THz-Band Near-Field RIS Channel Modeling for Linear Channel Estimation

Ahmad Dkhan; Hadi Sarieddeen; Ibrahim Abou-Faycal; Simon Tarboush; Tareq Y. Al-Naffouri

REVIEW 4 major objections 4 minor 23 references

THz-Band Near-Field RIS Channel Modeling for Linear Channel Estimation

T0 review · 4 major / 4 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read Near-field effects and mutual coupling sharpen THz RIS channel estimation by several dB.

desk verdict The dB gains from near-field correlation and mutual coupling are plausible, but the paper's correlated-MG construction is distributionally invalid, so its theoretical PDF and physical-consistency claims don't hold as written. read the letter →

arxiv 2505.09767 v1 pith:N5H7M6TA submitted 2025-05-14 eess.SP

Ahmad Dkhan , Simon Tarboush , Hadi Sarieddeen , Ibrahim Abou-Faycal , Tareq Y. Al-Naffouri This is my paper · ORCID

classification eess.SP

keywords terahertzcommunicationsreconfigurableintelligentsurfacemixturegammafadingnear-fieldspatialcorrelationmutualcouplingchannelestimationLMMSEmassiveMIMO

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 paper argues that a THz-band RIS-aided channel model that keeps near-field spherical wavefronts, spatial correlation, and mutual coupling at the base station, while describing small-scale fading with the mixture gamma distribution, leads to materially better linear channel estimation than models that drop these effects. It derives the distribution of the cascaded user-RIS-BS channel and plugs it into least-squares and linear minimum mean-square error estimators. The authors report that near-field modeling improves estimation accuracy by up to 3 dB over far-field modeling, and that ignoring mutual coupling and spatial correlation costs roughly 8 dB at high SNR. If correct, physically consistent THz channel models should include these effects rather than rely on conventional planar-wave independent fading.

What carries the argument

The carrying object is the Kronecker-correlated mixture-gamma channel model, where a channel vector is written as $h = R^{1/2} \tilde{h}$ with $\tilde{h}$ IID MG fading and $R$ the spatial correlation matrix from a near-field multi-ring geometry (von Mises angular spread, spherical distances, absorption). A second piece is the mutual coupling transformation $M^{1/2} R_{BR} M^{1/2}$ with $M = (Z + r_l I)^{-1}$. The cascaded RIS channel is the entrywise product of two such MG vectors, whose PDF is obtained via Gaussian-Laguerre quadrature, providing the covariance $R_{cc}$ used by the LMMSE estimator.

What would settle it

Run a Monte Carlo simulation that generates channel vectors as $R^{1/2} \tilde{h}$ from independent MG coefficients and compare the empirical histogram of the resulting entries with the MG PDF used to derive the cascaded distribution (20); if the histograms disagree, the theoretical covariance and NMSE curves in the paper do not describe the simulated channel.

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Extended reading notes

Core claim

The central claim is that accounting for near-field spatial correlation, mutual coupling, and mixture-gamma small-scale fading in an RIS-aided THz link yields several decibels of channel-estimation gain over conventional far-field uncorrelated models. The paper constructs a geometry-based stochastic channel with multi-ring scatterers, spherical wave distances, molecular absorption, and a Kronecker correlation matrix, then expresses the cascaded channel entries as products of MG random variables. It derives the PDF of that product using Gaussian-Laguerre quadrature and uses the resulting covariance in LMMSE estimation. Simulations at 142 GHz with 128-element arrays show that near-field correlation improves NMSE by up to 3 dB, and that neglecting both mutual coupling and spatial correlation degrades performance by about 8 dB.

Load-bearing premise

The model assumes in Eq. (16) that multiplying a vector of independent mixture-gamma fading coefficients by the square root of the correlation matrix R produces a channel whose entries are still mixture-gamma with the specified marginal; unlike Gaussian vectors, a linear combination of Gamma mixtures is generally not Gamma, so this step is not automatically valid.

Editorial extensions

If this is right

A THz RIS channel estimator built on near-field correlated MG statistics should beat one built on far-field planar assumptions by several dB in ultra-massive arrays.
Mutual coupling at the BS must be included; neglecting it and spatial correlation costs about 8 dB at 20 dB SNR in the paper's configuration.
The MG shape parameter changes channel variance and therefore estimator MSE, so fading severity must be matched to measurements.
The same covariance-based LMMSE structure can be reused once the cascaded channel covariance is computed from the correlated MG model.

Reading between the lines

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

The Kronecker square-root construction in Eq. (16) likely does not preserve the MG marginal distribution, so the theoretical PDF (20) may be approximate or mismatched to the simulated channel; validating or replacing this step is a direct next test.
Since MC on the RIS side is neglected, the authors' stated gains are a lower bound for tightly integrated active RIS surfaces, where MC is stronger; extending the model to the RIS reflection matrix is a natural continuation.
The multi-ring von Mises correlation model could be adapted to estimate the MG parameters themselves from measured arrays, turning the model into a fitting tool rather than a fixed simulation.

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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

Let me give you the short version: if you're looking for a model that quantifies the impact of near-field correlation and mutual coupling on THz RIS channel estimation, the numerical trends here are probably right, but the paper's statistical derivation is wrong. The authors start with an IID mixture-Gamma vector and multiply it by R^{1/2} to introduce correlation, then treat the output as MG-distributed. That is not true for Gamma mixtures—a linear combination of independent MG variables is not MG in general. So the derivation of the cascaded PDF in (20), which relies on the product of two MG variables from [16, Theorem 1], does not describe the channel they actually simulate. The Monte Carlo setup is also under-specified, so we can't tell what distribution the generated channels have.

