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

Mengnan Jian, George C. Alexandropoulos, Ertugrul Basar, Chongwen Huang, Ruiqi Liu, Yuanwei Liu, Chau Yuen

arXiv:2203.03176v1cs.ITeess.SP

TL;DR

RISs are being investigated for 6G because programmable electromagnetic surfaces could reshape wireless propagation, but their hardware, channel models, and channel-estimation methods require systematic treatment. This survey taxonomizes RIS architectures, reviews propagation and estimation models, and discusses standards and standardization activities. It synthesizes hardware designs, physics-based channel models, spatial-correlation findings, and reduced-overhead estimation strategies across the RIS literature.

  • Problem

    RIS research spans hardware, propagation modeling, channel estimation, and standardization, creating a need for an integrated overview of these developments.

  • Method

    The paper taxonomizes RIS hardware architectures and operation modes, reviews RIS unit-element and propagation models, surveys channel-estimation approaches, and examines standards.

  • Results

    The survey presents up-to-date coverage of RIS hardware, channel modeling, channel acquisition, and the relevance and standardization of RIS technology.

  • Takeaways & Limitations

    RISs are presented as a technology for dynamically programmable wireless environments, with channel estimation required for optimized incorporation into future networks.

Abstract

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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.

I. INTRODUCTION

RISs are proposed as programmable wireless-environment components for 6G, while this survey organizes advances in their hardware, channel modeling, estimation, and standardization.

  • Motivation: 6G research motivates RISs as artificial surfaces that electronically manipulate incoming electromagnetic fields for programmable wireless environments.Proposed objectives include coverage extension, capacity boosting, spatiotemporal focusing, energy efficiency, secrecy, and reduced electromagnetic exposure.
  • RIS concept: An RIS commonly uses many low-cost, nearly passive elements to apply controllable phase shifts without RF processing, amplification, filtering, or frequency conversion.This distinguishes RISs from massive MIMO, active beamformers, cooperative relaying, and backscatter communication.
  • Research landscape: RIS research has expanded across joint active and passive beamforming, channel modeling, performance analysis, physical-layer security, NOMA, and noncoherent modulation.These developments reflect rapidly increasing academic and industrial interest in RIS-enabled systems.
  • Research challenges: RIS channel models must capture propagation conditions, near-field effects, spatial correlation, mutual coupling, and the specific RIS architecture.Large indoor offices and street canyons can exhibit different path-loss and scattering behavior.
  • Research challenges: Nearly passive RISs cannot estimate parameters locally, making cascaded-channel estimation and dedicated signaling challenging as RIS size grows.Channel-state acquisition is particularly difficult in highly dynamic environments.
  • Survey scope: The survey taxonomizes RIS hardware architectures, reviews RIS channel models and estimation approaches, and discusses standards and future standardization activities.Its coverage spans hardware designs, operation modes, multi-user NOMA channel models, channel acquisition, and standardization.

II. RIS HARDWARE ARCHITECTURES

RIS hardware architectures differ mainly in whether the surface is active or passive, with passive designs emphasizing low power and active designs embedding energy-intensive RF processing.

  • Architecture overview: RIS structures are intended to operate as reconfigurable signal sources or wave collectors that make the electromagnetic response of distributed environmental objects controllable.This controllability is the distinctive hardware characteristic emphasized by the survey.
  • Active and passive RISs: Active RISs embed energy-intensive RF circuits and consecutive signal-processing units into the surface.They are described as an evolution of massive MIMO that packs software-controlled antenna elements onto a finite 2D surface.
  • Active and passive RISs: Passive RISs act as programmable mirrors or wave collectors whose low-cost, nearly passive elements reshape incident electromagnetic fields without dedicated power sources.Their circuitry and sensors may be powered through energy harvesting, potentially making them energy neutral.
  • Passive RISs: Passive RISs forward incoming signals without power amplifiers, RF chains, or sophisticated signal processing.They can operate in full duplex with low-rate control or backhaul connections and without significant self-interference or increased noise.

3) Discrete RISs:

Discrete RISs use individually controllable low-power unit cells, contrasting with contiguous apertures and supporting multiple approaches to electromagnetic-field manipulation.

