May 7, 2024, 4:42 a.m. | Chen Shao, Elias Giacoumidis, Patrick Matalla, Jialei Li, Shi Li, Sebastian Randel, Andre Richter, Michael Faerber, Tobias Kaefer

cs.LG updates on arXiv.org arxiv.org

arXiv:2405.02609v1 Announce Type: new
Abstract: We experimentally demonstrate a novel, low-complexity Fourier Convolution-based Network (FConvNet) based equalizer for 112 Gb/s upstream PAM4-PON. At a BER of 0.005, FConvNet enhances the receiver sensitivity by 2 and 1 dB compared to a 51-tap Sato equalizer and benchmark machine learning algorithms respectively.

abstract advanced arxiv benchmark complexity convolution cs.lg equalization fourier low machine machine learning network novel sensitivity type

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