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Deep learning for BER prediction in optical connections impaired by inter-core crosstalk

Esteves, S. ; Rebola, J. ; Santana, P.

Deep learning for BER prediction in optical connections impaired by inter-core crosstalk, Proc 13th IEEE/IET International Symposium on Communication Systems, Networks and Digital Signal Processing CSNDSP, Oporto, Portugal, Vol. , pp. - , July, 2022.

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To meet the required future challenge of providing
enough bandwidth to achieve high data traffic rates in datacenter
links, four-level pulse amplitude modulation (PAM4)
signals transmission in short-haul intensity modulation-direct
detection datacenters connections supported by homogeneous
weakly-coupled multicore fibers has been proposed in previous
work. However, in such fibers, a physical effect known as intercore
crosstalk (ICXT) limits significantly the performance of
short-reach connections by causing large bit error rate (BER)
fluctuations. In this paper, 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 was assessed by estimation of the root mean square error
(RMSE) using a synthetic dataset created with Monte Carlo
simulation. Considering PAM4 interdatacenter links 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. The CNN trained with different
optical parameters obtained the best performance in terms of
generalization comparing to the CNN trained with specific optical