PaperlayerView exact revision on arXiv

Source-linked AI summary

Smart Radio Environments Empowered by Reconfigurable Intelligent Surfaces: How it Works, State of Research, and Road Ahead

Marco Di Renzo, Alessio Zappone, Merouane Debbah, Mohamed-Slim Alouini, Chau Yuen, Julien de Rosny, Sergei Tretyakov

arXiv:2004.09352v1cs.ITeess.SP

TL;DR

The paper addresses how wireless environments can become programmable and how communication theory can be reconciled with electromagnetic theory for RIS-enabled networks. It surveys RIS operation, physics-based metasurface modeling, applications, research progress, and open challenges, emphasizing that realistic designs must account for electromagnetic constraints, estimation overhead, and deployment scale.

  • Problem

    Wireless research needs a framework for replacing uncontrollable-environment assumptions with programmable environments while reconciling communication-theoretic models with electromagnetic theory.

  • Method

    The paper provides a comprehensive overview of RIS-enabled smart radio environments, including operating principles, applications, communication-theoretic and electromagnetic modeling, research progress, and open issues.

  • Results

    Jointly optimizing transmitters, receivers, and the environment can further improve point-to-point channel capacity, including by using RISs to encode and modulate additional information.

  • Takeaways & Limitations

    RIS analysis and design for wireless networks should use appropriate physics-based metasurface models and reconcile communication and electromagnetic perspectives.

Abstract

from arXiv · show

What is a reconfigurable intelligent surface? What is a smart radio environment? What is a metasurface? How do metasurfaces work and how to model them? How to reconcile the mathematical theories of communication and electromagnetism? What are the most suitable uses and applications of reconfigurable intelligent surfaces in wireless networks? What are the most promising smart radio environments for wireless applications? What is the current state of research? What are the most important and challenging research issues to tackle? These are a few of the many questions that we investigate in this short opus, which has the threefold objective of introducing the emerging research field of smart radio environments empowered by reconfigurable intelligent surfaces, putting forth the need of reconciling and reuniting C. E. Shannon's mathematical theory of communication with G. Green's and J. C. Maxwell's mathematical theories of electromagnetism, and reporting pragmatic guidelines and recipes for employing appropriate physics-based models of metasurfaces in wireless communications.

I. INTRODUCTION

Future wireless networks face fundamental limitations because conventional designs treat the wireless environment as fixed and uncontrollable. The paper introduces smart radio environments, enabled by reconfigurable intelligent surfaces, as a vision in which the environment becomes programmable alongside transmitters and receivers.

  • Motivation: Future wireless networks must support rising traffic, data rates, and heterogeneous services that current 5G designs were not designed to meet.Global IP traffic is forecast to grow by 55% annually between 2020 and 2030, reaching 5,016 exabytes, with data rates scaling to 1 Tb/s.
  • Motivation: Traditional wireless networks assume that the propagation environment is fixed, cannot be modified, and can only be compensated through transmitter and receiver design.
  • Motivation: Programming the wireless environment could improve performance by controlling radio-wave propagation rather than treating environmental losses and interference as unavoidable.A base station may transmit watts of power while user equipment detects microwatts, with much of the remaining power wasted through the environment.
  • Smart radio environments: A smart radio environment makes the wireless environment an optimization variable jointly controlled with transmitters and receivers; this paradigm is also called an intelligent radio environment or Wireless 2.0.
  • Reconfigurable intelligent surfaces: A reconfigurable intelligent surface is an inexpensive adaptive thin composite sheet that can be programmed to modify impinging radio waves after deployment.The paper presents RISs as technology enablers for programmable wireless environments, potentially covering walls, buildings, ceilings, and similar structures.
  • Reconfigurable intelligent surfaces: RIS operation generally alternates between environmental-information-based control and programming, followed by configured assistance during normal network transmission.

G. Macroscopic Functions of Reconfigurable Intelligent Surfaces

RIS macroscopic functions span elementary electromagnetic transformations and communication-level operations optimized for network objectives. These functions support programmable environments, near-field focusing, new transmitter designs, and higher communication capacity.

