activity
20172023
most citedWhite Paper on Critical and Massive Machine Type Communication Towards 6G

164 citations · 288 across the 23 of their papers we have counts for

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41 papers · 1 filter

cs.NI2022

System Modeling and Performance Evaluation of Predictive QoS for Future Tele-Operated Driving

Hendrik Schippers, Cedrik Schüler, Benjamin Sliwa +1

Future Tele-operated Driving (ToD) applications place challenging Quality of Service (QoS) demands on existing mobile communication networks that are of highly important to comply…

cs.NI20211 cited

Machine Learning-Enabled Data Rate Prediction for 5G NSA Vehicle-to-Cloud Communications

Benjamin Sliwa, Hendrik Schippers, Christian Wietfeld

In order to satisfy the ever-growing Quality of Service (QoS) requirements of innovative services, cellular communication networks are constantly evolving. Recently, the 5G NonStan…

cs.NI2021

Modeling and Simulation of Reconfigurable Intelligent Surfaces for Hybrid Aerial and Ground-based Vehicular Communications

Karsten Heimann, Benjamin Sliwa, Manuel Patchou +1

The requirements of vehicular communications grow with increasing level of automated driving and future applications of intelligent transportation systems (ITS). Beside the ever-in…

cs.NI2021

Pushing the Limits: Resilience Testing for Mission-Critical Machine-Type Communication

Christian Arendt, Manuel Patchou, Stefan Böcker +2

Interdisciplinary application fields, such as automotive, industrial applications or field robotics show an increasing need for reliable and resilient wireless communication even u…

cs.NI2021

Towards Machine Learning-Enabled Context Adaption for Reliable Aerial Mesh Routing

Cedrik Schüler, Benjamin Sliwa, Christian Wietfeld

In this paper, we present Context-Adaptive PARRoT (CA-PARRoT) as an extension of our previous work Predictive Ad-hoc Routing fueled by Reinforcement learning and Trajectory knowled…

cs.NI2021

Client-Based Intelligence for Resource Efficient Vehicular Big Data Transfer in Future 6G Network

Benjamin Sliwa, Rick Adam, Christian Wietfeld

Vehicular big data is anticipated to become the "new oil" of the automotive industry which fuels the development of novel crowdsensing-enabled services. However, the tremendous amo…