  • Discrete RISs: Discrete holographic MIMO surfaces contain many discrete unit cells made from low-power, software-tunable metamaterials.Unit-cell tuning can use electronic components, liquid crystals, MEMS, electromechanical switches, or reconfigurable metamaterials.
  • Discrete and contiguous surfaces: Contiguous RISs integrate virtually infinite elements into a limited area to form a spatially continuous transceiver aperture.Their implementation and hardware differ essentially from discrete RISs.
  • Reflecting RISs: Passive smart-wireless-environment RISs rely on reconfigurable element reflections to manipulate incoming electromagnetic waves and achieve fine-grained beam control.Sub-wavelength meta-atoms are identified as favorable for quasi-free-space beam manipulation.
  • Reflecting RISs: In rich scattering, RISs manipulate many ray paths toward constructive interference or efficient field stirring rather than forming only a directive beam.Half-wavelength-sized meta-atoms can control more rays with a fixed amount of electronic components.
  • Implementation categories: RIS hardware taxonomies distinguish continuous and discrete implementations, as illustrated schematically in Fig. 1.The figure presents the two implementation categories without specifying performance differences.

2) Metasurface-Based Antennas:

Metasurface-based antenna architectures use dynamically configurable, waveguide-fed elements and simplified transceiver hardware for analog signal processing and channel acquisition.

  • DMA architectures: Dynamic metasurface antennas provide beam tailoring and analog processing of transmitted and received signals with simplified transceiver hardware.Compared with conventional antenna arrays, DMA architectures require less power and cost and can avoid complex corporate feeds or active phase shifters.
  • DMA architectures: A DMA can consist of multiple separate waveguide-fed element arrays, each connected to a single input/output port.Sub-wavelength spacing allows each port to feed many potentially coupled radiators.
  • Receiving RIS: A receiving RIS can connect all element outputs to one reception RF chain for explicit channel estimation at the RIS side.The chain includes a low-noise amplifier, RF-to-baseband mixer, and analog-to-digital converter.
  • Waveguide implementation: One-dimensional waveguides simplify analysis and make isolation between ports easier than in multiple-port two-dimensional waveguides.Microstrips are a common implementation of one-dimensional waveguides.

3) Receiving RISs:

Receiving RISs connect RIS-element outputs to reception hardware to support signal reception and channel estimation with few RF chains.

  • Receiving RISs: A DMA-based approach enables RIS baseband reception for explicit or implicit channel estimation with relatively few receive RF chains.The cited matrix-completion technique requires much fewer receive RF chains than RIS elements and relatively short training.
  • Receiving RISs: The receiving RIS architecture connects all RIS-element outputs to a single reception RF chain containing amplification, downconversion, and digitization.Training signals are sampled under one of M available RIS configurations selected by the random sampling unit.
  • Receiving RISs: A hybrid RIS design integrates sensing capability into a portion of the RIS structure.The supplied figure caption identifies the design as a hybrid RIS with integrated sensing capability.

4) Simultaneously Reflecting and Sensing RISs:

Hybrid RISs simultaneously perform programmable reflection and sensing by coupling each meta-atom to a waveguide and RF chain, though coupling prevents perfect reflection.

  • Simultaneously Reflecting and Sensing RISs: A hybrid meta-atom can programmably reflect part of an incident signal while feeding another part to a sensing unit.Waveguides couple to individual meta-atoms, and each waveguide can connect to an RF chain for local digital processing.
  • Simultaneously Reflecting and Sensing RISs: Waveguide coupling enables sensing but makes perfect reflection of the incident wave impossible.The coupling level determines how the received signal is divided between reflection and sensing.
  • Simultaneously Reflecting and Sensing RISs: The illustrated hybrid RIS uses mushroom structures loaded with varactor diodes to reconfigure resonance frequency and effective impedance.The element was selected to provide high reflectivity with reconfigurable phases and supports independent meta-atom addressing.

5) Simultaneously Transmitting and Reflecting RISs:

STAR-RISs, also called intelligent omni-surfaces, simultaneously transmit and reflect incident signals to create a full-space reconfigurable wireless environment.

