This paper considers a multi-user multiple-input multiple-output (MU-MIMO) system where the downlink communication between a base station (BS) and multiple user equipments (UEs) is aided by a reconfigurable intelligent surface (RIS). We study the sum rate maximization problem with the objective of finding the optimal precoding vectors and RIS configuration. Due to fronthaul limitation, each entry of the precoding vectors must be picked from a finite set of quantization labels. Furthermore, two scenarios for the RIS are investigated, one with continuous infinite-resolution reflection coefficients and another with discrete finite-resolution reflection coefficients. A novel framework is developed which, in contrast to the common literature that only offers sub-optimal solutions for optimization of discrete variables, is able to find the optimal solution to problems involving discrete constraints. Based on the classical weighted minimum mean square error (WMMSE), we transform the original problem into an equivalent weighted sum mean square error (MSE) minimization problem and solve it iteratively. We compute the optimal precoding vectors via an efficient algorithm inspired by sphere decoding (SD). For optimizing the discrete RIS configuration, two solutions based on the SD algorithm are developed: An optimal SD-based algorithm and a low-complexity heuristic method that can efficiently obtain RIS configuration without much loss in optimality. The effectiveness of the presented algorithms is corroborated via numerical simulations where it is shown that the proposed designs are remarkably superior to the commonly used benchmarks.

The signal processing community is currently witnessing a growing interest in near-field signal processing, driven by the trend toward the use of large-aperture arrays with high spatial resolution in the fields of communication, localization, sensing, imaging, and so on. From the perspective of localization and sensing, this trend breaks the basic far-field assumptions that have dominated the array signal processing research in the past, presenting new challenges and promising opportunities.

The use of a reconfigurable intelligent surface (RIS) for aiding user-specific transmission has been widely explored. However, little attention has been devoted to utilizing RIS for assisting cell-specific transmission, where the RIS needs to reflect signals in a broad angular range. Furthermore, although modern communication systems operate in two polarizations, the majority of the works on RIS consider a uni-polarized surface, only reflecting the signals in one polarization. To fill these gaps, we study a downlink broadcasting scenario where a base station (BS) sends a cell-specific signal to all the users residing at unknown locations with the assistance of a dual-polarized RIS. We utilize the duality between the auto-correlation function and power spectrum in the space/spatial-frequency domain to design configurations for broad-beam reflection. We first consider a free-space line-of-sight BS-RIS channel and show that the RIS configuration matrices must form a Golay complementary array pair for broad-beam radiation. We also present how to form Golay complementary array pairs based on known Golay complementary sequence pairs. We then consider an arbitrary BS-RIS channel and propose an algorithm based on stochastic optimization to find RIS configurations that produce a practically broad beam by relaxing the requirement on uniform broadness. Numerical simulations are finally conducted to corroborate the analyses and evaluate the performance.

In this paper, we consider a single-anchor localization system assisted by a reconfigurable intelligent surface (RIS), where the objective is to localize multiple user equipments (UEs) placed in the radiative near-field region of the RIS by estimating their azimuth angle-of-arrival (AoA), elevation AoA, and distance to the surface. The three-dimensional (3D) locations can be accurately estimated via the conventional MUltiple SIgnal Classification (MUSIC) algorithm, albeit at the expense of tremendous complexity due to the 3D grid search. In this paper, capitalizing on the symmetric structure of the RIS, we propose a novel modified MUSIC algorithm that can efficiently decouple the AoA and distance estimation problems and drastically reduce the complexity compared to the standard 3D MUSIC algorithm. Additionally, we introduce a spatial smoothing method by partitioning the RIS into overlapping sub-RISs to address the rank-deficiency issue in the signal covariance matrix. We corroborate the effectiveness of the proposed algorithm via numerical simulations and show that it can achieve the same performance as 3D MUSIC but with much lower complexity.

In this paper, we consider a downlink multi-user multiple-input multiple-output (MU-MIMO) communication assisted by a reconfigurable intelligent surface (RIS) and study the precoding and RIS configuration design under practical system constraints. These constraints include the limited-capacity fronthaul at the transmitter side and the finite resolution of RIS elements. We investigate the sum mean squared error (MSE) minimization problem and propose an algorithm based on the block coordinate descent method to optimize the precoding, RIS configuration, and receiver gains. We compute the precoding vectors and RIS configuration using the Schnorr-Euchner sphere decoding (SESD) method which delivers the optimal MSE-minimizing solution. We numerically evaluate the performance of the proposed SESD-based methods and corroborate their effectiveness in improving the system performance.

