南方科技大学知识苑(SUSTech KC): Deep Learning Based Detection With Radar Interference

题名	Deep Learning Based Detection With Radar Interference
作者	Liu，Chenguang 1; Chen，Yunfei 2; Yang，Shuang Hua3
发表日期	2022
DOI	10.1109/TVT.2022.3158692
发表期刊	IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 影响因子和分区
ISSN	0018-9545
EISSN	1939-9359
卷号	71 期号:6页码:6245-6254
摘要	Due to the increasing demand for spectrum resources, the co-existence of communications and radar systems has been proposed that allows radar and communications systems to operate in the same frequency band. On the other hand, deep learning has shown great potential in revolutionizing communications systems. In this work, we investigate the use of deep learning in communications systems subject to interference from radar systems. Specifically, we consider a single-carrier communications system. Linear frequency-modulated (LFM) and frequency-modulated continuous-wave (FMCW) are considered for radar. Several important system parameters, including the level of noise and interference, the radar interference coverage, the symbol duration, feature extraction methods and the number of hidden layers are investigated for the performance of the detector. Fully connected deep neural network (FCDNN) and long short-term memory (LSTM) detectors are implemented, where principle component analysis (PCA) is applied to preprocess the observed signals for the FCDNN detector. Numerical results show that the learning-based detector achieves comparable performance in the radar-communication system to the traditional detector but without interference cancellation. Preprocessing the received signals with PCA can improve the performance of FCDNN when interference is strong. Also, LSTM shows more robust performance than FCDNN when the channel has time-related distortion.
关键词	Communication systems Communications deep learning Deep learning Detectors Interference Neural networks Radar Radar detection radar interference signal detection
相关链接	[Scopus记录]
收录类别	SCI ; EI
语种	英语
学校署名	其他
资助项目	EC H2020 DAWN4IoE-Data Aware Wireless Network for Internet-of-Everything[778305] ; National Natural Science Foundation of China[61873119,92067109] ; Science and Technology Innovation Commission of Shenzhen[JCYJ20200109141218676] ; Guangdong Provincial Science and Technology Commission[2021A0505030001]
WOS研究方向	Engineering ; Telecommunications ; Transportation
WOS类目	Engineering, Electrical & Electronic ; Telecommunications ; Transportation Science & Technology
WOS记录号	WOS:000815676900048
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
EI入藏号	20221111799709
EI主题词	Deep neural networks ; Feature extraction ; Frequency modulation ; Long short-term memory ; Principal component analysis ; Radar interference ; Radar signal processing ; Tracking radar
EI分类号	Ergonomics and Human Factors Engineering:461.4 ; Information Theory and Signal Processing:716.1 ; Radar Systems and Equipment:716.2 ; Mathematical Statistics:922.2
ESI学科分类	ENGINEERING
Scopus记录号	2-s2.0-85126307950
来源库	Scopus
全文链接	https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9733260
引用统计	被引频次[WOS]：7
成果类型	期刊论文
条目标识符	http://sustech.caswiz.com/handle/2SGJ60CL/327832
专题	南方科技大学
作者单位	1.School of Engineering, University of Warwick, 2707 Coventry, West Midlands, United Kingdom of Great Britain and Northern Ireland, CV4 7AL 2.School of Engineering, University of Warwick, Coventry, West Midlands, United Kingdom of Great Britain and Northern Ireland, CV4 7AL 3.Computer Science and Engineering, Southern University of Science and Technology, 255310 Shenzhen, Guangdong, China
推荐引用方式 GB/T 7714	Liu，Chenguang,Chen，Yunfei,Yang，Shuang Hua. Deep Learning Based Detection With Radar Interference[J]. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY,2022,71(6):6245-6254.
APA	Liu，Chenguang,Chen，Yunfei,&Yang，Shuang Hua.(2022).Deep Learning Based Detection With Radar Interference.IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY,71(6),6245-6254.
MLA	Liu，Chenguang,et al."Deep Learning Based Detection With Radar Interference".IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 71.6(2022):6245-6254.