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Deep Learning Approach to Estimate Interchannel Interference in gridless Nyquist-WDM Systems

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Abstract

We propose an ensemble of deep learning models to estimate the level of interchannel interference in a 3×32-Gbaud 16-QAM gridless Nyquist-WDM system. Results showed mean-absolute-error up to 0.406GHz for transmission over 270km including overlapped channels.

© 2022 The Author(s)

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