Machine Learning for QoS Prediction in Vehicular Communication: Challenges and Solution Approaches

no code implementations • 23 Feb 2023 • Alexandros Palaios, Christian L. Vielhaus, Daniel F. Külzer, Cara Watermann, Rodrigo Hernangomez, Sanket Partani, Philipp Geuer, Anton Krause, Raja Sattiraju, Martin Kasparick, Gerhard Fettweis, Frank H. P. Fitzek, Hans D. Schotten, Slawomir Stanczak

This proactive behavior of the network can be leveraged to sustain, for example, a specific quality of service requirement.

Explainable Artificial Intelligence (XAI) Feature Engineering

Berlin V2X: A Machine Learning Dataset from Multiple Vehicles and Radio Access Technologies

1 code implementation • 20 Dec 2022 • Rodrigo Hernangómez, Philipp Geuer, Alexandros Palaios, Daniel Schäufele, Cara Watermann, Khawla Taleb-Bouhemadi, Mohammad Parvini, Anton Krause, Sanket Partani, Christian Vielhaus, Martin Kasparick, Daniel F. Külzer, Friedrich Burmeister, Frank H. P. Fitzek, Hans D. Schotten, Gerhard Fettweis, Sławomir Stańczak

The evolution of wireless communications into 6G and beyond is expected to rely on new machine learning (ML)-based capabilities.

Toward an AI-enabled Connected Industry: AGV Communication and Sensor Measurement Datasets

1 code implementation • 20 Dec 2022 • Rodrigo Hernangómez, Alexandros Palaios, Cara Watermann, Daniel Schäufele, Philipp Geuer, Rafail Ismayilov, Mohammad Parvini, Anton Krause, Martin Kasparick, Thomas Neugebauer, Oscar D. Ramos-Cantor, Hugues Tchouankem, Jose Leon Calvo, Bo Chen, Gerhard Fettweis, Sławomir Stańczak

This paper presents two wireless measurement campaigns in industrial testbeds: industrial Vehicle-to-vehicle (iV2V) and industrial Vehicle-to-infrastructure plus Sensor (iV2I+), together with detailed information about the two captured datasets.