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

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

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 a methodology for predictive systems, which, in turn, can make networks become proactive. This proactive behavior of the...

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Bibliographic Details
Main Authors: Alexandros Palaios, Christian L. Vielhaus, Daniel F. Kulzer, Cara Watermann, Rodrigo Hernangomez, Sanket Partani, Philipp Geuer, Anton Krause, Raja Sattiraju, Martin Kasparick, Gerhard P. Fettweis, Frank H. P. Fitzek, Hans D. Schotten, Slawomir Stanczak
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Access
Subjects:
Intelligent transportation systems
machine learning
quality of service
throughput prediction
vehicular communication
wireless networks
Online Access:https://ieeexplore.ieee.org/document/10213404/

Internet

https://ieeexplore.ieee.org/document/10213404/

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