A new scientific article is accepted in IEEE Journal of Selected topics in Quantum Electronics, with contributions from the KTH Royal Institute of Technology School of Engineering
Sciences, RISE Research Institutes of Sweden AB, Institute of Telecommunications, Chalmers University of Technology and Zhejiang University,
What is the paper about?
Recently proposed deep learning techniques for
EVM estimation extend the functionality of conventional optical
performance monitoring (OPM). In this article, we evaluate the
tolerance of our developed EVM estimation scheme against
various impairments in coherent optical systems. In particular, we
analyze the signal quality monitoring capabilities in the presence
of residual in-phase/quadrature (IQ) imbalance, fiber nonlinearity,
and laser phase noise. We use feedforward neural networks
(FFNNs) to extract the EVM information from amplitude
histograms of 100 symbols per IQ cluster signal sequence captured
before carrier phase recovery. We perform simulations of the
considered impairments, along with an experimental investigation
of the impact of laser phase noise.
What are the results?
We achieve a mean absolute estimation error below 0.15%, with short signals consisting of only 100 symbols per IQ cluster. Considering the estimation accuracy, the implementation complexity, and the potential energy savings, the proposed CNN- and FFNN-based schemes can be used to perform time-sensitive and accurate EVM estimation for mQAM signal quality monitoring purposes.
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