The number of users that can be spatially multiplexed by a wireless access point depends on the aperture of its antenna array. When the aperture increases and wavelength shrinks, “new” electromagnetic phenomena can be utilized to further enhance network capacity. In this chapter, we describe how extremely large aperture arrays (ELAA) can extend the radiative near-field region to kilometer distances. We demonstrate how this affects the propagation models in line-of-sight (LoS) scenarios and enables finite-depth beamforming. In particular, it becomes possible to simultaneously serve users that are located in the same direction but at different distances.

Considering the dimensionality of a typical reconfigurable intelligent surface (RIS), channel state information acquisition in RIS-assisted systems requires lengthy pilot transmissions. Moreover, the large aperture of the RIS may cause transmitters/receivers to fall in its near-field region, where both distance and angles affect the channel structure. This paper proposes a parametric maximum likelihood estimation (MLE) framework for jointly estimating the direct channel between the user and the base station (BS) and the line-of-sight channel between the user and the RIS, in both far-field and near-field scenarios. The MLE framework is first developed for the case of single-antenna BS and later extended to the scenario where the BS is equipped with multiple antennas. A novel adaptive RIS configuration strategy is proposed to select the RIS configuration for the next pilot to actively refine the estimate. We design a minimal-sized codebook of orthogonal RIS configurations to choose from during pilot transmission with a dimension much smaller than the number of RIS elements. To further reduce the required number of pilots, we propose an initialization strategy with two wide beams. We demonstrate numerically that the proposed MLE method needs only a few pilots for achieving accurate channel estimates and further show that the presented framework performs well under Rician fading. We also showcase efficient user channel tracking in near-field and far-field scenarios.

A reconfigurable intelligent surface (RIS) is a holographic MIMO surface composed of a large number of passive elements that can induce adjustable phase shifts to the impinging waves. By creating virtual line-of-sight (LOS) paths between the transmitter and the receiver, RIS can be a game changer for millimeter-wave (mmWave) communication systems that typically suffer from severe signal attenuation. Reaping the benefits of RIS, however, relies on the accuracy of the channel estimation, which is a challenging task due to the large number of RIS elements. Specifically, conventional channel estimators require a pilot overhead equal to the number of RIS elements, which is impractical. Herein, we propose a novel way to approximately represent the RIS channels in a lower-dimensional subspace and derive the basis vectors for the identified subspace. We use this channel structure to only send pilots in this subspace, thereby vastly saving on the pilot overhead. Numerical results demonstrate that when the RIS has an element spacing of a quarter of the wavelength, our method reduces the pilot overhead by 80% with retained or even improved performance.

Reconfigurable intelligent surface (RIS) is a newly-emerged technology that might fundamentally change how wireless networks are operated. Though extensively studied in recent years, the practical limitations of RIS are often neglected when assessing the performance of RIS-assisted communication networks. One of these limitations is that each RIS element is restricted to incur a controllable phase shift to the reflected signal from a predefined discrete set. This paper studies an RIS-assisted multi-user multiple-input multiple-output (MIMO) system, where an RIS with discrete phase shifts assists in simultaneous uplink data transmission from multiple user equipments (UEs) to a base station (BS). We aim to maximize the sum rate by optimizing the receive beamforming vectors and RIS phase shift configuration. To this end, we transform the original sum-rate maximization problem into a minimum mean square error (MMSE) minimization problem and employ the block coordinate descent (BCD) technique for iterative optimization of the variables until convergence. We formulate the discrete RIS phase shift optimization problem as a mixed-integer least squares problem and propose a novel method based on sphere decoding (SD) to solve it. Through numerical evaluation, we show that the proposed discrete phase shift design outperforms the conventional nearest point mapping method, which is prevalently used in previous works.

While reconfigurable intelligent surface (RIS)-aided user-specific beamforming has been vastly investigated, the aspect of utilizing RISs for assisting cell-specific transmission has been largely unattended. Aiming to fill this gap, we study a downlink broadcasting scenario where a base station (BS) sends a cell-specific signal to all the users located in a wide angular area with the assistance of a dual-polarized RIS. We utilize the polarization degree of freedom offered by this type of RIS and design the phase configurations in the two polarizations in such a way that the RIS can radiate a broad beam, thereby uniformly covering all azimuth and elevation angles where the users might reside. Specifically, the per-polarization configuration matrices are designed in such a way that the total power-domain array factor becomes spatially flat over all observation angles implying that the RIS can preserve the broad radiation pattern of a single element while boosting its gain proportionally to its aperture size. We validate the mathematical analyses via numerical simulations.