  • RIS unit cells and low-power configuration networks synthesize macroscopic radio-wave functions through microscopic design and control.Super-cells group jointly optimized unit cells into repeating structures that determine the surface period.
  • RIS functions are classified into EM-based transformations and communication-based operations optimized for objectives such as channel capacity.The former treats RISs as configurable black boxes, whereas the latter derives their functions through optimization.
  • 10,000 unit cells can support tens of bits per channel use with a single feeder, although the achievable rate depends on macro-cell configuration speed.
  • Communication-based RIS designs can implement modulation, multi-stream transmission, and joint encoding through configurable macro-cells.An RIS-based transmitter may encode information through macro-cell states and modulation symbols.
  • RISs can recycle existing radio waves without generating new electromagnetic signals, potentially reducing electromagnetic pollution and exposure in sensitive environments.The paper identifies hospitals as an example of an electromagnetically sensitive setting.
  • Large RISs may operate in the radiative near-field, enabling focused power delivery, customized propagation, and wireless networks with increased spatial capacity density.RISs can create EM-orthogonal communication links and move some multiple-antenna operations into the environment.
  • Smart radio environments treat the environment as a programmable network variable jointly optimized with transmitters and receivers.RISs shape impinging radio waves between transmission and reception rather than treating propagation as an uncontrollable channel.
  • Joint transmitter–RIS encoding generally yields better channel capacity than using RIS state only to customize the wireless environment.

A. Potential Applications of Reconfigurable Intelligent Surfaces in Smart Radio Environments

RISs can reshape wireless propagation to improve coverage, interference management, security, spatial multiplexing, focusing, localization, and simultaneous information-and-power transfer. Proposed smart radio environments apply these capabilities across infrastructure, vehicles, buildings, and other everyday settings, while requiring awareness of changing surroundings for optimal configuration.

  • RISs can create configurable non-line-of-sight links in dead zones or low-coverage areas.
  • RISs can steer signals toward intended locations while suppressing interference or degrading signals detected by eavesdroppers.
  • RISs can improve channel rank and conditioning, increasing spatial multiplexing capability and channel capacity in multiple-antenna systems.
  • Electrically large RISs can focus scattered waves into narrow spatial spots for dense deployments, high-precision localization, and targeted energy delivery.These capabilities support ranging, mapping, sensor recharging, and simultaneous information transfer.
  • RISs can encode sensed data into ambient radio waves, enabling low-power sensors to piggyback information without creating new radio transmissions.
  • Potential scenarios include smart cities, homes, buildings, factories, hospitals, campuses, undergrounds, stations, airports, vehicles, trains, airplanes, billboards, clothing, and glass.Applications include coverage, connectivity, exposure reduction, vehicle communications, wearable monitoring, and lower infrastructure or power requirements.
  • A truly smart radio environment must configure distributed RISs using awareness of complex and non-stationary surroundings to optimize communication performance and quality of experience.

IV. RISS IN WIRELESS NETWORKS – A MACROSCOPIC HOMOGENIZED COMMUNICATION-THEORETIC APPROACH

The paper develops a communication-theoretic approach for modeling metasurfaces and calculating their scattered electromagnetic fields. It connects microscopic unit-cell behavior with macroscopic surface models and uses diffraction-based field analysis within the two-dimensional electromagnetic regime of interest.

  • Modeling metasurfaces: The approach models metasurfaces macroscopically with continuous inhomogeneous functions that directly describe signal transformations on electromagnetic fields.
  • Modeling radio waves: The paper computes electromagnetic fields throughout a volume using diffraction theory and the Huygens-Fresnel principle.
  • Surface-field modeling and volume-field calculation are jointly needed to design, analyze, and optimize RISs in wireless networks.
  • Theoretical foundation: Surface electromagnetics provides the enabling discipline for modeling, analyzing, and synthesizing metasurfaces.
  • Electromagnetic regimes: The paper focuses on two-dimensional electromagnetic phenomena, represented through effective surface-averaged homogenized parameters.Circuit theory applies to zero-dimensional phenomena, transmission-line theory to one-dimensional phenomena, and Maxwell’s equations to three-dimensional phenomena.
  • Metasurface classes: Uniform, periodic, and quasi-periodic metasurfaces differ in how their properties or unit cells vary along tangential directions.Quasi-periodic surfaces retain a periodic lattice while allowing unit-cell geometry, size, orientation, or angle to differ.
  • Metasurface classes: Quasi-periodic metasurfaces offer more versatile electromagnetic-wave manipulation because their spatially varying unit cells act on incident waves across the surface.
  • Homogenization and equivalence: Homogenized surface models replace volumetric engineered structures with effective parameters, while surface equivalence represents sources using electric and magnetic currents on a thin enclosing layer.

