RIS-Aided Integrated Sensing and Communication Systems: STAR-RIS Versus Passive RIS?

被引:2
|
作者
Saikia, Prajwalita [1 ]
Jee, Anand [2 ]
Singh, Keshav [1 ]
Pan, Cunhua [3 ]
Huang, Wan-Jen [1 ]
Tsiftsis, Theodoros A. [4 ,5 ]
机构
[1] Natl Sun Yat Sen Univ, Inst Commun Engn, Kaohsiung 804, Taiwan
[2] Indian Inst Technol Delhi, Dept Elect Engn, New Delhi 110016, India
[3] Southeast Univ, Natl Mobile Commun Res Lab, Nanjing 210096, Peoples R China
[4] Univ Thessaly, Dept Informat Telecommun, Lamia 35100, Greece
[5] Univ Nottingham Ningbo China, Dept Elect & Elect Engn, Ningbo 315100, Peoples R China
来源
IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY | 2024年 / 5卷
基金
中国国家自然科学基金;
关键词
Integrated sensing and communication; Reconfigurable intelligent surfaces; Radar; Interference; Reflection; Array signal processing; Signal to noise ratio; Optimization; Next generation networking; Internet of Things; Integrated sensing and communication (ISAC); multiple input single output (MISO); passive reconfigurable intelligent surface (P-RIS); simultaneously transmitting and reflecting (STAR) RIS (S-RIS); weighted sum rate (WSR); INTELLIGENT REFLECTING SURFACE; RATE MAXIMIZATION; RADAR; DESIGN;
D O I
10.1109/OJCOMS.2024.3515933
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
A shortage of frequencies exists due to the demand of extensive connectivity and limited availability of spectrum. Thus a prominent solution of sharing spectrum between radar and communication systems has been proposed. Integrated sensing and communication (ISAC) aims to achieve complete integration and reciprocal advantages between communication and sensing processes. In this work, we consider Simultaneous Transmission and Reflection (STAR) Reconfigurable Intelligent Surface (S-RIS) and passive RIS (P-RIS) assisted dual-function radar communication system where S-RIS and P-RIS assist communication and sensing functionalities, respectively. We formulate an optimization problem that jointly optimizes the beamforming vector at the multi-antenna ISAC transmitter and phase shift vector to maximize the weighted sum-rate (WSR) at the communication users while taking care of the maximum power limit the ISAC transmitter and ensuring the performance of sensing model to detect targets and limitations of phase and amplitude of S-RIS elements. To address the non-convexity, we propose a low-complexity alternating optimization (AO) algorithm. Furthermore, we provide simulation results to verify the viability of the proposed framework with its a) only P-RIS assisted scheme, and b) proposed model with random phase shift, counterparts. The proposed algorithm is also shown to be effective in delivering nearly optimal design outcomes in scenario where the channel state information is imperfect (ICSI). Simulation results demonstrate the impact of RIS elements, number of antennas at the ISAC transmitter and the transmit power on WSR. Accordingly, we illustrate the impact of S-RIS and reflection users (RU) locations and the effect of sensing threshold and the number of targets which highlights the trade-off between sensing and communication. The S-RIS framework offer approximately performance gain of 12.5% and 38.8% as compared to conventional and random cases, respectively.
引用
收藏
页码:7954 / 7973
页数:20
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