Statistical CSI-based dual-hop hybrid RIS-assisted wireless communication

IF 2.9 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Shuang Liang, Guangliang Ren
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引用次数: 0

Abstract

Statistical channel state information (S-CSI) based ergodic achievable rate maximization is investigated for dual-hop hybrid reconfigurable intelligent surfaces (D-HRIS) assisted single-user wireless communication systems. A communication model, in which the transmitted signal is reflected through passive RIS (pRIS) to the active RIS (aRIS) and then reflected to the receiver via aRIS, is regarded as D-HRIS-assisted communication. The ergodic achievable rate is analyzed and its approximate expression is derived for this system. Based on the S-CSI, a low-complexity iterative updating scheme is proposed to design the precoding of the base station (BS) and the reflecting matrices of hybrid RISs (HRIS) to maximize the ergodic achievable rate. Specifically, the ergodic achievable rates of the proposal are 1.32 and 1.16 times larger than those of the dual-hop pRIS-aided scheme when the number of elements at the second-hop RIS is 20 and 100, respectively. When the Rician-K factor is larger than 0 dB, the ergodic achievable rate of the proposal is close to that of the instantaneous channel state information (I-CSI) based D-HRIS-assisted communication. And the performance of the system can still be guaranteed when RIS employs discrete phase shifters.
基于统计csi的双跳混合ris辅助无线通信
研究了基于统计信道状态信息(S-CSI)的双跳混合可重构智能表面(D-HRIS)辅助单用户无线通信系统的遍历可达速率最大化问题。将发射信号通过无源RIS (pRIS)反射到主动RIS (aRIS),再通过aRIS反射到接收机的通信模型称为d - hris辅助通信。分析了该系统的遍历可达率,并推导了遍历可达率的近似表达式。在S-CSI的基础上,提出了一种低复杂度迭代更新方案来设计基站预编码和混合RISs (HRIS)的反射矩阵,使遍历可达速率最大化。具体而言,当第二跳RIS上的元素数为20和100时,该方案的遍历可达率分别是双跳RIS辅助方案的1.32和1.16倍。当Rician-K因子大于0 dB时,该方案的遍历可达速率接近基于瞬时信道状态信息(I-CSI)的d - hris辅助通信速率。采用离散移相器后,系统的性能仍能得到保证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Digital Signal Processing
Digital Signal Processing 工程技术-工程:电子与电气
CiteScore
5.30
自引率
17.20%
发文量
435
审稿时长
66 days
期刊介绍: Digital Signal Processing: A Review Journal is one of the oldest and most established journals in the field of signal processing yet it aims to be the most innovative. The Journal invites top quality research articles at the frontiers of research in all aspects of signal processing. Our objective is to provide a platform for the publication of ground-breaking research in signal processing with both academic and industrial appeal. The journal has a special emphasis on statistical signal processing methodology such as Bayesian signal processing, and encourages articles on emerging applications of signal processing such as: • big data• machine learning• internet of things• information security• systems biology and computational biology,• financial time series analysis,• autonomous vehicles,• quantum computing,• neuromorphic engineering,• human-computer interaction and intelligent user interfaces,• environmental signal processing,• geophysical signal processing including seismic signal processing,• chemioinformatics and bioinformatics,• audio, visual and performance arts,• disaster management and prevention,• renewable energy,
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