Distributed Machine-Learning for Early HARQ Feedback Prediction in Cloud RANs

no code implementations • 17 Feb 2022 • Barış Göktepe, Cornelius Hellge, Thomas Schierl, Slawomir Stanczak

Compared to regular HARQ, the DA2SGMM reduces the maximum transmission latency by more than 72. 4 %, while maintaining more than 75 % of the throughput in the no-blockage scenario.

BIG-bench Machine Learning Denoising

Enhanced Machine Learning Techniques for Early HARQ Feedback Prediction in 5G

no code implementations • 27 Jul 2018 • Nils Strodthoff, Barış Göktepe, Thomas Schierl, Cornelius Hellge, Wojciech Samek

We investigate Early Hybrid Automatic Repeat reQuest (E-HARQ) feedback schemes enhanced by machine learning techniques as a path towards ultra-reliable and low-latency communication (URLLC).

BIG-bench Machine Learning General Classification +1