Federated Learning for 5G Radio Spectrum Sensing

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' (...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2021-12, Vol.22 (1), p.198
Main Authors: Wasilewska, Małgorzata, Bogucka, Hanna, Kliks, Adrian
Format: Article
Language:English
Subjects:
Algorithms
Communication
convolutional neural network
Decision making
Deep learning
federated learning
Licenses
LTE
machine learning
Neural networks
Neural Networks, Computer
Radio spectra
Research Design
Signal to noise ratio
Spectrum allocation
spectrum sensing
Wireless networks
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Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s22010198