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Title

Federated Learning for 5G Radio Spectrum Sensing

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

2022

Published in

Sensors

Journal year: 2022 | Journal volume: vol. 22 | Journal number: iss. 1

Article type

scientific article

Publication language

english

Keywords
EN
  • spectrum sensing
  • machine learning
  • 5G
  • LTE
  • federated learning
  • convolutional neural network
  • deep learning
  • clustering
  • cognitive radio
Abstract

EN Spectrum sensing (SS) is an important tool in finding new opportunities for spectrum sharing. The users, called Secondary Users (SU), who do not have a license to transmit without hindrance, need to employ SS in order to detect and use the spectrum without interfering with the licensed users’ (primary users’ (PUs’)) transmission. Deep learning (DL) has proven to be a good choice as an intelligent SS algorithm that considers radio environmental factors in the decision-making process. It is impossible though for SU to collect the required data and train complex DL models. In this paper, we propose to employ a Federated Learning (FL) algorithm in order to distribute data collection and model training processes over many devices. The proposed method categorizes FL devices into groups by their mean Signal-to-Noise ratio (SNR) and creates a common DL model for each group in the iterative process. The results show that detection accuracy obtained via the FL algorithm is similar to detection accuracy obtained by employing several DL models, namely convolutional neural networks (CNNs), specialized in spectrum detection for a PU signal with a given mean SNR value. At the same time, the main goal of simplification of the SS process in the network is achieved.

Pages (from - to)

1 - 15

DOI

10.3390/s22010198

URL

https://www.mdpi.com/1424-8220/22/1/198/htm

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

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Access level to full text

public

Points of MNiSW / journal

100.0

Impact Factor

3.576 [List 2020]

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