  • Simultaneously Transmitting and Reflecting RISs: STAR-RIS elements simultaneously reflect and transmit incident wireless signals, supporting 360-degree coverage.The concept is also referred to as the intelligent omni-surface.
  • Simultaneously Transmitting and Reflecting RISs: A STAR-RIS element represents the incident, reflected, and transmitted signal phases as φ0, φR, and φT, respectively.These phases describe the phase relationships at the surface.
  • Simultaneously Transmitting and Reflecting RISs: The tunable surface impedance controls both the reflecting phase φR and the transmission phase φT.This provides joint phase control over the two propagation components.
  • Simultaneously Transmitting and Reflecting RISs: Simultaneous reflection and transmission require each element to support both electric and magnetic currents.Reported prototypes include a transparent dynamic metasurface and PIN-diode-enabled STAR-IOS designs.

6) Amplifying RISs:

Amplifying RISs incorporate a power amplifier to increase reflected-signal strength and address double path loss and passive-reflection losses.

  • Amplifying RISs: An amplifying RIS design incorporates a single power amplifier to enable reflection amplification.Its motivation is to address the double path loss problem and lossy reflection associated with passive reflective RISs.
  • Amplifying RISs: Passive RISs perform efficiently only when positioned close to terminals, according to the discussion motivating amplifying RISs.The cited passage states that passive RISs otherwise remain a supportive technology for future wireless communication systems.

C. Fabrication Methodologies

RIS fabrication spans optical- and microwave-oriented techniques and produces continuous or discrete apertures. Continuous designs use varactor-controlled impedance patterns, while discrete software-defined metasurfaces integrate sensing, actuation, shielding, computing, and communication layers.

  • RIS fabrication uses electron beam lithography, focused-ion beam milling, interference and nanoimprint lithography, direct laser writing, and printed circuit board processes.
  • Fabrication techniques produce two typical aperture classes: approximately continuous microwave apertures and discrete apertures.
  • Continuous RISs use independently controlled varactor bias voltages to program surface impedance and manipulate reflection phase, amplitude, and phase distributions.
  • Discrete apertures are commonly realized as software-defined metasurface antennas with metamaterial, sensing and actuation, shielding, computing, and interface-communications layers.
  • Greenerwave prototypes demonstrate basic feasibility, while the RISE-6G project targets low-power, cost-effective RIS hardware from below 6 GHz through mmWave and sub-THz frequencies.

III. RIS-EMPOWERED SIGNAL PROPAGATION MODELING

RIS signal-propagation modeling spans stochastic, deterministic, physics-based, and application-specific formulations that capture propagation geometry, element behavior, polarization, spatial correlation, and mutual coupling. These models reveal regimes where conventional assumptions or beamforming can be inadequate and support channel and precoding analysis.

  • Channel modeling supports RIS technology validation by characterizing far-field and near-field propagation, path loss, scattering, and RIS-specific effects.
  • RIS propagation models include stochastic cascaded channels, two-path direct-and-assisted propagation, and physics-based tiled models incorporating polarization.
  • Tiled RIS models use offline unit design and online transmission-mode selection to maximize a desired quality-of-service criterion.
  • RIS channel formulations account for arbitrary array sizes, varying element distances, polarization mismatch, effective areas, mutual coupling, and coupled phase-amplitude responses.
  • Free-space optical models use the Huygens-Fresnel principle to capture RIS size, position, orientation, and phase-profile effects on reflected fields and end-to-end channels.
  • Typical THz RISs likely operate in the Fresnel zone, where spherical-wave modeling is needed and conventional beamforming is suboptimal, potentially reducing power gain.
  • RIS channel fading is always spatially correlated; although the asymptotic SNR limit equals that of independent models, convergence rates and covariance-matrix ranks differ.
  • Spatially correlated channel models support maximum-ratio transmission and zero-forcing precoding analyses whose sampled channel variance depends on patch-antenna number and spacing.

IV. CHANNEL ESTIMATION IN RIS-EMPOWERED SYSTEMS

RIS channel estimation must recover multiple coupled channels under high dimensionality, dynamic conditions, hardware nonidealities, and frequency-dependent propagation effects. The surveyed approaches reduce this burden through structured training, sparsity, channel splitting, grouping, hybrid sensing, and AI-based estimation.

