Performance Investigation of Modulation Format Identification in Super-Channel Optical Networks
The problem of automatic modulation format identification (MFI) is one of the main challenges in adaptive optical systems. In this work, we investigate MFI in super-channel optical networks. The investigation is conducted by considering the classification of seven multiplexed channels of 20 Gbaud, e...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Photonics Journal |
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| Online Access: | https://ieeexplore.ieee.org/document/9706292/ |
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| author | Waddah S. Saif Amr M. Ragheb Bernd Nebendahl Tariq Alshawi Mohamed Marey Saleh A. Alshebeili |
| author_facet | Waddah S. Saif Amr M. Ragheb Bernd Nebendahl Tariq Alshawi Mohamed Marey Saleh A. Alshebeili |
| author_sort | Waddah S. Saif |
| collection | DOAJ |
| description | The problem of automatic modulation format identification (MFI) is one of the main challenges in adaptive optical systems. In this work, we investigate MFI in super-channel optical networks. The investigation is conducted by considering the classification of seven multiplexed channels of 20 Gbaud, each with six commonly used modulation formats, including polarization division multiplexing (PDM)-BPSK, PDM-QPSK, and PDM-MQAM with (M = 8, 16, 32, 64). The classification performance is assessed under different values of optical signal-to-noise ratio (OSNR) and in the presence of channel interference, channel chromatic dispersion, phase noise, and <inline-formula><tex-math notation="LaTeX">$1^{st}$</tex-math></inline-formula> polarization mode dispersion (PMD). Furthermore, the effect of fiber nonlinearity on the MFI accuracy is investigated. A well-established machine learning algorithm based on histogram features and a convolutional neural network has been used in this investigation. Results indicate that accurate identification accuracy can be achieved within the OSNR range of practical systems and that the MFI accuracy of side subcarriers outperforms that of middle subcarriers at a fixed value of OSNR. The results also show that the MFI accuracy of PDM-16QAM and PDM-64QAM are affected more by channel interference than the other modulation formats, especially when the ratio of the subcarrier bandwidth to subcarriers spacing is <inline-formula><tex-math notation="LaTeX">$\geq$</tex-math></inline-formula> 1.4. Finally, laboratory experiments have been conducted for validation purposes. The experimental results were found in good agreement with those achieved by simulation. |
| first_indexed | 2024-12-12T07:09:46Z |
| format | Article |
| id | doaj.art-481ab307bd444715b7a08f1544d3184e |
| institution | Directory Open Access Journal |
| issn | 1943-0655 |
| language | English |
| last_indexed | 2024-12-12T07:09:46Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Photonics Journal |
| spelling | doaj.art-481ab307bd444715b7a08f1544d3184e2022-12-22T00:33:39ZengIEEEIEEE Photonics Journal1943-06552022-01-0114211010.1109/JPHOT.2022.31487989706292Performance Investigation of Modulation Format Identification in Super-Channel Optical NetworksWaddah S. Saif0https://orcid.org/0000-0002-8140-8803Amr M. Ragheb1https://orcid.org/0000-0002-4449-0182Bernd Nebendahl2Tariq Alshawi3https://orcid.org/0000-0002-5509-8696Mohamed Marey4https://orcid.org/0000-0002-2105-7239Saleh A. Alshebeili5https://orcid.org/0000-0003-4157-9277Electrical Engineering Department, King Saud University, Riyadh, Saudi ArabiaKACST-TIC in Radio Frequency and Photonics for the e-Society, King Saud University, Riyadh, Saudi ArabiaKeysight Technologies, Boeblingen, GermanyElectrical Engineering Department, King Saud University, Riyadh, Saudi ArabiaSmart Systems Engineering Laboratory and College of Engineering, Prince Sultan University, Riyadh, Saudi ArabiaElectrical Engineering Department, King Saud University, Riyadh, Saudi ArabiaThe problem of automatic modulation format identification (MFI) is one of the main challenges in adaptive optical systems. In this work, we investigate MFI in super-channel optical networks. The investigation is conducted by considering the classification of seven multiplexed channels of 20 Gbaud, each with six commonly used modulation formats, including polarization division multiplexing (PDM)-BPSK, PDM-QPSK, and PDM-MQAM with (M = 8, 16, 32, 64). The classification performance is assessed under different values of optical signal-to-noise ratio (OSNR) and in the presence of channel interference, channel chromatic dispersion, phase noise, and <inline-formula><tex-math notation="LaTeX">$1^{st}$</tex-math></inline-formula> polarization mode dispersion (PMD). Furthermore, the effect of fiber nonlinearity on the MFI accuracy is investigated. A well-established machine learning algorithm based on histogram features and a convolutional neural network has been used in this investigation. Results indicate that accurate identification accuracy can be achieved within the OSNR range of practical systems and that the MFI accuracy of side subcarriers outperforms that of middle subcarriers at a fixed value of OSNR. The results also show that the MFI accuracy of PDM-16QAM and PDM-64QAM are affected more by channel interference than the other modulation formats, especially when the ratio of the subcarrier bandwidth to subcarriers spacing is <inline-formula><tex-math notation="LaTeX">$\geq$</tex-math></inline-formula> 1.4. Finally, laboratory experiments have been conducted for validation purposes. The experimental results were found in good agreement with those achieved by simulation.https://ieeexplore.ieee.org/document/9706292/Machine learningmodulation format identificationsuper-channel optical networks |
| spellingShingle | Waddah S. Saif Amr M. Ragheb Bernd Nebendahl Tariq Alshawi Mohamed Marey Saleh A. Alshebeili Performance Investigation of Modulation Format Identification in Super-Channel Optical Networks IEEE Photonics Journal Machine learning modulation format identification super-channel optical networks |
| title | Performance Investigation of Modulation Format Identification in Super-Channel Optical Networks |
| title_full | Performance Investigation of Modulation Format Identification in Super-Channel Optical Networks |
| title_fullStr | Performance Investigation of Modulation Format Identification in Super-Channel Optical Networks |
| title_full_unstemmed | Performance Investigation of Modulation Format Identification in Super-Channel Optical Networks |
| title_short | Performance Investigation of Modulation Format Identification in Super-Channel Optical Networks |
| title_sort | performance investigation of modulation format identification in super channel optical networks |
| topic | Machine learning modulation format identification super-channel optical networks |
| url | https://ieeexplore.ieee.org/document/9706292/ |
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