Performance Analysis of Systems with Coupled and Decoupled RISs

We analyze and compare different methods for handling the mutual coupling in RIS-aided communication systems. A new mutual coupling aware algorithm is derived where the reactance of each element is updated successively with a closed-form solution. In comparison to existing element-wise methods, this approach leads to a considerably reduced computational complexity. Furthermore, we introduce decoupling networks for the RIS array as a potential solution for handling mutual coupling. With these networks, the system model reduces to the same structure as when no mutual coupling were present. Including decoupling networks, we can optimize the channel gain of a RIS-aided SISO system in closed-form which allows to analyze the scenario under mutual coupling analytically and to draw connections to the conventional transmit array gain. In particular, a super-quadratic channel gain can be achieved which scales as N^4 where N is the number of RIS elements.

Gohberg-Semencul Estimation of Toeplitz Structured Covariance Matrices and Their Inverses

When only few data samples are accessible, utilizing structural prior knowledge is essential for estimating covariance matrices and their inverses. One prominent example is knowing the covariance matrix to be Toeplitz structured, which occurs when dealing with wide sense stationary (WSS) processes. This work introduces a novel class of positive definiteness ensuring likelihood-based estimators for Toeplitz structured covariance matrices (CMs) and their inverses. In order to accomplish this, we derive positive definiteness enforcing constraint sets for the Gohberg-Semencul (GS) parameterization of inverse symmetric Toeplitz matrices. Motivated by the relationship between the GS parameterization and autoregressive (AR) processes, we propose hyperparameter tuning techniques, which enable our estimators to combine advantages from state-of-the-art likelihood and non-parametric estimators. Moreover, we present a computationally cheap closed-form estimator, which is derived by maximizing an approximate likelihood. Due to the ensured positive definiteness, our estimators perform well for both the estimation of the CM and the inverse covariance matrix (ICM). Extensive simulation results validate the proposed estimators' efficacy for several standard Toeplitz structured CMs commonly employed in a wide range of applications.

A Zero-Forcing Approach for the RIS-Aided MIMO Broadcast Channel

We present efficient algorithms for the sum-spectral efficiency (SE) maximization of the multi-user reconfigurable intelligent surface (RIS)-aided multiple-input multiple-output (MIMO) broadcast channel based on a zero-forcing approach. These methods conduct a user allocation for which the computation is independent of the number of elements at the RIS, that is usually large. Specifically, two algorithms are given that exploit the line-of-sight (LOS) structure between the base station (BS) and the RIS. Simulations show superior SE performance compared to other linear precoding algorithms but with lower complexity.

Data-Aided Channel Estimation Utilizing Gaussian Mixture Models

In this work, we propose two methods that utilize data symbols in addition to pilot symbols for improved channel estimation quality in a multi-user system, so-called semi-blind channel estimation. To this end, a subspace is estimated based on all received symbols and utilized to improve the estimation quality of a Gaussian mixture model-based channel estimator, which solely uses pilot symbols for channel estimation. Both of the proposed approaches allow for parallelization. Even the precomputation of estimation filters, which is beneficial in terms of computational complexity, is enabled by one of the proposed methods. Numerical simulations for real channel measurement data available to us show that the proposed methods outperform the studied state-of-the-art channel estimators.

Design of a Single-User RIS-Aided MISO System Based on Statistical Channel Knowledge

Reconfigurable intelligent surface (RIS) is considered a prospective technology for beyond fifth-generation (5G) networks to improve the spectral and energy efficiency at a low cost. Prior works on the RIS mainly rely on perfect channel state information (CSI), which imposes a huge computational complexity. This work considers a single-user RIS-assisted communication system, where the second-order statistical knowledge of the channels is exploited to reduce the training overhead. We present algorithms that do not require estimation of the CSI and reconfiguration of the RIS in every channel coherence interval, which constitutes one of the most critical practical issues in an RIS-aided system.

Physically Consistent Modelling of Wireless Links with Reconfigurable Intelligent Surfaces Using Multiport Network Analysis

RISs are an emerging technology for engineering the channels of future wireless communication systems. The vast majority of research publications on RIS are focussing on system-level optimization and are based on very simplistic models ignoring basic physical laws. There are only a few publications with a focus on physical modeling. Nevertheless, the widely employed model is still inconsistent with basic physical laws. We will show that even with a very simple abstract model based on isotropic radiators, ignoring any mismatch, mutual coupling, and losses, each RIS element cannot be modeled to simply reflect the incident signal by manipulating its phase only and letting the amplitude unchanged. We will demonstrate the inconsistencies with the aid of very simple toy examples, even with only one or two RIS elements. Based on impedance parameters, the problems associated with scattering parameters can be identified enabling a correct interpretation of the derived solutions.

High SNR Analysis of RIS aided MIMO BC Channels

We analyze the influence of an reconfigurable intelligent surface (RIS) on the channel eigenvalues within a high signal-to-noise ratio (SNR) scenario. This allows to connect specific channel properties with the rank improvement capabilities of the RIS. In particular, fundamental limits due to a possible line of sight (LOS) setup between the base station (BS) and the RIS are derived. Furthermore, dirty paper coding (DPC) based schemes are compared to linear precoding in such a scenario and it is shown that under certain channel conditions, the performance gap between DPC and linear precoding can be made arbitrarily small by the RIS.