Machine Learning for QoS Prediction in Vehicular Communication: Challenges and Solution Approaches-Reference-Cited by-同舟云学术

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

Published:2023 Issue: Volume:11 Page:92459-92477
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Palaios Alexandros¹^ORCID,Vielhaus Christian L.²^ORCID,Külzer Daniel F.³^ORCID,Watermann Cara¹^ORCID,Hernangómez Rodrigo⁴^ORCID,Partani Sanket⁵^ORCID,Geuer Philipp¹^ORCID,Krause Anton⁶^ORCID,Sattiraju Raja⁵^ORCID,Kasparick Martin⁴^ORCID,Fettweis Gerhard P.⁶^ORCID,Fitzek Frank H. P.²^ORCID,Schotten Hans D.⁵^ORCID,Stańczak Slawomir⁴^ORCID

Affiliation:

1. Ericsson Research, Herzogenrath, Germany

2. Deutsche Telecom Chair of Communication Networks, Technical University of Dresden (TU Dresden), Dresden, Germany

3. BMW Group, Munich, Germany

4. Fraunhofer Heinrich Hertz Institute, Berlin, Germany

5. Wireless Communications and Navigation, Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau, Kaiserslautern, Germany

6. Vodafone Chair Mobile Communications Systems, Technical University of Dresden (TU Dresden), Dresden, Germany

Funder

Federal Ministry of Education and Research (BMBF) of the Federal Republic of Germany

AI4Mobile Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10213404.pdf?arnumber=10213404

Reference66 articles.

1. Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)

2. A Systematic Study of Online Class Imbalance Learning With Concept Drift

3. Incremental Learning of Concept Drift from Streaming Imbalanced Data

4. Feasibility Study of Teleoperated Vehicles Using Multi-Operator LTE Connection

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