3) Macroscopic Description of a Metasurface:

The paper develops macroscopic models for metasurfaces and distinguishes communication-oriented field reflection coefficients from design-oriented impedance-based coefficients. It connects analytical constitutive relations to synthesis, power-efficiency analysis, and practical wireless-network modeling.

  • Mathematical modeling: GSTCs provide equations for designing metasurfaces that impose specified transformations on incident, reflected, and transmitted electromagnetic fields.The same constitutive relations support performance analysis of wireless networks with metasurfaces.
  • Synthesis: Metasurface synthesis first identifies susceptibility matrices from desired electromagnetic-field transformations, then maps them to physical arrangements of unit cells.The design therefore has an electromagnetic specification stage and a physical-structure realization stage.
  • Power and efficiency: The parameter Ar controls the power efficiency and implementation complexity of perfect anomalous reflectors, including globally passive designs with unitary power efficiency.For specular reflection, Ar = 1 can produce a structure that is both locally and globally passive with unitary efficiency.
  • Reflection coefficients: REM(x) is the appropriate field reflection coefficient for wireless communications, whereas RZ(x) characterizes impedance mismatch for metasurface design and cannot generally represent input-output fields.The two coefficients coincide only for specular reflection; using RZ(x) as a field coefficient can yield reflected fields inconsistent with Maxwell’s equations and misleading power budgets.
  • Reflection coefficients: Surface-averaged REM(x) and RZ(x) are macroscopic parameters and should not be interpreted as the response of an individual unit cell.Their definitions use surface-averaged rather than local acting electromagnetic fields.
  • Approximations: Ray-optics reflection-coefficient approximations are generally accurate for low-to-medium reflection angles but can differ in amplitude and phase from physics-based results.The exact surface-averaged coefficients are complex quantities whose amplitude and phase need not match the ray-optics expression.

5) Analysis of a Metasurface:

The analysis models a given metasurface through homogenized surface fields and susceptibility or impedance relations, then derives explicit reflected and transmitted fields. A matrix formulation treats the metasurface as a black box mapping incident surface fields to scattered fields, while accounting for cross-coupling and modeling constraints.

  • Surface EM fields: The analysis computes reflected and transmitted electric and magnetic fields for specified incident fields when the metasurface and its surface susceptibilities are given.The procedure uses GSTCs and a matrix representation of the constitutive relations.
  • Explicit analytical formulation: The constitutive relations are rewritten in a single matrix form, and an SVD-based solution provides an explicit formulation for the surface EM fields.The matrices and pseudoinverse are used to solve the system governing the fields.
  • Explicit analytical formulation: For a given metasurface structure and incident EM fields, the unique pseudoinverse enables calculation of the corresponding reflected and transmitted EM fields.The matrix P depends on the metasurface structure but not on the incident, reflected, or transmitted fields.
  • Functional structure of the surface EM fields: The matrix P expresses each tangential component of the reflected and transmitted surface fields as a linear combination of incident electric and magnetic field components.The formulation is illustrated for perfect anomalous reflection and perfect anomalous transmission.
  • Examples: In both anomalous-reflection and anomalous-transmission cases, transmitted fields generally depend on both incident electric and magnetic fields, indicating cross-coupling among EM fields.The same general formalism covers both case studies.
  • “Black box” modeling of metasurfaces: A metasurface can be modeled as a black box whose input is the incident surface-averaged electric and magnetic fields and whose output is the corresponding surface-averaged fields.The coefficients account implicitly for mutual coupling among unit cells and coupling between phase and amplitude responses.
  • Relation between synthesis and analysis: The homogenized model is sufficient for calculating scattered EM fields, but synthesis and analysis remain tied to the assumed properties of the incident, reflected, and transmitted waves.If deployed waves differ from the design assumptions, the actual response and efficiency can differ from the desired response.

D. On Modeling Radio Wave Propagation in the Presence of Metasurfaces

The paper develops physics-based frameworks for modeling radio-wave propagation around metasurfaces, covering near- and far-field regimes, electrically large and small surfaces, and volume-field reconstruction from surface fields.

