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Improving the Bootstrap of Blind Equalizers with Variational Autoencoders

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arxiv 2301.06576 v1 pith:YMA7MQTC submitted 2023-01-16 eess.SP cs.ITcs.LGmath.IT

Vincent Lauinger , Fred Buchali , Laurent Schmalen This is my paper · ORCID
classification eess.SPcs.ITcs.LGmath.IT
keywords equalizersblindvariationaladvantagesalgorithmsanalyzeautoencoderautoencoders
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We evaluate the start-up of blind equalizers at critical working points, analyze the advantages and obstacles of commonly-used algorithms, and demonstrate how the recently-proposed variational autoencoder (VAE) based equalizers can improve bootstrapping.

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Recent Advances on Machine Learning-aided DSP for Short-reach and Long-haul Optical Communications

    eess.SP 2024-11 unverdicted novelty 1.0 of 10

    A mini-review of the authors' prior work on VAE-based blind equalization, FPGA-implemented CNN equalizers, and spiking neural network equalizers for optical communications.

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