Search Results for author: Cornelius Hellge

Found 6 papers, 2 papers with code

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

no code implementations17 Feb 2022 Barış Göktepe, Cornelius Hellge, Thomas Schierl, Slawomir Stanczak

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.

BIG-bench Machine Learning Denoising

Open GOP Resolution Switching in HTTP Adaptive Streaming with VVC

no code implementations11 Mar 2021 Robert Skupin, Christian Bartnik, Adam Wieckowski, Yago Sanchez, Benjamin Bross, Cornelius Hellge, Thomas Schierl

The user experience in adaptive HTTP streaming relies on offering bitrate ladders with suitable operation points for all users and typically involves multiple resolutions.

Feedback Prediction for Proactive HARQ in the Context of Industrial Internet of Things

no code implementations14 Sep 2020 Baris Göktepe, Tatiana Rykova, Thomas Fehrenbach, Thomas Schierl, Cornelius Hellge

Furthermore, we demonstrate that the proposed protocol clearly outperforms the classical proactive HARQ in all scenarios when taking a processing delay reduction due to the less complex prediction approach into account, achieving an energy efficiency gain in the range of 11% up to 15% for very stringent latency budgets of 1 ms at $10^{-2}$ BLER and from 4% up to 7. 5% for less stringent latency budgets of 2 ms at $10^{-3}$ BLER.

HTML5 MSE Playback of MPEG 360 VR Tiled Streaming

1 code implementation7 Mar 2019 Dimitri Podborski, Jangwoo Son, Gurdeep Singh Bhullar, Cornelius Hellge, Thomas Schierl

In this demo paper we describe how we implemented the most advanced media profile from OMAF: HEVC-based viewport-dependent OMAF video profile with multi-resolution HEVC-tiles, using only JavaScript.


Multi-Kernel Prediction Networks for Denoising of Burst Images

2 code implementations5 Feb 2019 Talmaj Marinč, Vignesh Srinivasan, Serhan Gül, Cornelius Hellge, Wojciech Samek

The advantages of our method are two fold: (a) the different sized kernels help in extracting different information from the image which results in better reconstruction and (b) kernel fusion assures retaining of the extracted information while maintaining computational efficiency.

Image Denoising

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

no code implementations27 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

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