Reconfigurable Intelligent Surfaces for 6G and Beyond: A Comprehensive Survey from Theory to Deployment

Anis Amazigh Hamza; Iyad Dayoub; Nasrullah Armi; Prasetyo Putranto; Sameh Mabrouki

REVIEW 2 major objections 5 minor 1 cited by

Reconfigurable Intelligent Surfaces for 6G and Beyond: A Comprehensive Survey from Theory to Deployment

T0 review · 2 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read The paper claims that reconfigurable intelligent surfaces have matured enough that a single integrated survey—covering use cases, control mechanisms, channel sounding, channel estimation, and standardization—can serve as a unified…

desk verdict Useful, honest RIS survey that earns its place as a reference, but its channel-sounding “gap” claims rest on a non-systematic 2022–2024 selection. read the letter →

arxiv 2506.19526 v1 pith:XYWTRT5Y submitted 2025-06-24 eess.SP

Prasetyo Putranto , Anis Amazigh Hamza , Sameh Mabrouki , Nasrullah Armi , Iyad Dayoub This is my paper · ORCID

classification eess.SP

keywords reconfigurableintelligentsurfaces6GstandardizationchannelsoundingestimationRIScontrolmechanismsusecasesindustrialdeploymentsmartradioenvironment

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This survey sets out to show that reconfigurable intelligent surfaces (RIS) can be understood as one coherent technology rather than a scattering of isolated research threads. It knits together five strands: the use cases described by ETSI and the RISE-6G project, the control architectures that decide how much autonomy a surface has, the channel-sounding campaigns that measure how RIS behaves indoors and outdoors, the channel-estimation methods that recover the cascaded base-station-to-surface-to-user channel, and the standardization and industrial efforts preparing for 6G. A reader who takes the survey at face value comes away with a unified map of where RIS stands and what must happen before deployment. That matters because 6G standardization is approaching and RIS has not yet been standardized.

What carries the argument

The central object is the reconfigurable intelligent surface itself: a planar metasurface of unit cells whose impedance is tuned by diodes, varactors, liquid crystals, or other elements to shape reflected wavefronts via the generalized Snell's law. Throughout the survey, the load-bearing model is the cascaded channel: the BS–RIS–UE link composed of two sub-channels with a diagonal RIS reflection matrix, which all channel sounding and channel estimation techniques ultimately aim to characterize. Around this model, the survey builds its organizing taxonomies—control autonomy (fully controlled, partially controlled, fully autonomous), control channel (explicit in-band or out-of-band, implicit), channel estimation setup (cascaded versus separate), and RIS hardware (passive, active, hybrid).

What would settle it

A citation-level audit would settle the claim: if a search of the same literature base finds a 2022–2023 survey that already covers standardization and industry efforts in comparable depth, or finds key outdoor or mmWave channel-sounding experiments outside the 2022–2024 window that contradict the summary table, then the paper's central gap argument and its integrated-view contribution would be materially weakened.

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Extended reading notes

Core claim

The paper's central claim is that previous surveys cover RIS only in fragments, so that use cases, control mechanisms, channel sounding, and channel estimation live in separate literatures, while standardization and industrial perspectives are largely missing. By contrast, this survey claims to provide an integrated treatment: it organizes use cases from ETSI and RISE-6G, classifies control into fully controlled, partially controlled, and fully autonomous modes, tabulates 2022–2024 channel-sounding experiments, organizes channel estimation into conventional and machine-learning families, and traces RIS through ETSI, 3GPP, and ITU standardization timelines plus operator and vendor trials. If correct, it provides a single reference that researchers and industry can use to locate RIS within the 6G standardization process.

Load-bearing premise

The survey's added value rests on the assumption that its literature selection is representative—specifically that prior surveys really do omit standardization and industrial perspectives and that the 2022–2024 window captures the latest channel-sounding advances—so a biased or incomplete selection would weaken every gap claim and future direction drawn from it.

