Identification of advanced optical modulation format and estimation of signal-to-noise-ratio based on parallel-twin convolutional neural network

• Autorzy: Dong Xiaowei , Yu Zhihui

Identyfikatory

DOI

10.37190/oa230209

• Języki publikacji: EN

Abstrakty

EN

In this paper, we design a parallel-twin convolutional neural network (PT-CNN) deep learning model and use the signal constellation diagram to realize the identification of six advanced optical modulation formats (QPSK, 4QAM, 8PSK, 8QAM, 16PSK, 16QAM) and signal-to-noise-ratio (SNR) estimation. The influence of PT-CNN with different layers and kernel sizes is investigated and the optimal network model is chosen. Simulation results demonstrate that the proposed method has the advantages of not requiring manual feature extraction, having the ability to clearly distinguish the six modulation formats with 100% accuracy when SNR of the received signal sequences is higher than 12 dB. In addition, the high-accurate SNR estimation is realized simultaneously without increasing additional system complexity.

Słowa kluczowe

EN

deep learning; PT-CNN; parallel-twin convolutional neural network; constellation diagram; modulation format identification; SNR estimation

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Optica Applicata
• Rocznik: 2023
• Tom: Vol. 53, nr 2
• Strony: 281--289
• Opis fizyczny: Bibliogr. 13 poz., rys.

Twórcy

autor

Dong Xiaowei

• College of Information Engineering, North China University of Technology, Beijing 100144, China

autor

Yu Zhihui

• College of Information Engineering, North China University of Technology, Beijing 100144, China

Bibliografia

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• [10] SAIF W.S., RAGHEB A.M., NEBENDAHL B., ALSHAWI T., MAREY M., ALSHEBEILI S.A., Performance investigation of modulation format identification in super-channel optical networks, IEEE Photonics Journal 14(2), 2022: 8514910. https://doi.org/10.1109/JPHOT.2022.3148798
• [11] WANG D., ZHANG M., LI Z., LI J., FU M., CUI Y., CHEN X., Modulation format recognition and OSNR estimation using CNN-based deep learning, IEEE Photonics Technology Letters 29(19), 2017: 1667-1670. https://doi.org/10.1109/LPT.2017.2742553
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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).