liu.seSearch for publications in DiVA
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • 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
  • text
  • asciidoc
  • rtf
Transferring Knowledge for Tilt-Dependent Radio Map Prediction
Politecn Milan, Italy.
Politecn Milan, Italy.
Politecn Milan, Italy.
Nokia Bell Labs, Germany.
Show others and affiliations
2018 (English)In: 2018 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE (WCNC), IEEE , 2018Conference paper, Published paper (Refereed)
Abstract [en]

Fifth generation wireless networks (5G) will face key challenges caused by diverse patterns of traffic demands and massive deployment of heterogeneous access points. In order to handle this complexity, machine learning techniques are expected to play a major role. However, due to the large space of parameters related to network optimization, collecting data to train models for all possible network configurations can be prohibitive. In this paper, we analyze the possibility of performing a knowledge transfer, in which a machine learning model trained on a particular network configuration is used to predict a quantity of interest in a new, unknown setting. We focus on the tilt-dependent received signal strength maps as quantities of interest and we analyze two cases where the knowledge acquired for a particular antenna tilt setting is transferred to (i) a different tilt configuration of the same antenna or (ii) a different antenna with the same tilt configuration. Promising results supporting knowledge transfer are obtained through extensive experiments conducted using different machine learning models on a real dataset.

Place, publisher, year, edition, pages
IEEE , 2018.
Series
IEEE Wireless Communications and Networking Conference, ISSN 1525-3511
Keywords [en]
Radio map prediction; antenna tilt; machine learning; knowledge transfer
National Category
Telecommunications
Identifiers
URN: urn:nbn:se:liu:diva-149757DOI: 10.1109/WCNC.2018.8377359ISI: 000435542402080ISBN: 978-1-5386-1734-2 (print)OAI: oai:DiVA.org:liu-149757DiVA, id: diva2:1234342
Conference
IEEE Wireless Communications and Networking Conference (WCNC)
Note

Funding Agencies|European Union [643002]

Available from: 2018-07-24 Created: 2018-07-24 Last updated: 2018-07-24

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full text

Search in DiVA

By author/editor
Tatino, Cristian
By organisation
Communications and Transport SystemsFaculty of Science & Engineering
Telecommunications

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 7 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • 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
  • text
  • asciidoc
  • rtf