Editorial extensions

If this is right

If the survey's synthesis is right, the immediate 6G standardization window (3GPP Rel-20 study item) is the critical moment where RIS either enters the formal specification path or risks the fate of earlier over-engineered relay concepts.
Channel estimation research will need to shift from single-RIS passive setups toward high-mobility and multi-RIS scenarios, which the survey identifies as the least covered areas.
Outdoor and mmWave or sub-THz channel sounding remains too sparse to validate deployment-grade models, so early commercial RIS deployments will likely be indoor, outdoor-to-indoor, or controlled campus settings.
Control architecture will be a key standardization axis: the choice between fully controlled, partially controlled, and autonomous RIS determines signaling, backhaul, and computing requirements.
Machine-learning-based channel estimation, especially with hybrid RIS sensing elements, is a growing route to keep pilot overhead manageable.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The survey's staging argument suggests the most probable first standardized RIS form is a network-controlled repeater-like device rather than a fully autonomous metasurface; that is an extension, since the paper itself only presents the network-controlled repeater as a reference point.
Because the survey's path-loss and scattering-loss models assume Gaussian phase roughness and cascaded two-path attenuation, one could test whether those assumptions hold in outdoor dynamic environments; a meta-analysis of the campaigns in its sounding table could quantify model errors across frequency bands.
The classification of channel estimation by hardware type implies a testable trade-off: hybrid RIS with a few active sensing elements should achieve near-full channel-state-information performance at a fraction of active-RIS power, and a direct comparison of estimation error per watt across passive, hybrid, and active RIS would settle it.
The survey's control taxonomy could be turned into a standardization checklist that maps each use case to the required control channel type and autonomy level, which the paper does not explicitly provide.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

This is a solid survey, better than average for the genre. It delivers an integrated view of RIS use cases, control mechanisms, channel sounding, channel estimation, and standardization/industry. The standardization section (ETSI GRs, 3GPP timeline, ITU, national bodies) is genuinely useful and well-organized. Table III, the channel sounding summary, gives a compact at-a-glance comparison of 24 campaigns with setup, frequency, RIS size, tuning element, and substrate. That is a real service.

What is new here is not a result but consolidation: the comparative table of prior surveys, the 2022–2024 update on channel sounding, and the added industrial perspective. That is a legitimate increment over the prior surveys it builds on.

The stress-test note lands. Section III says the survey covers 2022–2024 because [56] covered earlier work, and the text uses that selection to claim outdoor and mmWave measurements are relatively limited. But there is no search strategy, inclusion criteria, or database coverage statement. With 24 entries and no audit trail, “comprehensive” is doing too much work. The scarcity claim might be true, but the survey does not establish it; it is an observation about a convenience sample. The same issue touches Table I, where eight surveys are selected and their “fragmented” nature is presented as fact. This is a moderate weakness: it does not invalidate the survey as a reference, but the authors should soften the gap claims or add a methodology paragraph.

Minor issues: Table IV has a duplicated entry ([175] appears twice), and one ETSI report is called “GS” instead of “GR.” Nothing load-bearing. The equations in Section III are attributed and internally consistent. The channel estimation taxonomy is clear, and the dense tables are usable. Citation patterns look fair.

This paper is for researchers entering RIS, for standardization people wanting a snapshot, and for anyone needing a quick map of channel sounding and estimation methods. It deserves a serious referee. The right outcome is probably acceptance after minor-to-moderate revision: add a methodology note to Section III, temper the “comprehensive” and gap language, and fix the typos. I would support that.

Referee Report

2 major / 5 minor

Summary. The paper is a survey of reconfigurable intelligent surface (RIS) technology for 6G, covering fundamentals and hardware taxonomies, use cases drawn from ETSI GR RIS 001 and the RISE-6G project, control mechanisms and control-channel taxonomies, RIS-assisted channel sounding campaigns from 2022–2024 with associated channel characterization models, channel estimation methods organized by architecture and technique, standardization activities in ETSI/3GPP/ITU and national bodies, and industrial perspectives from operators and vendors. It positions itself as an integrated survey that fills gaps left by prior surveys, particularly by updating channel sounding to 2022–2024 and by adding standardization and industrial viewpoints. The paper contains no new derivations, fitted parameters, or self-referential results; its claims are descriptive and grounded in the cited literature.

Significance. If the reviewed corpus is representative, the survey provides a useful unified reference for RIS researchers and for 6G standardization discussions. Its standardization section is a clear strength: it summarizes current ETSI GR reports, 3GPP release-level considerations, ITU activities, and concrete operator/vendor positions. The compilation of 23 recent channel-sounding experiments in Table III and the taxonomy of channel estimation approaches in Tables IV–VIII are also valuable organizing devices. The equations are attributed to cited sources and appear internally consistent, and there are no fitted parameters or new theoretical claims that would raise circularity concerns. The main caveat is that the survey's 'comprehensive' and 'latest advancements' claims rest on an undocumented selection of literature, so the value of the survey as an authoritative reference depends on a methodological transparency fix that is within the authors' control.

major comments (2)