  • From surface EM fields to EM fields in volumes: The electromagnetic field at any point in a volume can be computed from knowledge of the fields on both sides of a metasurface at z = 0+ and z = 0−.This formulation connects surface descriptions to volumetric electromagnetic fields.
  • Reference operating regimes: Metasurface modeling must account for near-field and far-field operation because evanescent modes become negligible only beyond distances of a few unit-cell sizes.At those distances, a surface-averaged reflection coefficient can be used for the macroscopic structure.
  • Reference operating regimes: Electrically large and electrically small metasurfaces require sufficiently general signal models because far-field operation cannot be assumed a priori.Electrical largeness depends on the metasurface size and transmission distances relative to whether the edges are visible.
  • Theory of diffraction and Huygens-Fresnel principle: Diffraction theory and the Huygens-Fresnel principle provide the mathematical basis for obtaining the electromagnetic field at any volume point in the presence of a metasurface.Each point on the metasurface acts as a source of secondary wavelets whose fields determine the volume field.
  • Reference system model: The paper relates surface-field formulations to Green’s theorem and considers a simplified atomic case with a single non-zero reflection coefficient.The reference analytical treatment is restricted to a one-dimensional metasurface in a two-dimensional plane, while more general cases are noted separately.

1) Analytical Formulation of the EM Field:

The analytical formulation represents the reflected electromagnetic field as an integral over the metasurface, combining incident propagation, the homogenized surface response, re-emission, and angular factors.

  • Analytical formulation: The source-to-surface and surface-to-observation distances determine the transmission geometry in the field expression.d_T(x) and d_R(x) represent distances from the source to a metasurface point and from that point to the observation location.
  • Analytical formulation: The reflected field is formulated through an integral over the metasurface rather than a sum over individual scatterers.This follows from a homogenized model consistent with sub-wavelength spacing between adjacent unit cells and GSTCs.
  • Interpretation of the reflected field: The integrand combines the incident Green function, the homogenized metasurface response, the re-emitted Green function, and cosine factors for inclination angles.These terms separately represent impinging propagation, surface interaction, outgoing propagation, and angular dependence.
  • Beamsteering vs. focusing: By selecting the metasurface amplitude and phase profiles, the same analytical framework describes specular reflection, anomalous reflection, and focusing.The examples correspond respectively to specular beamsteering, anomalous beamsteering, and beamforming.

2) Configuration of the Metasurface:

Metasurface configuration determines the desired wave transformation: uniform phase responses support specular reflection, phase gradients steer reflection anomalously, and location-dependent phase profiles enable focusing.

  • Specular reflector: A uniform metasurface modifies the phase of an impinging wave to realize specular reflection.For a fixed phase shift φ0 = π, the example corresponds to a perfect electric conductor with reflection coefficient −1.
  • Anomalous reflector: A phase-gradient metasurface modifies both the reflection angle and phase of the impinging radio wave.The phase-gradient parameters are optimized to steer the wave toward a specified reflection angle.
  • Anomalous reflector: The anomalous-reflector phase gradient realizes the desired function, although its reflected-to-incident power ratio may not be optimized.The design is an approximation of an optimal reflector derived from ray-optics arguments.
  • Focusing lens: A focusing lens uses a phase-gradient response designed to focus the impinging wave at a specified location.The target location is represented by (x̄_R, ȳ_R).
  • Optimization criteria for beamsteering and focusing: Beamsteering and focusing use different optimization criteria: beamsteering selects stationary points of P(x), whereas focusing selects zeros of P(x).The focusing criterion makes the complex terms co-phased at the target location, while beamsteering targets the desired reflection angle.
  • Comparing metasurfaces obtained from different optimization criteria: Comparisons between metasurface functions can be unfair because designs may use different optimization criteria, channel-state information, and implementation complexities.A metasurface optimized for focusing need not be relevant for evaluating anomalous-reflector operation.

3) Electrically Large vs. Electrically Small Regimes:

Metasurface behavior depends on whether its electrical size and transmission distances place it in the electrically large or electrically small regime. The corresponding approximations produce different distance, angular, and size-scaling laws, so the operating regime must guide model selection.

