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’ (prima...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/1/198 |
| _version_ | 1797497599244107776 |
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| author | Małgorzata Wasilewska Hanna Bogucka Adrian Kliks |
| author_facet | Małgorzata Wasilewska Hanna Bogucka Adrian Kliks |
| author_sort | Małgorzata Wasilewska |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T03:21:32Z |
| format | Article |
| id | doaj.art-d5c3ed28eeda4433b542b70b2fd14286 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T03:21:32Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-d5c3ed28eeda4433b542b70b2fd142862023-11-23T12:18:27ZengMDPI AGSensors1424-82202021-12-0122119810.3390/s22010198Federated Learning for 5G Radio Spectrum SensingMałgorzata Wasilewska0Hanna Bogucka1Adrian Kliks2Institute of Radiocommunications, Poznan University of Technology, 61-131 Poznań, PolandInstitute of Radiocommunications, Poznan University of Technology, 61-131 Poznań, PolandInstitute of Radiocommunications, Poznan University of Technology, 61-131 Poznań, PolandSpectrum 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.https://www.mdpi.com/1424-8220/22/1/198spectrum sensingmachine learning5GLTEfederated learningconvolutional neural network |
| spellingShingle | Małgorzata Wasilewska Hanna Bogucka Adrian Kliks Federated Learning for 5G Radio Spectrum Sensing Sensors spectrum sensing machine learning 5G LTE federated learning convolutional neural network |
| title | Federated Learning for 5G Radio Spectrum Sensing |
| title_full | Federated Learning for 5G Radio Spectrum Sensing |
| title_fullStr | Federated Learning for 5G Radio Spectrum Sensing |
| title_full_unstemmed | Federated Learning for 5G Radio Spectrum Sensing |
| title_short | Federated Learning for 5G Radio Spectrum Sensing |
| title_sort | federated learning for 5g radio spectrum sensing |
| topic | spectrum sensing machine learning 5G LTE federated learning convolutional neural network |
| url | https://www.mdpi.com/1424-8220/22/1/198 |
| work_keys_str_mv | AT małgorzatawasilewska federatedlearningfor5gradiospectrumsensing AT hannabogucka federatedlearningfor5gradiospectrumsensing AT adriankliks federatedlearningfor5gradiospectrumsensing |