[§III.A, Table III] The survey's central update claim—that the 2022–2024 window captures the latest channel-sounding advances and that outdoor and mmWave campaigns remain relatively scarce—rests entirely on the corpus assembled in Table III. However, no search strategy is documented: the text only states that the survey focuses on papers published between 2022 and 2024 because [56] covered earlier works. There is no list of databases, search strings, inclusion/exclusion criteria, or screening procedure, so a reader cannot audit whether omitted works would change the gap claims (for example, only [114] and [115] are exclusively outdoor, while [116]–[120] are mixed-environment campaigns). Please add a reproducible methodology subsection describing the literature retrieval and selection process, and either justify the corpus as complete or soften the 'comprehensive' and 'latest advancements' claims to match the actual selection.
[§I-A, Table I] The positioning claim that prior surveys are fragmented and leave standardization and industrial perspectives underexplored is supported by only eight selected surveys ([54]–[61]), with no documented procedure for identifying or selecting those surveys. This makes the differentiation claim difficult to verify, since other recent RIS surveys may already cover standardization or industrial aspects. Please either document how the comparison set in Table I was chosen or temper the framing to 'among the surveys we compared,' so that the novelty claim is not stronger than the evidence.

minor comments (5)

[§V-A1] The ETSI document is referred to as 'GS RIS 003' twice in this subsection, while the correct series designation used elsewhere in the paper is 'GR RIS 003'; please correct this typo.
[Table IV] Several reference numbers appear to be duplicated within the table: [175] appears twice in the single-RIS multi-user MIMO passive-RIS cell, [183] appears in both the conventional and the ML-based lists, and [148] appears in two MISO cells. The authors should verify the intended classification and remove unintended duplicates.
[§III.A] The opening sentence says indoor channel sounding was investigated in [97]–[111], but [112] is then listed among the indoor experiments in the following sentence; include [112] in the initial enumeration or rephrase the sentence.
[§III.B.1, Eq. (2)] The sentence defining θt and θr as the 'angle of arrival (AoA) from BS to RIS' and 'angle of departure (AoD) from RIS to BS' is confusing because the AoA is measured at the RIS and the AoD is measured at the RIS; please reword using standard incidence/reflection angle notation consistent with the cited source [119].
[§VI.C.2 and §IV.C] The heading 'Wideband Commmunication' contains a typo, and the phrase 'This factors challenge the accurate extraction' in §IV.C should read 'These factors challenge the accurate extraction.'

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the survey is a descriptive literature review with no derived predictions, fitted parameters, or self-referential results that reduce to its own inputs.

full rationale

This paper is a survey, not a derivation chain. It makes no novel quantitative predictions, fits no parameters, and presents no result that is defined in terms of its own conclusion. Every load-bearing claim, such as the comparison of prior surveys in Table I, the summary of channel sounding campaigns in Table III, and the identification of outdoor/mmWave measurement gaps, is presented as a descriptive summary of external literature with explicit references. Even where the survey chooses the 2022-2024 window for channel sounding because [56] already covered earlier works, that is a corpus-selection decision, not a circular reduction: the statement that outdoor experiments are relatively limited is based on the enumerated auditable list of 24 campaigns, and it remains externally falsifiable. The standardization and industrial perspectives are attributed to ETSI, 3GPP, ITU, and named companies, and no uniqueness theorem or prior result by the same authors is invoked to force a conclusion. The paper is self-contained as a literature review and does not manufacture its conclusions from its own assumptions. Hence the circularity score is 0.

Assumptions & free parameters 0 free parameters · 0 assumptions · 0 invented entities

This is a survey paper. It introduces no fitted parameters, no unproved axioms beyond the normal practice of literature synthesis, and no new postulated entities such as particles, forces, or dimensions. The equations in the paper are quoted from cited sources and used for exposition rather than as new derivations.

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Cite this review

Pith. "Pith review of Reconfigurable Intelligent Surfaces for 6G and Beyond: A Comprehensive Survey from Theory to Deployment." pith.science (2026). https://pith.science/paper/XYWTRT5Y

@misc{pith2026250619526,
  author       = {Pith},
  title        = {Pith review of: Reconfigurable Intelligent Surfaces for 6G and Beyond: A Comprehensive Survey from Theory to Deployment},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XYWTRT5Y}},
  note         = {Machine review of arXiv:2506.19526}
}

read the original abstract

As the wireless research community moves toward shaping the vision of sixth-generation (6G) networks, reconfigurable intelligent surfaces (RIS) have emerged as a promising technology for controlling the propagation environment. Although RIS has not yet been standardized, its versatile applications and enabling capabilities have attracted growing attention in both academia and industry. This survey presents a comprehensive review of RIS technology spanning theoretical foundations, design aspects, and practical deployment considerations. In contrast to existing surveys that focus on isolated aspects, this work offers an integrated view covering use cases, control mechanisms, channel sounding methodologies, and channel estimation strategies. Each of these topics is reviewed through the lens of recent literature, synthesizing the latest advancements to provide updated insights for both academic researchers and industry practitioners. It further addresses emerging topics such as standardization activities and industrial perspectives, which are often overlooked in prior literature. By bridging theoretical insights with practical challenges, this survey aims to provide a holistic understanding of RIS and support its evolution from a research concept toward real-world implementation.

