all AI news
Advanced Equalization in 112 Gb/s Upstream PON Using a Novel Fourier Convolution-based Network
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
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
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
Software Engineer for AI Training Data (School Specific)
@ G2i Inc | Remote
Software Engineer for AI Training Data (Python)
@ G2i Inc | Remote
Software Engineer for AI Training Data (Tier 2)
@ G2i Inc | Remote
Data Engineer
@ Lemon.io | Remote: Europe, LATAM, Canada, UK, Asia, Oceania
Artificial Intelligence – Bioinformatic Expert
@ University of Texas Medical Branch | Galveston, TX
Lead Developer (AI)
@ Cere Network | San Francisco, US