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Data Sets For Machine Learning In Wireless Communications And Networks
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Network and Systems Engineering.ORCID iD: 0000-0001-9810-3478
Tech Univ Dresden, Dresden, Germany..
Kings Coll London, London, England..
Ericsson, Toronto, ON, Canada.;AI Enabled Next Generat Wireless Networks, Toronto, ON, Canada.;Univ Ottawa, Ottawa, ON, Canada..
2023 (English)In: IEEE Communications Magazine, ISSN 0163-6804, E-ISSN 1558-1896, Vol. 61, no 9, p. 80-81Article in journal, Editorial material (Other academic) Published
Abstract [en]

The articles in this special section focus on the role of data sets for the evolution of the telecommunication industry in the 5G and 6G era. In 5G and 6G, many new services are emerging to accommodate various Internet of Things (IoT) devices, going beyond the traditional provisions of mobile phones and internet connectivity. Examples of these services include extended reality devices, sensors, or ground and aerial robots. The deployment of these advanced services, however, poses challenges for the wireless network, particularly in its ability to support ubiquitous connections while meeting diverse quality-of-service (QoS) requirements. Despite the remarkable success of model-based design and analysis in wireless networks, it has become evident that these conventional approaches may not be fully adequate to address the dynamic and diverse QoS requirements posed by the emerging IoT landscape. The heterogeneity of devices and services necessitates a more adaptive and intelligent approach to ensure efficient network performance.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2023. Vol. 61, no 9, p. 80-81
Keywords [en]
Special issues and sections, 5G mobile communication, Data models, Machine learning, Wireless communication, Internet, 6G mobile communication
National Category
Telecommunications
Identifiers
URN: urn:nbn:se:kth:diva-339327DOI: 10.1109/MCOM.2023.10268845ISI: 001080991100005Scopus ID: 2-s2.0-85174492111OAI: oai:DiVA.org:kth-339327DiVA, id: diva2:1811165
Note

QC 20231110

Available from: 2023-11-10 Created: 2023-11-10 Last updated: 2023-11-10Bibliographically approved

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Fischione, Carlo

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