IEICE TRANSACTIONS on Communications
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A Survey on Spectrum Sensing and Learning Technologies for 6G
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Summary :
Cognitive radio provides a feasible solution for alleviating the lack of spectrum resources by enabling secondary users to access the unused spectrum dynamically. Spectrum sensing and learning, as the fundamental function for dynamic spectrum sharing in 5G evolution and 6G wireless systems, have been research hotspots worldwide. This paper reviews classic narrowband and wideband spectrum sensing and learning algorithms. The sub-sampling framework and recovery algorithms based on compressed sensing theory and their hardware implementation are discussed under the trend of high channel bandwidth and large capacity to be deployed in 5G evolution and 6G communication systems. This paper also investigates and summarizes the recent progress in machine learning for spectrum sensing technology.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E104-B No.10 pp.1207-1216
- Publication Date
- 2021/10/01
- Publicized
- 2021/04/26
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.2020DSI0002
- Type of Manuscript
- Special Section INVITED PAPER (Special Section on Dynamic Spectrum Sharing for Future Wireless Systems)
- Category
Authors
Zihang SONG
University of Surrey
Yue GAO
University of Surrey
Rahim TAFAZOLLI
University of Surrey
Keyword
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Zihang SONG, Yue GAO, Rahim TAFAZOLLI, "A Survey on Spectrum Sensing and Learning Technologies for 6G" in IEICE TRANSACTIONS on Communications,
vol. E104-B, no. 10, pp. 1207-1216, October 2021, doi: 10.1587/transcom.2020DSI0002.
Abstract: Cognitive radio provides a feasible solution for alleviating the lack of spectrum resources by enabling secondary users to access the unused spectrum dynamically. Spectrum sensing and learning, as the fundamental function for dynamic spectrum sharing in 5G evolution and 6G wireless systems, have been research hotspots worldwide. This paper reviews classic narrowband and wideband spectrum sensing and learning algorithms. The sub-sampling framework and recovery algorithms based on compressed sensing theory and their hardware implementation are discussed under the trend of high channel bandwidth and large capacity to be deployed in 5G evolution and 6G communication systems. This paper also investigates and summarizes the recent progress in machine learning for spectrum sensing technology.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2020DSI0002/_p
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@ARTICLE{e104-b_10_1207,
author={Zihang SONG, Yue GAO, Rahim TAFAZOLLI, },
journal={IEICE TRANSACTIONS on Communications},
title={A Survey on Spectrum Sensing and Learning Technologies for 6G},
year={2021},
volume={E104-B},
number={10},
pages={1207-1216},
abstract={Cognitive radio provides a feasible solution for alleviating the lack of spectrum resources by enabling secondary users to access the unused spectrum dynamically. Spectrum sensing and learning, as the fundamental function for dynamic spectrum sharing in 5G evolution and 6G wireless systems, have been research hotspots worldwide. This paper reviews classic narrowband and wideband spectrum sensing and learning algorithms. The sub-sampling framework and recovery algorithms based on compressed sensing theory and their hardware implementation are discussed under the trend of high channel bandwidth and large capacity to be deployed in 5G evolution and 6G communication systems. This paper also investigates and summarizes the recent progress in machine learning for spectrum sensing technology.},
keywords={},
doi={10.1587/transcom.2020DSI0002},
ISSN={1745-1345},
month={October},}
Copy
TY - JOUR
TI - A Survey on Spectrum Sensing and Learning Technologies for 6G
T2 - IEICE TRANSACTIONS on Communications
SP - 1207
EP - 1216
AU - Zihang SONG
AU - Yue GAO
AU - Rahim TAFAZOLLI
PY - 2021
DO - 10.1587/transcom.2020DSI0002
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E104-B
IS - 10
JA - IEICE TRANSACTIONS on Communications
Y1 - October 2021
AB - Cognitive radio provides a feasible solution for alleviating the lack of spectrum resources by enabling secondary users to access the unused spectrum dynamically. Spectrum sensing and learning, as the fundamental function for dynamic spectrum sharing in 5G evolution and 6G wireless systems, have been research hotspots worldwide. This paper reviews classic narrowband and wideband spectrum sensing and learning algorithms. The sub-sampling framework and recovery algorithms based on compressed sensing theory and their hardware implementation are discussed under the trend of high channel bandwidth and large capacity to be deployed in 5G evolution and 6G communication systems. This paper also investigates and summarizes the recent progress in machine learning for spectrum sensing technology.
ER -
English