  • Electrically large regime: In the electrically large regime, short transmission distances make the metasurface appear infinitely large, enabling an approximation based on a stationary point.The stationary point is the solution associated with the phase function used in the analytical formulation.
  • Electrically small regime: In the electrically small regime, long transmission distances permit a parallel-ray or plane-wave approximation across the metasurface.The resulting field depends on distances from the source and observation point to the metasurface center.
  • Comparing the two regimes: For a 28 GHz, 1.5-meter metasurface, the electrically large approximation is accurate for distances of the order of tens of meters, with its range depending on size and frequency.Larger surfaces and higher operating frequencies generally extend the distances over which this approximation is accurate.
  • Design insights and performance trends: In the electrically large regime, the metasurface behaves as a specular mirror and its reflected-field intensity follows a reciprocal-square-root distance scaling.The scaling agrees with the method of images and approximates geometric-optics propagation.
  • Design insights and performance trends: The electrically large regime retrieves the law of reflection at the stationary point, where the incidence and reflection angles coincide.This conclusion follows from the stationary-point relations for the transmission distances.
  • Design insights and performance trends: In the electrically small regime, the metasurface behaves as a scatterer or diffuser, with field intensity governed predominantly by source-to-center and center-to-observation distances.Its angular response is sinc-like, unlike the electrically large regime.
  • Design insights and performance trends: The field-intensity scaling with metasurface size differs between regimes: it is independent of 2Lx when electrically large and increases linearly with Lx when electrically small.This distinction supports consistency with the power conservation principle.
  • Beyond uniform metasurfaces: Nonuniform metasurfaces configured as anomalous reflectors or focusing lenses can exhibit different performance trends and require evaluation based on complexity and environmental information.A focusing lens is expected to maximize received power at a specified location.

E. Modeling and Analyzing Reconfigurable Intelligent Surfaces: Recipe for Wireless Researchers

The paper proposes a practical recipe for incorporating analytically tractable yet sufficiently accurate metasurface models into wireless-network signal and system models.

  • Recipe overview: The recipe distinguishes whether the metasurface structure is fixed in advance or optimized jointly with wireless-network performance metrics.These two cases organize the subsequent modeling procedures for wireless researchers.

1) The Metasurface Structure is Given:

When the metasurface structure is specified beforehand, researchers derive its electromagnetic representation from known fields and then evaluate network performance for optimization.

  • The Metasurface Structure is Given: Known incident, reflected, and transmitted electric and magnetic fields serve as the inputs for the fixed-structure modeling procedure.If surface susceptibilities are already available, the procedure can begin at the next step.
  • The Metasurface Structure is Given: Surface susceptibility functions are obtained from constitutive relations, and electromagnetic fields are represented through surface reflection and transmission coefficients.This connects the specified wave transformations to a tractable surface model.
  • The Metasurface Structure is Given: The Huygens-Fresnel formulation then provides electromagnetic fields at generic observation points within the considered volume.The analytical formulation must remain consistent with the input fields.
  • The Metasurface Structure is Given: The performance metric is formulated from the electromagnetic fields and used for system optimization.The procedure therefore links a prescribed metasurface transformation to a wireless-network objective.

2) The Metasurface Structure is Optimized:

When the metasurface structure is not predetermined, its surface susceptibilities are optimized through an electromagnetic model whose resulting performance metric identifies the corresponding structure.

  • The Metasurface Structure is Optimized: The optimized-structure case treats the surface susceptibility functions as unknowns selected to maximize metrics such as spectral efficiency or energy efficiency.The procedure begins by expressing the electromagnetic fields in terms of generic susceptibilities.
  • The Metasurface Structure is Optimized: The Huygens-Fresnel formulation maps the generic susceptibility-based field representation to electromagnetic fields at observation points.This supplies the field quantities needed to evaluate the wireless objective.
  • The Metasurface Structure is Optimized: The performance metric is expressed as a function of the generic surface susceptibilities, and optimization returns the susceptibilities and corresponding metasurface structure.The output is the optimized physical configuration associated with the chosen metric.
  • Modeling requirements: Appropriate realistic models for metasurfaces and radio-wave propagation are necessary for results consistent with the underlying electromagnetic behavior.The paper presents this as a general modeling requirement for wireless-network analysis.
  • Modeling approach: The paper focuses on macroscopic homogenized modeling through surface susceptibility functions and associated surface reflection and transmission coefficients.This modeling focus supports computational analysis and wireless-network optimization.
  • State of research: Earlier work demonstrated intelligent walls for rapidly controlling indoor coverage and spatial microwave modulators for passive shaping of complex microwave fields.These examples illustrate precursors and experimental directions related to programmable wireless environments.
  • State of research: Research on RIS-empowered smart radio environments expanded rapidly during 2015–2020, while simple but sufficiently accurate received-power models remained an open challenge.The paper situates its modeling recipe within a growing research area and an unresolved performance-analysis problem.
  • State of research: Information-theoretic analysis shows that jointly encoding information in transmitted signals and RIS configurations can achieve capacity, while fixed SNR-maximizing configurations are strictly suboptimal.The reported strategy also uses layered encoding for practical successive-cancellation-type decoding.