Figures

Figures reproduced from arXiv: 2506.19526 by the authors.

**Figure 1.** Illustrations of (a) the generalized Snell’s law applied to a metasurface, [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗

**Figure 3.** Organization of the survey. research-oriented vision of the RISE-6G1project. Together, they provide a comprehensive overview of how RIS can be effectively integrated into future wireless systems. In 2023, ETSI published the first report (GR RIS-001 [17]) which specifies 11 key use cases of RIS which can be summarized as follows: 1) Coverage Enhancement: RIS can mitigate coverage gaps caused by obstacles, such as wal… view at source ↗

Figures from the paper (15 more)

**Figure 4.** Figure 4: Summary of category 1 of RIS use cases: enhanced connectivity and [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]

**Figure 5.** Figure 5: Summary of category 2 of RIS use cases: enhanced localization [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]

**Figure 7.** Figure 7: RIS operation taxonomy [94]. TABLE II RIS CONTROL CHANNEL TAXONOMY [88] Control Channel Description Explicit Out-of-band: Away from RIS main communication channel (by using different frequency), simpler design, but possibly lower spectral efficiency. In-band: Overlap…

**Figure 8.** Figure 8: A block diagram of an RIS-assisted channel sounding setup. [PITH_FULL_IMAGE:figures/full_fig_p009_8.png]

**Figure 9.** Figure 9: Categorization of channel estimation setup and RIS hardware [PITH_FULL_IMAGE:figures/full_fig_p014_9.png]

**Figure 10.** Figure 10: Illustration of cascaded and separate channel estimation in RIS [PITH_FULL_IMAGE:figures/full_fig_p014_10.png]

**Figure 11.** Figure 11: Two types of active RIS structures [217]. [PITH_FULL_IMAGE:figures/full_fig_p015_11.png]

**Figure 13.** Figure 13: Active double-RIS multiuser MIMO setup in [217]. [PITH_FULL_IMAGE:figures/full_fig_p015_13.png]

**Figure 14.** Figure 14: Various pattern of the active elements placement in hybrid RIS: (a) random-like [187], (b) single [152], (c) L-shape [188], (d) subarray [230]. [PITH_FULL_IMAGE:figures/full_fig_p016_14.png]

**Figure 15.** Figure 15: Categorization of channel estimation techniques in RIS-assisted [PITH_FULL_IMAGE:figures/full_fig_p016_15.png]

**Figure 16.** Figure 16: Schematic illustration for case in [131], [132]. [PITH_FULL_IMAGE:figures/full_fig_p021_16.png]

**Figure 18.** Figure 18: RIS-assisted high-speed railway communication in [216]. [PITH_FULL_IMAGE:figures/full_fig_p022_18.png]

**Figure 19.** Figure 19: Roadside RIS-assisted vehicular communication in [206] for (a) [PITH_FULL_IMAGE:figures/full_fig_p023_19.png]

**Figure 20.** Figure 20: Illustration of 3 types of RIS deployment scenario based on GR RIS 001: (a) outdoor (b) indoor (c) outdoor-to-indoor. [PITH_FULL_IMAGE:figures/full_fig_p025_20.png]

**Figure 21.** Figure 21: The possibility of RIS for the next standardization of 3GPP [283]. [PITH_FULL_IMAGE:figures/full_fig_p028_21.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

Geometry-Informed Optimization of Binary RIS Configurations for Communication and Sensing
cs.IT 2026-08 conditional novelty 4.0 of 10

Every optimal 1-bit RIS configuration aligns the channel vectors with a common direction, yielding an exact O(N^2) SISO algorithm and a structured MIMO sampling heuristic.

Reference graph

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RIS-Aided MIMO Systems With Hardware Impairments: Robust Beamforming Design and Analysis,

J. Wang, S. Gong, Q. Wu, and S. Ma, “RIS-Aided MIMO Systems With Hardware Impairments: Robust Beamforming Design and Analysis,” IEEE Transactions on Wireless Communications , vol. 22, no. 10, pp. 6914–6929, Oct. 2023

2023
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Spatially Correlated RIS-Aided Secure Massive MIMO Under CSI and Hardware Imperfections,

D. Yang, J. Xu, W. Xu, B. Sheng, X. You, C. Yuen, and M. D. Renzo, “Spatially Correlated RIS-Aided Secure Massive MIMO Under CSI and Hardware Imperfections,” IEEE Transactions on Wireless Communications, vol. 23, no. 9, pp. 11 461–11 475, Sep. 2024

