34 citations · 84 across the 5 of their papers we have counts for
5 papers
QoS prediction in radio vehicular environments via prior user information
Noor Ul Ain, Rodrigo Hernangómez, Alexandros Palaios +2
Reliable wireless communications play an important role in the automotive industry as it helps to enhance current use cases and enable new ones such as connected autonomous driving…
Machine Learning for QoS Prediction in Vehicular Communication: Challenges and Solution Approaches
Alexandros Palaios, Christian L. Vielhaus, Daniel F. Külzer +11
As cellular networks evolve towards the 6th generation, machine learning is seen as a key enabling technology to improve the capabilities of the network. Machine learning provides…
Toward an AI-enabled Connected Industry: AGV Communication and Sensor Measurement Datasets
Rodrigo Hernangómez, Alexandros Palaios, Cara Watermann +13
This paper presents two wireless measurement campaigns in industrial testbeds: industrial Vehicle-to-vehicle (iV2V) and industrial Vehicle-to-infrastructure plus Sensor (iV2I+), to…
Berlin V2X: A Machine Learning Dataset from Multiple Vehicles and Radio Access Technologies
Rodrigo Hernangómez, Philipp Geuer, Alexandros Palaios +14
The evolution of wireless communications into 6G and beyond is expected to rely on new machine learning (ML)-based capabilities. These can enable proactive decisions and actions fr…
Mobility, traffic and radio channel prediction: 5G and beyond applications
Henrik Rydén, Alex Palaios, László Hévizi +3
Machine learning (ML) is an important component for enabling automation in Radio Access Networks (RANs). The work on applying ML for RAN has been under development for many years a…