Reconfigurable Intelligent Surfaces for 6G: Nine Fundamental Issues and One Critical Problem

Reconfigurable intelligent surface (RIS) is promising for future 6G wireless communications. However, the increased number of RIS elements results in the high overhead for channel acquisition and the non-negligible power consumption. Therefore, how to improve the system capacity with limited RIS elements is essential. Unlike the classical regular RIS whose elements are arranged on a regular grid, in this paper, we propose an irregular RIS structure to improve the system capacity. The key idea is to irregularly configure a given number of RIS elements on an enlarged surface, which provides extra spatial degrees of freedom compared with the regular RIS. In this way, the received signal power can be enhanced, and thus the system capacity can be improved. Then, we formulate a joint topology and precoding optimization problem to maximize the capacity for irregular RIS-aided communication systems. Accordingly, a joint optimization algorithm with low complexity is proposed to alternately optimize the RIS topology and the precoding design. Particularly, a tabu search-based method is used to design the irregular RIS topology, and a neighbor extraction-based cross-entropy method is introduced to optimize the precoding design. Simulation results demonstrate that, subject to the constraint of limited RIS elements, the proposed irregular RIS can significantly enhance the system capacity.

Reconfigurable intelligent surface (RIS) has shown its potential in terahertz (THz) communications, due to its capability to expand coverage and compensate for the severe attenuation of THz signals. This paper investigates a large-scale RIS-assisted THz communications system in the near-field. Nevertheless, the beam squint effect of RIS, caused by the frequency-independent phase shifting circuit, results in severe array gain loss across the wide bandwidth. While true time delays (TTDs) that generate frequency-dependent phase shifts can mitigate beam squint, they often suffer from high power consumption. To address the drawback, we introduce a set of fixed true time delays (FTTDs) with low power consumption and low insertion loss for the RIS. These FTTDs, shared by the elements of RIS, can generate frequency-dependent phase shifts, thereby addressing the beam squint effect. To overcome the limitation of FTTDs being unable to change delays, we propose a dynamic architecture that consists of a switch network and two-layer phase shifters, allowing the elements of RIS to select the FTTDs. Subsequently, we analyze the theoretical array gain of the proposed FTTD-equipped RIS and determine the minimum number of FTTDs required for effective mitigation. Then, we formulate a problem of maximizing the achievable rate and propose a two-stage algorithm. Specifically, in the first stage, we obtain the optimal wideband beamforming design for both the BS and the RIS. In the second stage, we approximate this optimal design with the beamforming design from our proposed architecture. Finally, simulation results demonstrate that our proposed RIS design, with a small number of FTTDs, can achieve a near-optimal achievable rate and higher energy efficiency.

Ongoing discussions about the future of wireless communications are reaching a turning point as standardization activities for the sixth generation of mobile networks (6G) become more mature. New technologies must now face renewed scrutiny by the industry and academia in order to be ready for deployment in the near future. Recently, reconfigurable intelligent surfaces (RISs) gained attention as a promising solution for improving the propagation conditions of signal transmission in general. The RIS is a planar array of tunable resonant elements designed to dynamically and precisely manipulate the reflection of incident electromagnetic waves. However, the physical structure of the RIS and its components may be subject to practical limitations and imperfections. It is imperative that the hardware imperfections (HWIs) associated with the RIS be analyzed, so that it remains a feasible technology from a practical standpoint. Moreover, solutions for mitigating the HWIs must be considered, as is discussed in this work. More specifically, we introduce a gradient descent optimization for mitigating HWIs in RIS-aided wideband communication systems. Numerical results show that the proposed optimization is able to compensate for HWIs such as the phase-shift noise (PSN) and RIS surface deformations.