2024
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Intelligent and Reliable Millimeter Wave Communications for RIS-Aided Vehicular Networks,

M. Alsenwi, M. Abolhasan, and J. Lipman, “Intelligent and Reliable Millimeter Wave Communications for RIS-Aided Vehicular Networks,” IEEE Transactions on Intelligent Transportation Systems , vol. 23, no. 11, pp. 21 582–21 592, Nov. 2022

2022
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Channel Estimation for XL-RIS-Aided Millimeter-Wave Systems,

X. Yu, W. Shen, R. Zhang, C. Xing, and T. Q. S. Quek, “Channel Estimation for XL-RIS-Aided Millimeter-Wave Systems,” IEEE Transactions on Communications, vol. 71, no. 9, pp. 5519–5533, Sep. 2023

2023
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CRB Minimization for RIS-Aided mmWave Integrated Sensing and Communications,

W. Lyu, S. Yang, Y . Xiu, Y . Li, H. He, C. Yuen, and Z. Zhang, “CRB Minimization for RIS-Aided mmWave Integrated Sensing and Communications,” IEEE Internet of Things Journal , vol. 11, no. 10, pp. 18 381–18 393, May 2024

2024
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Active Control of THz Waves in Wireless Environments Using Graphene-Based RIS,

S. Dash, C. Psomas, I. Krikidis, I. F. Akyildiz, and A. Pitsillides, “Active Control of THz Waves in Wireless Environments Using Graphene-Based RIS,” IEEE Transactions on Antennas and Propagation, vol. 70, no. 10, pp. 8785–8797, Oct. 2022

2022
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Resource Management for Multiplexing eMBB and URLLC Services Over RIS-Aided THz Communication,

H. Zarini, N. Gholipoor, M. R. Mili, M. Rasti, H. Tabassum, and E. Hossain, “Resource Management for Multiplexing eMBB and URLLC Services Over RIS-Aided THz Communication,” IEEE Transactions on Communications, vol. 71, no. 2, pp. 1207–1225, Feb. 2023

2023
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Performance Analysis of RIS-Aided THz Wireless Systems Over α-μ Fading: An Approximate Closed-Form Approach,

N. P. Le and M.-S. Alouini, “Performance Analysis of RIS-Aided THz Wireless Systems Over α-μ Fading: An Approximate Closed-Form Approach,” IEEE Internet of Things Journal , vol. 11, no. 1, pp. 1328–1343, Jan. 2024

2024
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Channel Estimation for RIS Assisted THz Systems With Beam Split,

X. Su, R. He, B. Ai, Y . Niu, and G. Wang, “Channel Estimation for RIS Assisted THz Systems With Beam Split,” IEEE Communications Letters, vol. 28, no. 3, pp. 637–641, Mar. 2024

2024
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RIS-Assisted Visible Light Communication Systems: A Tutorial,

S. Aboagye, A. R. Ndjiongue, T. M. N. Ngatched, O. A. Dobre, and H. V . Poor, “RIS-Assisted Visible Light Communication Systems: A Tutorial,” IEEE Communications Surveys & Tutorials , vol. 25, no. 1, pp. 251–288, 2023

2023
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Optimized Design of Joint Mirror Array and Liquid Crystal-Based RIS-Aided VLC Systems,

O. Maraqa and T. M. N. Ngatched, “Optimized Design of Joint Mirror Array and Liquid Crystal-Based RIS-Aided VLC Systems,” IEEE Photonics Journal , vol. 15, no. 4, pp. 1–11, Aug. 2023

2023
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Sum Rate and Fairness Optimization in RIS-Assisted VLC Systems,

S. Abdeljabar, M. W. Eltokhey, and M.-S. Alouini, “Sum Rate and Fairness Optimization in RIS-Assisted VLC Systems,” IEEE Open Journal of the Communications Society , vol. 5, pp. 2555–2566, 2024

2024
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Reconfigurable Intelligent Surface Enabled Over-the-Air Uplink NOMA,

E. Arslan, F. Kilinc, S. Arzykulov, A. T. Dogukan, A. Celik, E. Basar, and A. M. Eltawil, “Reconfigurable Intelligent Surface Enabled Over-the-Air Uplink NOMA,” IEEE Transactions on Green Communications and Networking , vol. 7, no. 2, pp. 814–826, Jun. 2023

2023
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On the Error Rate Performance of Full-Duplex Cooperative NOMA in Wireless Networks,

A. A. Hamza, I. Dayoub, I. Alouani, and A. Amrouche, “On the Error Rate Performance of Full-Duplex Cooperative NOMA in Wireless Networks,” IEEE Transactions on Communications , vol. 70, no. 3, pp. 1742–1758, Mar. 2022