G. Channel Estimation

Channel estimation is a central challenge for nearly-passive RISs because their limited onboard processing requires new acquisition algorithms and protocols. Research therefore exploits channel sparsity, structured pilots, tensor or matrix factorizations, beam training, and optimized RIS patterns to reduce overhead or improve accuracy.

  • Channel-estimation challenges: Nearly-passive RISs require new channel-estimation algorithms and protocols because they have minimal onboard processing during normal operation.The design must also keep channel-estimation overhead low.
  • Estimation methods and results: RIS channel estimation spans cascaded-channel recovery, beam training, iterative factorization, discrete phase-shift designs, and joint estimation with rate optimization.Reported performance can depend on initialization quality in successive-refinement approaches.
  • Estimation methods and results: One-order-of-magnitude lower estimation variance is reported for RIS-element activation patterns based on the minimum variance unbiased estimation principle.The estimation phase mimics a series of discrete Fourier transforms.
  • Estimation methods and results: Low-SNR estimation accuracy improves over least-squares methods when constrained error minimization is solved using Lagrange multipliers and dual ascent.Cramer-Rao lower bounds are used for benchmarking.
  • Estimation methods and results: Three-phase protocols and massive MIMO can reduce the resources needed to estimate many channel coefficients in RIS-assisted uplinks.Related work also studies joint channel estimation and activity detection for massive connectivity.
  • Estimation methods and results: Channel sparsity and millimeter-wave structure are repeatedly exploited to reduce pilot or time-frequency overhead in RIS channel acquisition.Examples include compressed sensing, matrix factorization, and a low-complexity topology-aware method.

I. Stochastic Geometry Based Analysis

Stochastic-geometry analysis is used to study RIS deployment and environmental reflections at network scale, but modeling RIS-enabled reflections remains difficult. Existing work combines geometric object models with beamforming and resource-optimization methods, while emphasizing that estimation and feedback overhead must be included.

  • Stochastic-geometry modeling: Modeling reflections from environmental objects, under either conventional or generalized reflection laws, remains an open and challenging research issue.Current models commonly represent objects only as attenuators that create line-of-sight or non-line-of-sight links.
  • Stochastic-geometry modeling: A modified random line process models RIS-coated objects and yields the probability that a randomly distributed object acts as a reflector for a transmitter-receiver pair.This provides an analytical framework for RIS-enabled environmental reflections.
  • Stochastic-geometry modeling: Stochastic geometry has been applied to multi-RIS MIMO services, blockage modeling, blindspot ratios, user association, and large-scale RIS deployment in cellular networks.These studies target network-level outage, coverage, and deployment insights.
  • Resource optimization: Passive and active beamforming research commonly uses alternating maximization to jointly optimize transmitter beamforming and RIS phase shifts despite non-convexity.Other approaches include closed-form subproblems, semidefinite relaxation, successive convex approximation, and manifold optimization.
  • Resource optimization: Large-scale RISs can provide higher spectral and energy efficiency than increasing the base-station transmit antenna array, while reducing the number of transmitter and receiver antennas.The reported optimization methods are iterative and suboptimal or low-complexity.
  • Resource optimization: RIS resource optimization must jointly account for channel estimation and feedback because their overhead affects performance more fundamentally than in other wireless systems.Existing work often assumes channel state information has already been acquired.

K. Physical Layer Security

RISs are studied for both strengthening physical-layer security and enabling attacks, with performance depending on channel, network, and protocol conditions. Research mainly optimizes transmit processing and RIS phase shifts, while related NOMA and backscatter studies broaden the application scope.

