activity
20182026
most citedEnhanced Machine Learning Techniques for Early HARQ Feedback Prediction in 5G

71 citations · 269 across the 13 of their papers we have counts for

collaborators

15 papers

cs.CV2026

GaussianPOP: Principled Simplification Framework for Compact 3D Gaussian Splatting via Error Quantification

Soonbin Lee, Yeong-Gyu Kim, Simon Sasse +5

Existing 3D Gaussian Splatting simplification methods commonly use importance scores, such as blending weights or sensitivity, to identify redundant Gaussians. However, these score…

eess.SP2025

3GPP NR V2X Mode 2d: Analysis of Distributed Scheduling for Groupcast using ns-3 5G LENA Simulator

Thomas Fehrenbach, Luis Omar Ortiz Abrego, Cornelius Hellge +2

Vehicle-to-everything (V2X) communication is a key technology for enabling intelligent transportation systems (ITS) that can improve road safety, traffic efficiency, and environmen…

cs.CV2025

Compression of 3D Gaussian Splatting with Optimized Feature Planes and Standard Video Codecs

Soonbin Lee, Fangwen Shu, Yago Sanchez +2

3D Gaussian Splatting is a recognized method for 3D scene representation, known for its high rendering quality and speed. However, its substantial data requirements present challen…

cs.CV2023

ECRF: Entropy-Constrained Neural Radiance Fields Compression with Frequency Domain Optimization

Soonbin Lee, Fangwen Shu, Yago Sanchez +2

Explicit feature-grid based NeRF models have shown promising results in terms of rendering quality and significant speed-up in training. However, these methods often require a sign…

cs.IT2023

On the Limits of HARQ Prediction for Short Deterministic Codes with Error Detection in Memoryless Channels (Extended Version with Proofs)

Barış Göktepe, Cornelius Hellge, Tatiana Rykova +2

We provide a mathematical framework to analyze the limits of Hybrid Automatic Repeat reQuest (HARQ) and derive analytical expressions for the most powerful test for estimating the…

eess.SP2022★ 12 cited

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

Barış Göktepe, Cornelius Hellge, Thomas Schierl +1

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…