2022
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Reconfigurable Intelligent Surface-Enabled Downlink NOMA,

A. T. Dogukan, E. Arslan, and E. Basar, “Reconfigurable Intelligent Surface-Enabled Downlink NOMA,” IEEE Transactions on Wireless Communications, vol. 23, no. 11, pp. 16 950–16 961, Nov. 2024. JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2021 32

2024
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Maximum Sum Rate of MCM-NOMA in Future Vehicular Sensor Networks,

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2023
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The Road to Next- Generation Multiple Access: A 50-Year Tutorial Review,

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2024
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Socially Aware V2X Networks With RIS: Joint Resource Optimization,

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2022
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Visible Light and Reconfigurable Intelligent Surfaces for Beyond 5G V2X Communication Networks at Road Intersections,

G. Singh, A. Srivastava, and V . A. Bohara, “Visible Light and Reconfigurable Intelligent Surfaces for Beyond 5G V2X Communication Networks at Road Intersections,” IEEE Transactions on Vehicular Technology, vol. 71, no. 8, pp. 8137–8151, Aug. 2022

2022
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Proximal Policy Optimization for RIS-Assisted Full Duplex 6G-V2X Communications,

P. Saikia, S. Pala, K. Singh, S. K. Singh, and W.-J. Huang, “Proximal Policy Optimization for RIS-Assisted Full Duplex 6G-V2X Communications,” IEEE Transactions on Intelligent Vehicles , vol. 9, no. 7, pp. 5134–5149, Jul. 2024

2024
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AI Empowered RIS-Assisted NOMA Networks: Deep Learning or Reinforcement Learning?

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2022
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Deep Learning-Based Time-Varying Channel Estimation for RIS Assisted Communication,

M. Xu, S. Zhang, J. Ma, and O. A. Dobre, “Deep Learning-Based Time-Varying Channel Estimation for RIS Assisted Communication,” IEEE Communications Letters , vol. 26, no. 1, pp. 94–98, Jan. 2022

2022
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Double Deep Learning for Joint Phase-Shift and Beamforming Based on Cascaded Channels in RIS-Assisted MIMO Networks,

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2023
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Multi- Agent Deep Reinforcement Learning for Beam Codebook Design in RIS-Aided Systems,

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2024
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Heuristic Algorithms for RIS-Assisted Wireless Networks: Exploring Heuristic- Aided Machine Learning,

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2024
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Deep-Reinforcement-Learning-Based AoI-Aware Resource Allocation for RIS-Aided IoV Networks,

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2025
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Active RIS Versus Passive RIS: Which is Superior With the Same Power Budget?

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2022
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Active RIS vs. Passive RIS: Which Will Prevail in 6G?

Z. Zhang, L. Dai, X. Chen, C. Liu, F. Yang, R. Schober, and H. V . Poor, “Active RIS vs. Passive RIS: Which Will Prevail in 6G?” IEEE Transactions on Communications, vol. 71, no. 3, pp. 1707–1725, Mar. 2023

2023
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Channel Estimation and Training Design for Active RIS Aided Wireless Communications,

H. Chen, N. Li, R. Long, and Y .-C. Liang, “Channel Estimation and Training Design for Active RIS Aided Wireless Communications,” IEEE Wireless Communications Letters , vol. 12, no. 11, pp. 1876–1880, Nov. 2023

2023
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Wireless Communication Aided by Intelligent Reflecting Surface: Active or Passive?

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2021
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Channel Estimation With Hybrid Reconfigurable Intelligent Metasurfaces,

H. Zhang, N. Shlezinger, G. C. Alexandropoulos, A. Shultzman, I. Alamzadeh, M. F. Imani, and Y . C. Eldar, “Channel Estimation With Hybrid Reconfigurable Intelligent Metasurfaces,” IEEE Transactions on Communications, vol. 71, no. 4, pp. 2441–2456, Apr. 2023

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Hybrid Reconfigurable Intelligent Metasurfaces: Enabling Simultaneous Tunable Reflections and Sensing for 6G Wireless Communications,

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2024
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Reconfigurable Intelligent Surfaces in Challenging Environments: Underwater, Underground, Industrial and Disaster,

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2021
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Channel Estimation With Reconfigurable Intelligent Surfaces-A General Framework,

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2022
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An Overview of Signal Processing Techniques for RIS/IRS-Aided Wireless Systems,

C. Pan, G. Zhou, K. Zhi, S. Hong, T. Wu, Y . Pan, H. Ren, M. D. Renzo, A. Lee Swindlehurst, R. Zhang, and A. Y . Zhang, “An Overview of Signal Processing Techniques for RIS/IRS-Aided Wireless Systems,” IEEE Journal of Selected Topics in Signal Processing, vol. 16, no. 5, pp. 8...

