Change search
CiteExportLink to record
Permanent link

Direct link
Cite
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
  • text
  • asciidoc
  • rtf
Federated Learning-Based Spectrum Occupancy Detection
Institute of Radiocommunications, Poznan University of Technology, 60-965 Poznan, Poland.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems. Institute of Radiocommunications, Poznan University of Technology, 60-965 Poznan, Poland.ORCID iD: 0000-0001-6766-7836
2023 (English)In: Sensors, E-ISSN 1424-8220, Vol. 23, no 14, article id 6436Article in journal (Refereed) Published
Abstract [en]

Dynamic access to the spectrum is essential for radiocommunication and its limited spectrum resources. The key element of dynamic spectrum access systems is most often effective spectrum occupancy detection. In many cases, machine learning algorithms improve this detection’s effectiveness. Given the recent trend of using federated learning, we present a federated learning algorithm for distributed spectrum occupancy detection. This idea improves overall spectrum-detection effectiveness, simultaneously keeping a low amount of data that needs to be exchanged between sensors. The proposed solution achieves a higher accuracy score than separate and autonomous models used without federated learning. Additionally, the proposed solution shows some sort of resistance to faulty sensors encountered in the system. The results of the work presented in the article are based on actual signal samples collected in the laboratory. The proposed algorithm is effective (in terms of spectrum occupancy detection and amount of exchanged data), especially in the context of a set of sensors in which there are faulty sensors.

Place, publisher, year, edition, pages
MDPI, 2023. Vol. 23, no 14, article id 6436
Keywords [en]
federated learning, machine learning, spectrum occupancy detection
National Category
Computer Sciences
Research subject
Signal Processing
Identifiers
URN: urn:nbn:se:ltu:diva-99209DOI: 10.3390/s23146436ISI: 001038807300001PubMedID: 37514730Scopus ID: 2-s2.0-85166193718OAI: oai:DiVA.org:ltu-99209DiVA, id: diva2:1782704
Note

Godkänd;2023;Nivå 0;2023-08-15 (hanlid);

Funder: National Science Centre in Poland (2021/41/N/ST7/01298)

Available from: 2023-07-16 Created: 2023-07-16 Last updated: 2023-08-21Bibliographically approved

Open Access in DiVA

fulltext(641 kB)88 downloads
File information
File name FULLTEXT01.pdfFile size 641 kBChecksum SHA-512
42fc3e4d53d12a0f5c19b74b27da847b43493878185c6693789d98ecf5f8697c65bf05b33340286d76cb5d3035a640cdb9c4f106d003e9c9bc929d490dd8cde0
Type fulltextMimetype application/pdf

Other links

Publisher's full textPubMedScopus

Authority records

Kliks, Adrian

Search in DiVA

By author/editor
Kliks, Adrian
By organisation
Signals and Systems
In the same journal
Sensors
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar
Total: 88 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
pubmed
urn-nbn

Altmetric score

doi
pubmed
urn-nbn
Total: 73 hits
CiteExportLink to record
Permanent link

Direct link
Cite
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
  • text
  • asciidoc
  • rtf