  • Estimation challenges: Accurate CSI requires estimating the direct BS–UE channel together with the BS–RIS and RIS–UE channels.Nearly passive RISs cannot estimate parameters locally, so the cascaded transmitter–RIS–receiver channel must be inferred.
  • Overhead reduction: RIS channel dimension increases sharply, making short-pilot and low-overhead channel estimation and feedback central design objectives.Grouping adjacent elements, hierarchical training, and estimating aggregated channels can reduce pilot requirements.
  • Training design: Training performance depends on RIS reflection patterns, with optimal activation and jointly designed training sequences formulated to reduce channel-estimation error.Other methods use channel splitting, ON/OFF activation, MMSE or least-squares estimation, and minimum-variance criteria.
  • Channel structure: Channel estimation exploits scenario structure, including sparse millimeter-wave channels, shared BS–RIS components, angular sparsity, and differing coherence times.For facade deployments, the BS–RIS channel can be estimated less frequently than the more mobile RIS–UE channel.
  • High-frequency effects: High-frequency spatial-wideband effects and large RIS apertures can create element-dependent delays, dispersion, and estimation-accuracy degradation.These effects are especially relevant in millimeter-wave and THz systems.
  • Nonideal hardware: Practical estimation must account for discrete phase shifts, hardware impairments, and phase noise because ideal-RIS assumptions do not hold in applications.The surveyed work includes iterative least-squares and linear LMMSE estimators, while reporting that transceiver impairments can limit estimation at high SNR.
  • Algorithm selection: AI-based channel-estimation schemes use massive channel-measurement data and achieve good performance with acceptable complexity, supporting their future development.The paper presents complexity analysis as a basis for selecting algorithms in practical system design.

V. RIS STANDARDIZATION: CURRENT STATUS AND ROAD AHEAD

RIS standardization has begun mainly through regional organizations, while global bodies are considering how RIS channel modeling, use cases, and deployment scenarios could enter future standards. The paper outlines alternative 3GPP paths and recommends beginning with simple, practical scenarios.

  • Current regional status: Regional RIS standardization is already underway, particularly through China’s FuTURE forum and CCSA.FuTURE established a working group in December 2020, while CCSA approved a RIS study item targeting a technical report.
  • Current regional status: ETSI work items were progressing toward draft reports in mid-2022 and stable reports by the end of 2022, with normative specifications considered after 2023.The passage describes the ISG’s initial focus as studies and informative reports.
  • Global standards bodies: RISs were identified as critical physical-layer components for next-generation networks in an ITU-R future-technology trends process, while 3GPP activity had begun by March 2021.The cited passage connects regional standardization efforts with global standards-development organizations.
  • 3GPP pathways: A possible 3GPP route is a Release 19 study item on use cases, deployment scenarios, and channel models followed by a work item.This route would allow RISs to be incorporated as a component of 5G standards and thereby inherited by 6G standards.
  • 3GPP pathways: An alternative is to standardize RISs directly as part of 6G standards alongside other new 6G features.The paper presents this as the second possible 3GPP approach.
  • Recommended study scope: Standardization studies should begin with simple scenarios and practical models, such as fixed RIS panels and prioritized single-carrier operation.These choices avoid mobility issues and help stabilize network topology during early studies.

VI. CONCLUSIONS

The paper surveys RIS hardware architectures, propagation modeling, channel estimation, and standardization as research and development around RISs expands across academia and industry. It positions these topics as foundations for future RIS-enabled wireless networks.

  • Conclusion: RIS research and development is expanding across antenna design, metamaterials, electromagnetics, signal processing, and wireless communications.The paper links this activity to recent proof-of-concept systems including passive reflectarrays and dynamic metasurface antennas.
  • Conclusion: The paper overviews RIS hardware architectures, operational considerations, RIS propagation models, and channel-estimation approaches.It identifies channel estimation as a prerequisite for optimized incorporation of RISs into future wireless networks.
  • Conclusion: The paper discusses RIS relevance to current standards and highlights ongoing and future standardization activities for RIS-enabled wireless networking.
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