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 have 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.

Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfaces

“…The typical hardware implementation of RISs is a planar array of ultra-thin meta-atoms (also known as unit cells or elements), which have multiple digitalized states corresponding to distinct EM responses [22]. The meta-atoms are usually printed on a dielectric substrate and their digital control can be achieved by leveraging semiconductor devices, such as Positive-Intrinsic-Negative (PIN) diodes, Field-Effect Transistors (FETs), and Micro-ElectroMechanical System (MEMS) switches (e.g., varactor diodes) [126], [127].…”

“…At every time instant, CSI measurements need to be performed and collected by the controller. Leveraging the uplink/downlink channel reciprocity in time division duplexing systems, channel acquisition can be implemented by transmitting pilot signals from the UEs in the uplink and applying estimation techniques for the direct and the RIS-assisted channels at the BS and/or the controller sides [127]. With this information at the controller's disposal, the sum-rate maximizing phase profiles for the RISs and the BS precoding matrix are computed and shared with the involved devices.…”

Channel Estimation with Simultaneous Reflecting and Sensing Reconfigurable Intelligent Metasurfaces

“…A rich body of literature has examined the fabrication of solely reflective RISs using metamaterials. A variety of implementations have been recently presented in [6], [33], ranging from RISs that change the wave propagation inside a multi-scattering environment for improving the received signal, to those which realize anomalous reflection, such that the reflected beam does not follow Snell's law and is directed towards desired directions. More recently, RISs which simultaneously refract and relfect their impinging signals were proposed to offer 360 • coverage [34]- [36].…”

“…In order to investigate the capabilities of HRISs in facilitating multi-user wireless communications, we henceforth study the problem of channel estimation in RIS-empowered systems, being one of the main challenges associated with conventional almost passive and reflective RISs [19], [33]. In particular, we consider an uplink multi-user MIMO system, where a BS equipped with M antenna elements serves K single antenna UTs with the assistance of a HRIS, as illustrated in Fig.…”

Near-Field Hierarchical Beam Management for RIS-Enabled Millimeter Wave Multi-Antenna Systems

“…There exists already a remarkable amount of literature on Channel Estimation (CE) techniques for RIS-assisted wireless communications systems; see [10] for a recent overview. The two main CE categories, whose overhead does not scale with Q, exploit sparsity-and codebook-based schemes [11], [12].…”