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Artificial intelligence in RAN – a software framework for AI-driven RAN automation
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science.ORCID iD: 0000-0003-1558-4670
KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Software and Computer systems, SCS.
2020 (English)In: Ericsson Technology Review, ISSN 0014-0171Article in journal (Other (popular science, discussion, etc.)) Published
Abstract [en]

Artificial intelligence and its subfield machine learning offer well-established techniques for solving historically difficult multi-parameterization problems. Used correctly, these techniques have tremendous potential to overcome complex cross-domain automation challenges in radio networks.

Our ongoing research reveals that an integrated framework of software enablers will be essential to success.

Place, publisher, year, edition, pages
2020.
National Category
Computer Systems Telecommunications
Identifiers
URN: urn:nbn:se:kth:diva-296244OAI: oai:DiVA.org:kth-296244DiVA, id: diva2:1559085
Note

QC 20220322

Available from: 2021-06-01 Created: 2021-06-01 Last updated: 2023-03-17Bibliographically approved

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ETR-AI(472 kB)429 downloads
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File name FULLTEXT01.pdfFile size 472 kBChecksum SHA-512
4862a3afc1cbdad0f8e3ce861b53c2e0216dcbae7d11b3c2ead56cb34760c144805afec902682fe85e48b944407ee71dfd98fe89281270f6a3b35d085d2ad3ee
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Corcoran, DiarmuidErmedahl, Andreas

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
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  • asciidoc
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