Reconfigurable Intelligent Surfaces for Wireless Communications: Overview of Hardware Designs, Channel Models, and Estimation Techniques

Abstract

The demanding objectives for the future sixth generation (6G) of wireless communication networks have spurred recent research efforts on novel materials and radio-frequency front-end architectures for wireless connectivity, as well as revolutionary communication and computing paradigms. Among the pioneering candidate technologies for 6G belong the reconfigurable intelligent surfaces (RISs), which are artificial planar structures with integrated electronic circuits that can be programmed to manipulate the incoming electromagnetic field in a wide variety of functionalities. Incorporating RISs in wireless networks has been recently advocated as a revolutionary means to transform any wireless signal propagation environment to a dynamically programmable one, intended for various networking objectives, such as coverage extension and capacity boosting, spatiotemporal focusing with benefits in energy efficiency and secrecy, and low electromagnetic field exposure.Motivated by the recent increasing interests in the field of RISs and the consequent pioneering concept of the RIS-enabled smart wireless environments, in this paper, we overview and taxonomize the latest advances in RIS hardware architectures as well as the most recent developments in the modeling of RIS unit elements and RIS-empowered wireless signal propagation. We also present a thorough overview of the channel estimation approaches for RIS-empowered communications systems, which constitute a prerequisite step for the optimized incorporation of RISs in future wireless networks. Finally, we discuss the relevance of the RIS technology in the latest wireless communication standards, and highlight the current and future standardization activities for the RIS technology and the consequent RIS-empowered wireless networking approaches.

Reducing the Control Overhead of Intelligent Reconfigurable Surfaces Via a Tensor-Based Low-Rank Factorization Approach

“…R P , ] ∈ R P ×1 that contains the number of components for each factor for a certain P (with P = 3 in this case). As expected, when the number of components R or R (3) increases, the achievable data rate also increases, and we can observe that for the PARAFAC-IRS model with R = 16 and for the Tucker-IRS model with R 3 = [16,4,4], the proposed models achieves the optimum performance of the benchmark method [34].…”

IRS Phase-Shift Feedback Overhead-Aware Model Based on Rank-One Tensor Approximation

“…With the factorization, an additional complexity is introduced on the IRS controller, which will have to build the phase-shift vector, which in this case is given as s = s (3) ⊗s (2) ⊗s (1) ∈ C 1024×1 . Physically, the Kronecker product in (11) represents a summation of the factors phase-shifts, as shown in Figure 1 (b).…”

Efficient 3D Image Reconstruction for Near-Field Microwave Imaging Using Dynamic Metasurface Antenna

“…As mentioned in the previous sections, the introduced system does not provide the desired data acquisition rate due to numerous scanning points in the vertical direction and the need for many frequency samples. We address this issue by defining a minimization problem using CS theory and based on the relation extracted in (10) so that there is no need to collect numerous data at the Nyquist rate. Assume that s exhibits sparsity on a certain orthonormal basis .…”

“…Details of implementation are provided in Section III. By solving the above problem, s can be retrieved with much less data (according to CS theory), and then the scene image can be reconstructed by using (10). A summary of the main steps of implementing the proposed approach with the variables introduced above is given in the form of a block diagram in Fig.…”

“…Recently, dynamic metasurface antenna (DMA) has been proposed as an effective platform for computational microwave imaging [8], [9]. DMA contains a waveguide with subwavelength metamaterial elements called unit-cells [10]. The DMA can produce different radiation patterns even at a single frequency [9].…”