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Article

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Title

Federated Learning-Based Spectrum Occupancy Detection

Authors

[ 1 ] Instytut Radiokomunikacji, Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ P ] employee

Scientific discipline (Law 2.0)

[2.3] Information and communication technology

Year of publication

2023

Published in

Sensors

Journal year: 2023 | Journal volume: vol. 23 | Journal number: iss. 14

Article type

scientific article

Publication language

english

Keywords
EN
  • federated learning
  • machine learning
  • spectrum occupancy detection
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.

Pages (from - to)

6436-1 - 6436-14

DOI

10.3390/s23146436

URL

https://www.mdpi.com/1424-8220/23/14/6436

Comments

article number: 6436

License type

CC BY (attribution alone)

Open Access Mode

open journal

Open Access Text Version

final published version

Date of Open Access to the publication

at the time of publication

Full text of article

Download file

Access level to full text

public

Ministry points / journal

100

Impact Factor

3,9 [List 2022]

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