Learning to Beamform for Dual-Functional MIMO Radar-Communication Systems

doi:10.1109/ICC45041.2023.10279159

	Learning to Beamform for Dual-Functional MIMO Radar-Communication Systems
	Yifei Zhao1 ; Zixin Wang1 ; Zhibin Wang1 ; Xu Chen 2; Yong Zhou1
	2023-05-28
会议录名称	ICC 2023 - IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS
ISSN	1938-1883
卷号	2023-May
页码	3572-3577
发表状态	已发表
DOI	10.1109/ICC45041.2023.10279159
摘要	Dual-functional radar-communication (DFRC) attracts extensive attention recently, given its potential to integrate the sensing and communication processes for enhancing the spectrum efficiency and hardware utilization. Due to the co-channel interference, effective resource allocation is a critical issue for DFRC, which typically relies on the accurate channel estimation. However, the conventional estimate-then-optimize algorithms may not work well due to inaccurate channel estimation, high computation complexity, and inconsistent optimization goals. This paper considers a DRFC system with multiuser multiple-input-multiple-output (MIMO) communications and MIMO radar sensing, where an end-to-end learning algorithm is developed to tackle the aforementioned issues. We formulate an optimization problem to maximize the communication performance subject to the radar sensing constraints, via optimizing both the transmit and receive beamforming matrices, while considering channel estimation in the loop. To tackle this challenging problem, we exploit the universal approximation property of the neural network to develop an end-to-end learning algorithm to directly learn the mapping between the pilot signals and the beamforming matrices, and meanwhile appropriately design the loss function to account for the radar sensing constraints. Simulations show that our proposed algorithm achieves a much greater communication performance than the baseline algorithm, while guaranteeing the same sensing performance. © 2023 IEEE.
关键词	Array signal processing Simulation Neural networks Channel estimation Radar Approximation algorithms Sensors
会议名称	2023 IEEE International Conference on Communications, ICC 2023
会议地点	Rome, Italy
会议日期	28 May-1 June 2023
URL	查看原文
收录类别	EI
语种	英语
出版者	Institute of Electrical and Electronics Engineers Inc.
EI入藏号	20234815114452
EI主题词	Learning algorithms
EI分类号	711.2 Electromagnetic Waves in Relation to Various Structures ; 716.1 Information Theory and Signal Processing ; 716.2 Radar Systems and Equipment ; 723.4.2 Machine Learning ; 921 Mathematics
原始文献类型	Conference article (CA)
来源库	IEEE
引用统计	正在获取...
文献类型	会议论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/343611
专题	信息科学与技术学院信息科学与技术学院_PI研究组_周勇组信息科学与技术学院_硕士生信息科学与技术学院_博士生
作者单位	1.School of Information Science and Technology, ShanghaiTech University, Shanghai, China 2.School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
第一作者单位	信息科学与技术学院
第一作者的第一单位	信息科学与技术学院
推荐引用方式 GB/T 7714	Yifei Zhao,Zixin Wang,Zhibin Wang,et al. Learning to Beamform for Dual-Functional MIMO Radar-Communication Systems[C]:Institute of Electrical and Electronics Engineers Inc.,2023:3572-3577.