摘要
To complement terrestrial connections,the space-air-ground integrated network(SAGIN)has been proposed to provide wide-area connections with improved quality of experience(QoE).Spectrum management is an important issue in SAGIN due to the explosive proliferation of wireless devices and services.While the progress on enabling dynamic spectrum access shows promise in advancing increased spectrum sharing,the issue of reliable spectrum sensing under low signal-to-noise ratio(SNR)remains one of the key challenges faced by the spectrum management.As artificial intelligence can provide wireless networks intelligence through learning and data mining,deep learning-based spectrum sensing is proposed in order to improve the spectrum sensing performance,where a deep neural network-based detection framework is built to extract features in a data-driven way based on the covariance matrix of the received signal.To eliminate the impact of noise uncertainty,a blind threshold setting scheme is proposed without using the system prior information.Numerical analyses on simulated and real-world signals show that the detection performance of the proposed scheme is improved under a low SNR regime.
基金
National Key R&D Program of China(2019YFB1803300)
National Natural Science Foundation of China(NSFC)(61901276)
Natural Science Foundation of Guangdong Province(2020A1515010673)
Foundation for Distinguished Young Talents in Higher Education of Guangdong(2018KQNCX222)
Natural Science Foundation of SZU(2019115)。
作者简介
Ruifan Liu,received the B.Sc.degree in communication and information engineering from Shenzhen University in 2020.His research interests include cognitive radio,and spectrum detection and sharing;corresponding author:Yuan Ma(S’15-M’17),received the B.Sc.degree(First Class Hons.)in telecommunications engineering from Beijing University of Posts and Telecommunications,Beijing,China,in 2013,and the Ph.D.degree in electronic engineering from Queen Mary University of London,London,U.K.,in 2017.She is currently an Assistant Professor with the College of Information Engineering,Shenzhen University.Her research interests include cognitive and cooperative wireless networking,compressive sensing,data-driven signal processing,and spectrum analysis,detection and sharing;Xingjian Zhang(S’16-M’19),received the B.Sc.degree(First Class Hons.)in telecommunications engineering from Beijing University of Posts and Telecommunications,Beijing,China,in 2014,and the Ph.D.degree in electronic engineering from Queen Mary University of London,London,U.K.,in 2018.He is currently a Researcher in the Peng Cheng Laboratory.His research interests include cooperative wireless sensor networks,compressive sensing,real-time spectrum monitoring and analysis,and Internet of things(IoT)applications;Yue Gao(S’03-M’07-SM’13),a Professor and Chair in Wireless Communications at Institute for Communication Systems,University of Surrey,U.K.He received the Ph.D.degree from the Queen Mary University of London(QMUL),U.K.,in 2007.He currently leads the Antennas and Signal Processing Lab developing fundamental research into practice in the interdisciplinary area of smart antennas,signal processing,spectrum sharing,millimetre-wave and Internet of things technologies in mobile and satellite systems.He has published over 200 peer-reviewed journal and conference papers,3 patents,1 book,5 book chapters,and 3 best paper awards.He is an Engineering and Physical Sciences Research Council Fellow from 2018 to 2023.He was a CoRecipient of the EU Horizon Prize Award on Collaborative Spectrum Sharing in 2016.He served as the Signal Processing for Communications Symposium Co-Chair for IEEE ICCC 2016,the Publicity Co-Chair for the IEEE GLOBECOM 2016,the Cognitive Radio Symposium Co-Chair for the IEEE GLOBECOM 2017,and the General Chair of the IEEE WoWMoM and iWEM 2017.He is the Chair of the IEEE Technical Committee on Cognitive Networks,the Secretary of the IEEE ComSoc Technical Committee Wireless Communication and the IEEE Distinguished Lecturer of the Vehicular Technology Society.He is an Editor for the IEEE Internet of Things Journal,IEEE Transactions on Vehicular Technology,and IEEE Transactions on Cognitive Networks.
