Please use this identifier to cite or link to this item:
http://hdl.handle.net/10071/26539
Author(s): | Esteves, S. Rebola, J. Santana, P. |
Date: | 2022 |
Title: | Deep learning for BER prediction in optical connections impaired by inter-core crosstalk |
Book title/volume: | 2022 13th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP) |
Pages: | 440 - 445 |
Event title: | 13th International Symposium on Communication Systems, Networks and Digital Signal Processing, CSNDSP 2022 |
Reference: | Esteves, S., Rebola, J. & Santana, P. (2022).Deep learning for BER prediction in optical connections impaired by inter-core crosstalk. In 13th International Symposium on Communication Systems, Networks and Digital Signal Processing, CSNDSP 2022 (pp. 440-445). IEEE. https://doi.org/10.1109/CSNDSP54353.2022.9908035 |
ISBN: | 978-1-6654-1044-1 |
DOI (Digital Object Identifier): | 10.1109/CSNDSP54353.2022.9908035 |
Abstract: | Four-level pulse amplitude modulation (PAM4) signals transmission in short-haul intensity modulation-direct detection datacenters connections supported by homogeneous weakly-coupled multicore fibers is seen as a promising technology to meet the future challenge of providing enough bandwidth and achieve high data capacity in datacenter links. However, in multicore fibers, inter-core crosstalk (ICXT) limits significantly the performance of such short-reach connections by causing large bit error rate (BER) fluctuations. In this work, a convolutional neural network (CNN) is proposed for eye-pattern analysis and BER prediction in PAM4 inter-datacenter optical connections impaired by ICXT, with the aim of optical performance monitoring. The performance of the CNN is assessed by estimation of the root mean square error (RMSE) using a synthetic dataset created with Monte Carlo simulation. Considering PAM4 interdatacenter connections with one interfering core and for different skew-symbol rate products, extinction ratios and crosstalk levels, the obtained results show that the implemented CNN is able to predict the BER without surpassing a RMSE limit of 0.1. |
Peerreviewed: | yes |
Access type: | Open Access |
Appears in Collections: | IT-CRI - Comunicações a conferências internacionais |
Files in This Item:
File | Size | Format | |
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conferenceobject_89851.pdf | 1,47 MB | Adobe PDF | View/Open |
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