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

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

Creator:

Dong, Xiaowei ; Yu, Zhihui

Contributor:

Urbańczyk, Wacław. Redakcja

Description:

Optica Applicata, Vol. 53, 2023, nr 2, s. 281-289 ; Optica Applicata is an international journal, published in a non-periodical form in the years 1971-1973 and quarterly since 1973. From the beginning of the year 2008, Optica Applicata is an Open Access journal available online via the Internet, with free access to the full text of articles serving the best interests of the scientific community. The journal is abstracted and indexed in: Chemical Abstracts, Compendex, Current Contents, Inspec, Referativnyj Zhurnal, SCI Expanded, Scopus, Ulrich’s Periodicals Directory ; click here to follow the link

Abstrakt:

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.

Publisher:

Oficyna Wydawnicza Politechniki Wrocławskiej

Place of publication:

Date:

Resource Type:

Resource Identifier:

doi:10.37190/oa2302

Source:

<sygn. PWr A3481II> ; click here to follow the link ; click here to follow the link

Language:

Relation:

Optica Applicata ; Optica Applicata, Vol. 53, 2023 ; Optica Applicata, Vol. 53, 2023, nr 2 ; Politechnika Wrocławska. Wydział Podstawowych Problemów Techniki

Rights:

Wszystkie prawa zastrzeżone (Copyright)

Access Rights:

Dla wszystkich w zakresie dozwolonego użytku

Location:

Politechnika Wrocławska

Group publication title:

Optica Applicata

Subject and keywords:

optyka deep learning PT-CNN constellation diagram modulation format identification SNR estimation

Object collections:

Last modified:

Feb 12, 2025

All available object's versions:

https://dbc.wroc.pl./publication/170160

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Optica Applicata

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