首页|Researchers from Fraunhofer Heinrich Hertz Institute Report on Findings in Machine Learning (Distributed Machine-learning for Early Harq Feedback Prediction In Cloud Rans)
Researchers from Fraunhofer Heinrich Hertz Institute Report on Findings in Machine Learning (Distributed Machine-learning for Early Harq Feedback Prediction In Cloud Rans)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
IEEE
A new study on Machine Learning is now available. According to news reporting originating in Berlin, Germany, by NewsRx journalists, research stated, "In this work, we propose novel HARQ prediction schemes for Cloud RANs (C-RANs) that use feedback over a rate-limited feedback channel (2 - 6 bits) from the Remote Radio Heads (RRHs) to predict at the User Equipment (UE) the decoding outcome at the BaseBand Unit (BBU) ahead of actual decoding. In particular, we propose a Dual Autoencoding 2-Stage Gaussian Mixture Model (DA2SGMM) that is trained in an end-to-end fashion over the whole C-RAN setup." Financial support for this research came from Federal Ministry of Education and Research of Germany in the Programme of "Souvern. Digital. Vernetzt." Joint Project 6G-RIC.
BerlinGermanyEuropeCyborgsEmerging TechnologiesMachine LearningFraunhofer Heinrich Hertz Institute
NETL
NSTL
IEEE