2022
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A Survey on Channel Estimation and Practical Passive Beamforming Design for Intelligent Reflecting Surface Aided Wireless Communications,

B. Zheng, C. You, W. Mei, and R. Zhang, “A Survey on Channel Estimation and Practical Passive Beamforming Design for Intelligent Reflecting Surface Aided Wireless Communications,” IEEE Communications Surveys & Tutorials , vol. 24, no. 2, pp. 1035–1071, 2022

2022
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Going Beyond a Simple RIS: Trends and Techniques Paving the Path of Future RIS,

K. Shafique and M. Alhassoun, “Going Beyond a Simple RIS: Trends and Techniques Paving the Path of Future RIS,” IEEE Open Journal of Antennas and Propagation , vol. 5, no. 2, pp. 256–276, Apr. 2024

2024
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Indoor Measurements for RIS-Aided Communication: Practical Phase Shift Optimization, Coverage Enhancement, and Physical Layer Security,

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2024
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Indoor mm-Wave Coverage Enhancement: Reconfigurable Intelligent Surface Deployment Strategy Based on Human Mobility Model,

H. Qin, Z. Liu, and C. Yang, “Indoor mm-Wave Coverage Enhancement: Reconfigurable Intelligent Surface Deployment Strategy Based on Human Mobility Model,” IEEE Communications Letters , vol. 26, no. 10, pp. 2475–2479, Oct. 2022

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RIS-Assisted Coverage Enhancement in Millimeter-Wave Cellular Networks,

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Multi-RIS Deployment Location Optimization for Coverage Enhancement in Outdoor Wireless Communication Networks,

Y . Ma, K. Ota, and M. Dong, “Multi-RIS Deployment Location Optimization for Coverage Enhancement in Outdoor Wireless Communication Networks,” IEEE Transactions on Vehicular Technology, vol. 73, no. 10, pp. 15 332–15 344, Oct. 2024

2024
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Energy and Spectral Efficiency Analysis for RIS-Aided V2V-Visible Light Communication,

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2023
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RIS-Assisted Spectral Efficiency Enhancement for Vertical Sectorization Network,

H. Lu, S. Zhuang, and H. Zhuang, “RIS-Assisted Spectral Efficiency Enhancement for Vertical Sectorization Network,” IEEE Wireless Communications Letters, vol. 14, no. 2, pp. 315–319, Feb. 2025

2025
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Intelligent Reflecting Surface Enhanced Wireless Network via Joint Active and Passive Beamforming,

Q. Wu and R. Zhang, “Intelligent Reflecting Surface Enhanced Wireless Network via Joint Active and Passive Beamforming,” IEEE Transactions on Wireless Communications , vol. 18, no. 11, pp. 5394–5409, Nov. 2019

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Reconfigurable Intelligent Surfaces for Energy Efficiency in Wireless Communication,

C. Huang, A. Zappone, G. C. Alexandropoulos, M. Debbah, and C. Yuen, “Reconfigurable Intelligent Surfaces for Energy Efficiency in Wireless Communication,” IEEE Transactions on Wireless Communications, vol. 18, no. 8, pp. 4157–4170, Aug. 2019

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Enhancing the Physical Layer Security of Two-Way Relay Systems With RIS and Beamforming,

Y . Zhang, S. Zhao, Y . Shen, X. Jiang, and N. Shiratori, “Enhancing the Physical Layer Security of Two-Way Relay Systems With RIS and Beamforming,” IEEE Transactions on Information Forensics and Security, vol. 19, pp. 5696–5711, 2024

2024
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Physical Layer Security Enhancement With Reconfigurable Intelligent Surface-Aided Networks,

J. Zhang, H. Du, Q. Sun, B. Ai, and D. W. K. Ng, “Physical Layer Security Enhancement With Reconfigurable Intelligent Surface-Aided Networks,” IEEE Transactions on Information Forensics and Security , vol. 16, pp. 3480–3495, 2021

2021
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RIS-Aided Localization Under Position and Orientation Offsets in the Near and Far Field,

D.-R. Emenonye, H. S. Dhillon, and R. M. Buehrer, “RIS-Aided Localization Under Position and Orientation Offsets in the Near and Far Field,” IEEE Transactions on Wireless Communications , vol. 22, no. 12, pp. 9327–9345, Dec. 2023

2023
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Multiple Anchors and RIS-Aided Localization Method in Complex NLOS Environments,

Z. Wu, Y . Li, X. Zhang, X. Meng, X. Lv, and Y . Wu, “Multiple Anchors and RIS-Aided Localization Method in Complex NLOS Environments,” IEEE Internet of Things Journal , vol. 11, no. 22, pp. 36 922–36 932, Nov. 2024

