具有可重构智能表面的近场MIMO信道中的空间复用

IF 1.1 4区 工程技术 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC
Giulio Bartoli, Andrea Abrardo, Nicolo Decarli, Davide Dardari, Marco Di Renzo
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引用次数: 0

摘要

我们考虑在存在可重构智能表面(RIS)的情况下的多输入多输出(MIMO)信道。具体而言,我们的重点是分析近场中视线和低散射MIMO信道中的空间复用增益。我们证明了信道容量是通过对端到端发射机RIS-接收机信道进行对角化,并将注水功率分配应用于发射机RIS和RIS-接收器信道奇异值的有序乘积来实现的。所获得的容量实现解决方案需要具有反射系数的非对角矩阵的RIS。在几乎无源RIS的假设下,即RIS不需要功率放大,仅在发射机处需要注水功率分配。我们将RIS的这种设计称为线性、几乎无源、可重构的电磁对象(EMO)。此外,我们还介绍了RIS的一种闭合形式和低复杂度设计,其反射系数矩阵与单位模项是对角的。反射系数由两个聚焦函数的乘积给出:一个将RIS辅助信号导向MIMO发射机的中点,另一个将RIS辅助信号转向MIMO接收机的中点。我们证明了在傍轴设置下,该解在视线通道中是精确的。借助于视线(自由空间)通道中的广泛数值模拟,我们表明,所提出的方法提供的性能(速率和自由度)接近于以高计算复杂度数值求解非凸优化问题所获得的性能。此外,我们还表明,在大多数考虑的案例研究中,它提供的性能与EMO(非对角线RIS)接近。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Spatial multiplexing in near field MIMO channels with reconfigurable intelligent surfaces

Spatial multiplexing in near field MIMO channels with reconfigurable intelligent surfaces

We consider a multiple-input multiple-output (MIMO) channel in the presence of a reconfigurable intelligent surface (RIS). Specifically, our focus is on analysing the spatial multiplexing gains in line-of-sight and low-scattering MIMO channels in the near field. We prove that the channel capacity is achieved by diagonalising the end-to-end transmitter-RIS-receiver channel, and applying the water-filling power allocation to the ordered product of the singular values of the transmitter-RIS and RIS-receiver channels. The obtained capacity-achieving solution requires an RIS with a non-diagonal matrix of reflection coefficients. Under the assumption of nearly-passive RIS, that is, no power amplification is needed at the RIS, the water-filling power allocation is necessary only at the transmitter. We refer to this design of RIS as a linear, nearly-passive, reconfigurable electromagnetic object (EMO). In addition, we introduce a closed-form and low-complexity design for RIS, whose matrix of reflection coefficients is diagonal with unit-modulus entries. The reflection coefficients are given by the product of two focusing functions: one steering the RIS-aided signal towards the mid-point of the MIMO transmitter and one steering the RIS-aided signal towards the mid-point of the MIMO receiver. We prove that this solution is exact in line-of-sight channels under the paraxial setup. With the aid of extensive numerical simulations in line-of-sight (free-space) channels, we show that the proposed approach offers performance (rate and degrees of freedom) close to that obtained by numerically solving non-convex optimization problems at a high computational complexity. Also, we show that it provides performance close to that achieved by the EMO (non-diagonal RIS) in most of the considered case studies.

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来源期刊
IET Signal Processing
IET Signal Processing 工程技术-工程:电子与电气
CiteScore
3.80
自引率
5.90%
发文量
83
审稿时长
9.5 months
期刊介绍: IET Signal Processing publishes research on a diverse range of signal processing and machine learning topics, covering a variety of applications, disciplines, modalities, and techniques in detection, estimation, inference, and classification problems. The research published includes advances in algorithm design for the analysis of single and high-multi-dimensional data, sparsity, linear and non-linear systems, recursive and non-recursive digital filters and multi-rate filter banks, as well a range of topics that span from sensor array processing, deep convolutional neural network based approaches to the application of chaos theory, and far more. Topics covered by scope include, but are not limited to: advances in single and multi-dimensional filter design and implementation linear and nonlinear, fixed and adaptive digital filters and multirate filter banks statistical signal processing techniques and analysis classical, parametric and higher order spectral analysis signal transformation and compression techniques, including time-frequency analysis system modelling and adaptive identification techniques machine learning based approaches to signal processing Bayesian methods for signal processing, including Monte-Carlo Markov-chain and particle filtering techniques theory and application of blind and semi-blind signal separation techniques signal processing techniques for analysis, enhancement, coding, synthesis and recognition of speech signals direction-finding and beamforming techniques for audio and electromagnetic signals analysis techniques for biomedical signals baseband signal processing techniques for transmission and reception of communication signals signal processing techniques for data hiding and audio watermarking sparse signal processing and compressive sensing Special Issue Call for Papers: Intelligent Deep Fuzzy Model for Signal Processing - https://digital-library.theiet.org/files/IET_SPR_CFP_IDFMSP.pdf
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