APSIPA Transactions on Signal and Information Processing > Vol 13 > Issue 4

Joint Optimization for RIS Assisted Dual Functional Radar Communication in IoV

Yaping Cui, Chongqing University of Posts and Telecommunications and Chongqing Key Laboratory, China, cuiyp@cqupt.edu.cn , Kang Wang, Chongqing University of Posts and Telecommunications and Chongqing Key Laboratory, China, Peng He, Chongqing University of Posts and Telecommunications and Chongqing Key Laboratory, China, Ruyan Wang, Chongqing University of Posts and Telecommunications and Chongqing Key Laboratory, China, Dapeng Wu, Chongqing University of Posts and Telecommunications and Chongqing Key Laboratory, China
 
Suggested Citation
Yaping Cui, Kang Wang, Peng He, Ruyan Wang and Dapeng Wu (2024), "Joint Optimization for RIS Assisted Dual Functional Radar Communication in IoV", APSIPA Transactions on Signal and Information Processing: Vol. 13: No. 4, e303. http://dx.doi.org/10.1561/116.00000060

Publication Date: 16 May 2024
© 2024 Y. Cui, K. Wang, P. He, R. Wang and D. Wu
 
Subjects
 
Keywords
Internet of Vehiclesreconfigurable intelligent surfacedual function radar communication system
 

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In this article:
Introduction 
System Model 
Problem Formulation And Transformation 
Joint Guaranteed Radar Communication Algorithm 
Simulations And Analysis 
Conclusion 
References 

Abstract

Reconfigurable Intelligent Surface (RIS) has been recognized as the core technology for 6G. However, transmission signals may be blocked by obstacles due to mobility and complex transmission in vehicular scenario. In order to solve the above issue, this paper researches the RIS assisted dual function radar communication system (DFRC) in the Internet of Vehicles (IoV), and proposes an alternating optimization algorithm, named joint guaranteed radar communication (JGRC) algorithm, with sensed power and semidefinite relaxation to maximize the spectral efficiency of the communication vehicle while simultaneously ensuring the radar sensing performance of the target vehicle. Specifically, the proposed JGRC algorithm can be divided into two stages. Stage one, active beamforming of Base Station (BS) transmission is optimized with the power constraint of the sensed target vehicle. Stage two, the optimized active beamforming is applied, and the phase shift matrix is optimized. Then, RIS phase shift optimization problem is transformed into Quadratically Constrained Quadratic Programming (QCQP), and rank-1 non-convex constraint is relaxed by semidefinite relaxation, which can be transformed into a standard semidefinite programming solved by Matlab toolbox. Simulation results demonstrate that compared to the RIS element random reflection, the proposed algorithm achieves higher spectral efficiency by 9.5%.

DOI:10.1561/116.00000060

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APSIPA Transactions on Signal and Information Processing Special Issue - Emerging Wireless Sensing Technologies for Smart Environments
See the other articles that are part of this special issue.