2024
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Phase Design and Near-Field Target Localization for RIS-Assisted Regional Localization System,

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2022
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User Sensing in RIS-Aided Wideband mmWave System With Beam-Squint and Beam-Split,

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2025
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Simultaneous Beam Training and Target Sensing in ISAC Systems With RIS,

K. Chen, C. Qi, O. A. Dobre, and G. Y . Li, “Simultaneous Beam Training and Target Sensing in ISAC Systems With RIS,” IEEE Transactions on Wireless Communications , vol. 23, no. 4, pp. 2696–2710, Apr. 2024

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Wireless Power Transfer Empowered by Reconfigurable Intelligent Surfaces,

L. Zhao, Z. Wang, and X. Wang, “Wireless Power Transfer Empowered by Reconfigurable Intelligent Surfaces,” IEEE Systems Journal, vol. 15, no. 2, pp. 2121–2124, Jun. 2021

2021
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Multiuser MISO PS-SWIPT Systems: Active or Passive RIS?

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Energy Harvesting Maximization for Reconfigurable Intelligent Surfaces Using Amplitude Measurements,

M. Tavana, M. Masoudi, and E. Björnson, “Energy Harvesting Maximization for Reconfigurable Intelligent Surfaces Using Amplitude Measurements,” IEEE Transactions on Communications, vol. 72, no. 4, pp. 2201–2215, Apr. 2024

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Wireless Energy Harvesting for Autonomous Reconfigurable Intelligent Surfaces,

K. Ntontin, A.-A. A. Boulogeorgos, E. Bj"ornson, W. A. Martins, S. Kisseleff, S. Abadal, E. Alarcón, A. Papazafeiropoulos, F. I. Lazarakis, and S. Chatzinotas, “Wireless Energy Harvesting for Autonomous Reconfigurable Intelligent Surfaces,” IEEE Transactions on Green Communica...

2023
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Power-Efficient Cooperative Communication Within IIoT Subnetworks: Relay or RIS?

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2025
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Traffic Aware Power Saving Communication Assisted By Double-Faced Active RIS,

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2023
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EMF-Aware Cellular Networks in RIS-Assisted Environments,

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2022
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EMF-Aware Energy Efficient MU-SIMO Systems With Multiple RISs,

S. Aghashahi, A. Tadaion, Z. Zeinalpour-Yazdi, M. B. Mashhadi, and A. Elzanaty, “EMF-Aware Energy Efficient MU-SIMO Systems With Multiple RISs,” IEEE Transactions on Vehicular Technology , vol. 73, no. 5, pp. 7339–7344, May 2024

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Energy Efficiency Optimization of Reconfigurable Intelligent Surfaces With Electromagnetic Field Exposure Constraints,

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2022
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RIS-Aided mmWave Network Planning Toward Connectivity Enhancement and Minimal Electromagnetic Field Exposure,

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On the use of intelligent metasurfaces in data centers,

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Deployment and control strategies of RIS based connectivity (Final Specifications),

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2023
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Control for RIS-based localisation and sensing (Final Specifications),

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2023
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Network architectures & deployment strategies with RIS for enhanced EE, EMFEU, and SSE (Final Specifications),

K. D. Katsanos, G. C. Alexandropoulos, D. T. PhanHuy, R. Ibrahim, Y . Yu, Y . Bénédic, P. Ratajczak, S. Jeong, M. D. Renzo, H. Guo, T. Svensson, R. Kotaba, P. Popovski, and M. Merluzzi, “Network architectures & deployment strategies with RIS for enhanced EE, EMFEU, and SSE (Fi...

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Reconfigurable Intelligent Surface-Assisted Localization: Technologies, Challenges, and the Road Ahead,

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Passive vs. Active Reconfigurable Intelligent Surfaces for Integrated Sensing and Communication: Challenges and Opportunities,

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A Survey on Integrated Sensing and Communication with Intelligent Metasurfaces: Trends, Challenges, and Opportunities,

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International Workshop on RIS - Feb, 22&23, 2024 Talk by Dinh-Thuy PHAN HUY (Orange, France),

“International Workshop on RIS - Feb, 22&23, 2024 Talk by Dinh-Thuy PHAN HUY (Orange, France),” France, Mar. 2024

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Uniform Analysis of Multipath Components From Various Scenarios With Time-Domain Channel Sounding at 300GHz,

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Channel Estimation for RIS-assisted Millimeter-wave MIMO Systems,

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A Novel Reconfigurable Intelligent Surface for Wide- Angle Passive Beamforming,

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A dual-band 3-bit reconfigurable intelligent surface with independent control of phases,

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