Reconfigurable intelligent surface (RIS) is thought to be a potential key technique for future wireless communications due to its ability for manipulating the electromagnetic environment smartly. This paper focuses on the rank and capacity analysis when a RIS is introduced into a multiple-input multiple-output (MIMO) system. By establishing a system model for this communication system, various simulations are conducted for identifying the characteristics of the channel. The simulations of different distance between the access point (AP) and user equipment (UE) show that the condition number of the channel is worsen when the distance increases and the role of the RIS in rank improvement is weaken. The spatial distributions of the reciprocal condition number of the channel which corresponds to the RIS locations are obtained and rank-deficient zones are found in different AP, UE and RIS configurations, which depicts the spatial characteristics of the RIS-assisted MIMO channel. The simulations also indicate that the condition number of the channel not only varies with the RIS location, but is also affected by the antenna array size and orientation of AP and UE. In addition, when the AP has a larger amount of antennas than UE, it is advantageous to place the RIS near the UE rather than the AP to achieve better channel condition. Modulation and coding schemes are applied in the simulations for comparison and capacity improvement is witnessed. Beneficial suggestions for RIS deployment in the MIMO system are concluded according to the simulation results.

Reconfigurable intelligent surface (RIS) has recently emerged as a promising technology for wireless communications, which intelligently controls the phase shift of each unit cell to form desired beams. Most prior works on RIS focus on a single frequency band, and thus for time-division duplexing (TDD) systems, the same phase shifts can be applied to both uplink and downlink. However, for the frequency-division duplexing (FDD) mode, if the same phase shifts are applied in both uplink and downlink, the directions of the uplink RIS beams will not be aligned with those of the downlink, which will in turn cause performance degradation. To address this issue, in this paper, we investigate the practical RIS design and optimization for FDD systems. By representing the reflection coefficients of RIS with the equivalent circuit model which includes the resistor, inductor and tunable capacitor, we first provide the guidelines on the circuit design to realize 2π phase control over the two frequency bands of the FDD system. In addition, we propose a low-resolution RIS configuration scheme with two tunable modes corresponding to two capacitances, and we formulate a max-min signal-to-noise ratio (SNR) problem to maximize the minimum SNR of uplink and downlink. To solve the non-convex problem, we propose an alternating optimization algorithm to obtain a suboptimal solution. Simulation results show that our proposed RIS design outperforms those benchmarks which design the circuits by only optimizing uplink or downlink.

Reconfigurable Intelligent Surface (RIS) is one of the promising techniques for 6G wireless communications, and recently has also been shown to be able to improve secure communications. However, there is a “double fading” effect in the reflection link between base station and user, thus passive RIS only achieves a negligible secrecy gain in typical communications scenarios. In this letter, we propose an active RIS-aided multi-antenna physical layer secrecy transmission scheme, where the active RIS can amplify the signal actively. Our aim is to minimize the transmit power subject to the constraint of secrecy rate. To solve the non-convex optimization problem, a penalty-based alternating minimization (AltMin) algorithm is proposed to optimize both the beamformer at the transmitter and the reflection matrix at RIS. Simulation results show that active RIS can resist the impact of “double fading” effect effectively, and is more energy efficient than passive RIS.

Aiming at simplifying the hardware structure and reducing the energy consumption in wireless communication via reconfigurable intelligent surfaces (RIS), this paper introduces a novel RIS design founded on the correlation between the phase shift values of the surface elements. First, a correlation analysis is conducted, considering the azimuth angle of a target device within a coverage region spanning from -80∘ to 80∘. The correlation is demonstrated for different deployment cases, creating the basis for the new RIS structure, termed Connected-RIS, where correlated elements are designed to share the same control signal. The fundamental performance of the proposed design is then analyzed in terms of control signals, power consumption, and communication system performance, comparing it to two RIS structures with full control: one with the same size as in the proposed design, and the other employing the minimum number of elements necessary to satisfy the fair coverage criterion. The correlation-based RIS design enables three-dimensional passive beamforming and significantly reduces the number of required load impedances and control signals, thereby lowering the hardware cost and simplifying the control circuitry. It also achieves substantial power savings as compared to the baseline schemes, while maintaining sufficient gain for a fair radio coverage. For instance, numerical simulations demonstrate that the proposed design reduces the power consumption by almost 86-92% and the control signals by 83-98% compared to operation with fully controlled RIS.

