中文版 | English
Title

Predictive Beamforming for Integrated Sensing and Communication in Vehicular Networks: A Deep Learning Approach

Author
DOI
Publication Years
2022
Conference Name
IEEE International Conference on Communications (ICC)
ISSN
1550-3607
ISBN
978-1-5386-8348-4
Source Title
Volume
2022-May
Pages
1948-1954
Conference Date
16-20 May 2022
Conference Place
Seoul, Korea, Republic of
Publication Place
345 E 47TH ST, NEW YORK, NY 10017 USA
Publisher
Abstract
The implementation of integrated sensing and communication (ISAC) highly depends on the effective beamforming design exploiting accurate instantaneous channel state information (ICSI). However, channel tracking in ISAC requires large amount of training overhead and prohibitively large computational complexity. To address this problem, in this paper, we focus on ISAC-assisted vehicular networks and exploit a deep learning approach to implicitly learn the features of historical channels and directly predict the beamforming matrix for the next time slot to maximize the average achievable sum-rate of system, thus bypassing the need of explicit channel tracking for reducing the system signaling overhead. To this end, a general sum-rate maximization problem with Cramer-Rao lower bounds-based sensing constraints is first formulated for the considered ISAC system. Then, a historical channels-based convolutional long short-term memory network is designed for predictive beamforming that can exploit the spatial and temporal dependencies of communication channels to further improve the learning performance. Finally, simulation results show that the proposed method can satisfy the requirement of sensing performance, while its achievable sum-rate can approach the upper bound obtained by a genie-aided scheme with perfect ICSI available.
Keywords
SUSTech Authorship
Others
Language
English
URL[Source Record]
Indexed By
Funding Project
National Natural Science Foundation of China[61801082]
WOS Research Area
Telecommunications
WOS Subject
Telecommunications
WOS Accession No
WOS:000864709902042
EI Accession Number
20223712710859
EI Keywords
Channel state information ; Deep learning
ESI Classification Code
Ergonomics and Human Factors Engineering:461.4 ; Electromagnetic Waves in Relation to Various Structures:711.2
Data Source
IEEE
PDF urlhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9839000
Citation statistics
Cited Times [WOS]:0
Document TypeConference paper
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/401502
DepartmentDepartment of Electrical and Electronic Engineering
Affiliation
1.State Key Laboratory of Internet of Things for Smart City, University of Macau, Macau, China
2.Department of Electronic and Electrical Engineering, Southern University of Science and Technology, China
3.School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, Australia
4.School of Electrical and Information Engineering, University of Sydney, Sydney, Australia
Recommended Citation
GB/T 7714
Chang Liu,Weijie Yuan,Shuangyang Li,et al. Predictive Beamforming for Integrated Sensing and Communication in Vehicular Networks: A Deep Learning Approach[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2022:1948-1954.
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