment Compensation for CO-OFDM System Based on Deep Learning and LDPC Code |
| title_full | Multi-Impairment Compensation for CO-OFDM System Based on Deep Learning and LDPC Code |
| title_fullStr | Multi-Impairment Compensation for CO-OFDM System Based on Deep Learning and LDPC Code |
| title_full_unstemmed | Multi-Impairment Compensation for CO-OFDM System Based on Deep Learning and LDPC Code |
| title_short | Multi-Impairment Compensation for CO-OFDM System Based on Deep Learning and LDPC Code |
| title_sort | multi impairment compensation for co ofdm system based on deep learning and ldpc code |
| topic | Coherent optical orthogonal frequency division multiplexing (CO-OFDM) system deep learning low-density parity-check (LDPC) code multiple impairments compensation |
| url | https://ieeexplore.ieee.org/document/10449366/ |
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