Reconfigurable intelligent surfaces (RISs) are an emerging technology for future wireless communication. The vast majority of recent research on RIS has focused on system level optimizations. However, developing straightforward and tractable electromagnetic models that are suitable for RIS aided communication modeling remains an open issue. In this paper, we address this issue and derive communication models by using rigorous scattering parameter network analysis. We also propose new RIS architectures based on group and fully connected reconfigurable impedance networks that can adjust not only the phases but also the magnitudes of the impinging waves, which are more general and more efficient than conventional single connected reconfigurable impedance network that only adjusts the phases of the impinging waves. In addition, the scaling law of the received signal power of an RIS aided system with reconfigurable impedance networks is also derived. Compared with the single connected reconfigurable impedance network, our group and fully connected reconfigurable impedance network can increase the received signal power by up to 62%, or maintain the same received signal power with a number of RIS elements reduced by up to 21%. We also investigate the proposed architecture in deployments with distance-dependent pathloss and Rician fading channel, and show that the proposed group and fully connected reconfigurable impedance networks outperform the single connected case by up to 34% and 48%, respectively.

Backscatter communication (BackCom) has gained increasing attention due to its low power consumption and low cost. However, its performance is severely degraded by the near-far effect due to the limited backscattering power at the tag side. In this paper, we consider two effective methods to solve the above problem by using the reconfigurable intelligent surface (RIS) and the relay. The objective is to maximize the received signal-to-noise ratio (SNR) subject to the transmit and circuit power control. To gain insight into both schemes, we derive a closed-form expression for the coverage radius of RIS-aided BackCom. Furthermore, considering the relay as a competing technology to the RIS, we compare their performance in terms of transmission performance, required transmit power, and quantify the number of reflecting elements needed for the RIS to outperform the relay. Simulation results demonstrate the RIS-aided BackCom with a sufficiently large number of elements achieves a superior performance, offers greater deployment flexibility, and is suitable for high self-interference levels.

This paper investigates a reconfigurable intelligent surface (RIS) aided communication scenario in which the RIS and the transmitter jointly send information to the receiver. While most studies of the RIS focus on its ability to beamform and boost the received signal-to-noise ratio (SNR), it is known that if the information data stream is also available at the RIS and can be modulated through the adjustable phases at the RIS, significant improvement in the multiplexing gain of the overall channel is possible. To achieve the theoretic multiplexing gain, this paper provides a symbol level precoding (SLP) approach for modulating data through the phases of the RIS. The SLP strategy is to set the received signal to be as close to the desired points in a constellation as possible. The idea is to directly make the received signal to be the symbols corresponding to the information to be transmitted and to design the transmitter and the reflective coefficients of the RIS so that the desired symbols are synthesized at the receiver. This strategy is formulated as an optimization problem and an augmented Lagrangian approach with the Riemannian conjugate gradient descent method is proposed to solve the optimization problem efficiently. Numerical simulation results show that the proposed method can achieve the theoretical multiplexing gain.

Recently, the reconfigurable intelligent surface (RIS) aided communication system has emerged as a promising candidate for future wireless communications. The existing channel estimation methods for RIS-aided millimeter-wave systems assume that the RIS is ideal without blockage. However, in practice, RIS may be blocked by the rain, snow, or dust, which will cause absorption and scattering of the incident/reflected signals and change the channel characteristics. In this paper, we formulate the system model of RIS-aided multi-user communications considering the blocked RIS. Then, we propose a variational Bayesian based channel estimation algorithm that is robust to RIS blockage, where we can simultaneously estimate the channel information and the RIS blockage. Simulation results demonstrate the superior performance of the proposed algorithm to existing ones.

We consider multi-antenna wireless systems aided by reconfigurable intelligent surfaces (RIS). RIS presents a new physical layer technology for improving coverage and energy efficiency by intelligently controlling the propagation environment. In practice however, achieving the anticipated gains of RIS requires accurate channel estimation. Recent attempts to solve this problem have considered the least-squares (LS) approach, which is simple but also sub-optimal. The optimal channel estimator, based on the minimum mean-squared-error (MMSE) criterion, is challenging to obtain and is non-linear due to the non-Gaussianity of the effective channel seen at the receiver. Here we present approaches to approximate the optimal MMSE channel estimator. As a first approach, we analytically develop the best linear estimator, the LMMSE, together with a corresponding majorization-minimization-based algorithm designed to optimize the RIS phase shift matrix during the training phase. This estimator is shown to yield improved accuracy over the LS approach by exploiting second-order statistical properties of the wireless channel and the noise. To further improve performance and better approximate the globally-optimal MMSE channel estimator, we propose data-driven non-linear solutions based on deep learning. Specifically, by posing the MMSE channel estimation problem as an image denoising problem, we propose two convolutional neural network (CNN)-based methods to perform the denoising and approximate the optimal MMSE channel estimation solution. Our numerical results show that these CNN-based estimators give superior performance compared with linear estimation approaches. They also have low computational complexity requirements, thereby motivating their potential use in future RIS-aided wireless communication systems.