  • Secure communications: RIS-assisted physical-layer security research studies secrecy-rate maximization or power minimization under eavesdropping, energy, and phase-shift constraints.Common solution methods include alternating optimization and semidefinite relaxation.
  • Secure communications: RIS-based secure-transmission designs extend to multiple eavesdroppers and artificial-noise-assisted systems.These works optimize combinations of transmit beamforming, artificial noise, and RIS phase shifts.
  • RIS-enabled security threats: An RIS-based jammer can outperform conventional active jamming without using internal energy to generate jamming signals.The jammer controls reflected signals to decrease the legitimate receiver’s SINR.
  • NOMA and backscatter: RIS-NOMA is reported to outperform RIS-OMA and conventional relaying, with performance improving as the number of reflecting elements increases.The comparison is reported in the cited RIS-NOMA frameworks.
  • NOMA and backscatter: Under an improved quasi-degradation condition and the same RIS phase-shift matrix, NOMA always outperforms zero-forcing and achieves the same performance as dirty paper coding.The cited work characterizes optimal beamforming for RIS-assisted NOMA and zero-forcing.
  • NOMA and backscatter: RIS-NOMA can enhance diversity order through more phase shifts, but NOMA may underperform OMA in some network configurations.RISs can also align user-channel directions, facilitating NOMA implementation.
  • NOMA and backscatter: RIS-aided backscattering supports low-power IoT connectivity by passively reflecting and modulating incident radio waves.The cited application studies secondary spectral efficiency subject to a primary-system spectral-efficiency requirement.

N. Aerial Communications

RISs support aerial, wireless-powered, millimeter-wave, and optical communications by shaping propagation and jointly optimizing surface configuration with placement or transmission resources. The reported studies address rate, coverage, energy harvesting, spectral efficiency, and outage under application-specific constraints.

  • Aerial communications: Joint UAV trajectory and RIS beamforming design is used to maximize average achievable rate despite the resulting non-convex optimization problem.The problem is divided into RIS-beamforming and trajectory subproblems.
  • Aerial communications: Building-mounted RISs can coherently direct reflections toward UAVs, with signal gain analyzed against UAV height, RIS size, altitude, and base-station distance.An optimal RIS location in height and distance is identified for maximizing performance.
  • Aerial communications: UAV-mounted RISs enable three-dimensional or panoramic reflections, while reinforcement learning can model propagation and maintain line-of-sight operation for mobile users.Some designs jointly optimize placement, phase shifts, beamforming, or UAV-carried RIS positioning.
  • Wireless power transfer: RISs improve throughput in cooperative wireless-powered communication by jointly optimizing phase shifts, transmission time, and power allocation.Related work also considers simultaneous wireless information and power transfer with optimized base-station precoding and RIS phases.
  • Optimization methods: Alternating manifold optimization is proposed for a coupled beamforming problem, and simulations report that it outperforms existing algorithms.The result is stated for the cited optimization problem.
  • Millimeter-wave and optical communications: Proper positioning of active antennas relative to an RIS considerably improves spectral efficiency in millimeter-wave massive MIMO systems.RIS-aided architectures are reported as energy efficient and scalable compared with large conventional arrays.
  • Millimeter-wave and optical communications: Multiple optical RISs can create artificial channels to improve optical-system performance and reduce outage probability under beam jitter, RIS jitter, and obstruction.The analysis explicitly accounts for these factors in the channel coefficients.
  • Programmable environments: Software-defined metamaterials use reusable software modules and embedded controllers to reconfigure RIS electromagnetic behavior at runtime.This capability is presented as a potential enabler for RIS-empowered smart radio environments.

U. Localization, Positioning, and Sensing

RISs are being investigated for positioning, localization, sensing, and mapping because their controllable propagation and sharp beams can improve spatial awareness. The paper also emphasizes that realistic near-field models, hardware validation, and deployment-scale analysis remain necessary.

  • Localization and positioning: RIS-based localization error decreases quadratically with surface area, except on the central perpendicular line, where it decreases linearly.The analysis compares deployments using one large RIS or multiple smaller RISs; neither configuration always outperforms the other.
  • Localization and positioning: RISs provide promising millimeter-wave MIMO positioning and tracking performance because they can realize very sharp beams.Theoretical positioning bounds and numerical results examine how the number of reflecting elements affects localization performance.
  • Sensing and channel characterization: RIS prototypes support environment-customized RF sensing, including posture recognition, using electrically controllable two-dimensional arrays.The reported posture-recognition prototype contains 2,304 controllable unit elements in a 69 cm × 69 cm × 0.52 cm structure.
  • Sensing and channel characterization: Experimental metasurface measurements show that path loss depends on surface size, applied function, and whether propagation occurs in the radiative near-field or far-field.The measurements used three manufactured metasurfaces across 4.25 GHz and 10.5 GHz test conditions.
  • Beyond communications: High focusing by electrically large RISs creates opportunities for high-precision radio localization and mapping, but performance across size, structure, and propagation regime remains open.The paper identifies localization and mapping as potential enablers for communication-related tasks.
Loading 2004.09352v1…