A Survey on Spectrum Sensing and Learning Technologies for 6G

Zihang SONG
Yue GAO
Rahim TAFAZOLLI

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: 
Keyword: 
cognitive radio,  spectrum sensing,  compressed sensing,  machine learning,  

Full Text: FreePDF

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.