Locally caching at base stations (BSs) can increase the network capacity as well as reduce the backhaul burden. However, it iremains an open problem whether it could increase energy efficiency (EE) for the future sixth‐generation (6G) network when the offloading link is in a deep fading condition. In order to analyse this problem, we attempt to analyse the energy efficiency of cache‐enabled wireless communication system assisted by reconfigurable intelligent surfaces (RIS), which is a new paradigm to increase EE. First, by considering the central limit theorem and Jensen's inequality, we obtain the approximated expression for EE when the number of RIS reflecting elements is very large. Secondly, due to the adoption of the RIS‐User/BS random association and a new BS‐RIS‐User wireless link, we analyse the performance between EE and the number of RIS reflecting elements under some condition. Finally, it is shown by full simulation that it is valid of our analysis, meanwhile, we gain the optimal cache capacity and the optimal transmission power of BS to maximize EE. Moreover, we show that the help of RIS can improve EE when the system is noise‐limited due to RIS can improve the signal power and has very low transmit power, but EE decreases in an interference‐limited system. Furthermore, we obtain the optimal number of BSs at the minimum EE in interference‐limited system, but EE decreases as the number of BS increases when the system is noise‐limited.

As a promising imaging technique, computational imaging is able to reconstruct the target by transmitting temporal-spatial incoherent signals. With the reconstructed image, a deterministic model of the scene can be established. However, in previous computational imaging systems, the transceiver is an antenna array and each antenna element needs to be modulated respectively, which requires many radio-frequency (RF) chains and complicated signal processing techniques. In addition, the traditional method uses colocated transmitting and receiving antenna which is not conducive to the flexibility of the system. Reconfigurable intelligent surface (RIS) is a revolutionary technology for achieving spectrum and energy efficient in future wireless communication systems. In this paper, we propose distributed computational imaging with reconfigurable intelligent surface. The transmitting antenna, receiving antenna and RIS are distributed in different spatial positions. By deploying reconfigurable intelligent surface, the phase and amplitude of the detecting signals are stochastically modulated by reconfigurable passive elements of RIS, which achieves high spectrum and energy efficiency with low hardware cost. Furthermore, we analyze four models of the detecting signal in detail and subdivide region of interest (ROI) into a collection of grids to formulate the reconstruction process as a sparse recovery problem. Simulation results demonstrate that the proposed distributed computational imaging with RIS can achieve outstanding imaging performance and reconstruct the target effectively.

In a device-to-device communication scenario, the number of far users (FUs) is higher than the near users (NUs) in each cluster. In such a scenario, the paired NUs and FUs communicate through non-orthogonal multiple access (NOMA), and the remaining FUs communicate through orthogonal multiple accesses (OMA) schemes. This system is called as hybrid-NOMA (HNOMA). This letter proposes a reconfigurable intelligent surface (RIS)-assisted HNOMA system to enhance the energy and spectral efficiencies of the conventional HNOMA. In this proposed RIS-assisted HNOMA (PR-HNOMA) system, the NU is paired with multiple FUs with allocations of different bandwidths. The PR-HNOMA system’s sum rate is maximized by considering joint beamforming at BS and RIS via semidefinite relaxation (SDR) and alternatively optimization (AO) techniques. Further, the sum rate of the PR-HNOMA under imperfect successive interference cancellation (ISIC) is analyzed and compared with different NOMA and OMA schemes. Simulation results show that the PR-HNOMA achieves better sum-rate performance than the conventional RIS assisted HNOMA (R-HNOMA), and RIS assisted NOMA (R-CNOMA), and OMA (R-OMA).