That said, the paper is not a throwaway. It combines several pieces that haven't been put together before: MG fading for THz, near-field spherical-wave correlation from [20], mutual coupling at the BS, and LS/LMMSE estimation. The authors use measurement-based MG parameters, and the comparisons—near-field vs far-field, with vs without MC—are clearly presented and align with other results in the literature. Importantly, the covariance-based estimators (LMMSE and LS) only depend on second-order statistics. If the simulated channel is generated with the correct covariance R, the theoretical NMSE curves for those estimators would still match, even though the marginals are not MG. So the headline dB gains may survive the flaw; what dies is the 'physically consistent MG modeling' narrative and the closed-form cascaded PDF.

Another oddity: the MG distribution is for the fading magnitude, but the channel in (9) is complex. The Kronecker expression h = R^{1/2} \tilde h with MG \tilde h looks like it ignores phase altogether. The paper never explains how complex envelopes and phases enter the simulation. This needs to be fixed or clarified.

If I were the editor, I'd send this to reviewers. The topic is timely, the model is a natural extension, and the numerical results could be salvageable if the authors either generate correlated MG exactly (via a copula or by mixing correlated Gamma components) or explicitly restrict their claims to covariance-based analysis. It's a conditional accept in its current form.

Referee Report

4 major / 4 minor

Summary. The paper proposes a THz-band RIS-aided channel model in which small-scale fading follows the mixture gamma (MG) distribution, with near-field spatial correlation, mutual coupling at the base station, molecular absorption, and spherical wave propagation. The authors construct correlated MG vectors via a Kronecker square-root transformation, derive a cascaded RIS channel distribution using a product-of-MG result from prior work, and evaluate LS and LMMSE channel estimators. Numerical results claim that accounting for near-field correlation and mutual coupling improves estimation accuracy by several dB compared with models that neglect these effects.

Significance. If the modeling and derivations were correct, the paper would provide a useful framework for THz RIS channel estimation in ultra-massive arrays, combining measured MG fading parameters with near-field correlation and mutual coupling. A strength is the use of measurement-based MG parameters from prior work and the incorporation of established mutual-coupling and von Mises angular-spread models. However, the central distributional construction in Eq. (16) is not justified for MG variables, and the Monte Carlo generator is not specified, so the quantitative performance claims are not substantiated as written.

major comments (4)

[Sec. III-B, Eq. (16)] The transformation h = R^{1/2} \tilde{h} is used to generate a correlated MG channel vector from an IID MG vector. For Gaussian vectors this operation preserves the distribution family, but for MG (finite Gamma-mixture) variables it does not: each output entry is a weighted sum of independent Gamma-mixture variables, and such a sum is not generally itself a Gamma mixture. Consequently, h_RU in (17) and H_BR in (19) do not have MG-distributed entries, and the invocation of [16, Theorem 1] to derive the cascaded PDF in (20) is invalid. The theoretical NMSE curves based on the variance expression in (22) therefore do not describe the channel that is actually simulated unless a different, unspecified generation procedure is used.
[Sec. III-A and III-B, Eqs. (9)-(16)] The physical channel in Eq. (9) is a complex sum of random-phase phasors with distance-dependent magnitudes and absorption phases. The Kronecker construction in Eq. (16) instead produces a real (or at least phase-free) linear combination of nonnegative MG variables. This construction cannot reproduce the phase statistics of Eq. (9) or the distance-dependent phase differences that enter the near-field correlation matrix R_NF in (11). The paper does not explain how Eq. (16) is intended to correspond to Eq. (9); the relationship between the physical model and the Kronecker model needs to be established, or the paper should state clearly that Eq. (16) is an independent statistical model.
[Sec. IV, Eq. (27)] The covariance matrix R_cc is quoted from [19] rather than derived for the proposed MG model. For C = H_BR diag(h_RU) with independent H_BR and h_RU, the exact covariance under a Kronecker vec ordering is (R_RB ⊙ E[h_RU h_RU^H]) ⊗ R_BR; if h_RU has nonzero mean, E[h_RU h_RU^H] includes the mean contribution. Since the MG entries defined in Eq. (4) are nonnegative and hence have positive mean, using a spatial correlation matrix alone in (27) is not the correct LMMSE covariance. The derivation of (27) from the proposed channel model should be provided, including which matrix is intended to represent E[h_RU h_RU^H].
[Sec. V, Numerical Results] The Monte Carlo simulation setup is under-specified: the paper states that 1000 trials are used and that measurement-based MG parameters from [14] are adopted, but it does not describe the actual channel generation algorithm, the drawn values of the MG parameters, the quadrature order N, or how the spatial correlation and mutual coupling are applied to the simulated fading realizations. Because the Theo/Sim agreement claimed in Fig. 2 depends on the exact generator, the results cannot be reproduced or checked. In addition, the performance metric is defined as NMSE = Tr(R_e)/Tr(R_cc); for the LS estimator, Tr(R_e) is independent of the channel covariance, so NMSE scales inversely with Tr(R_cc). The paper does not state whether the compared models are normalized to equal total channel power, so the reported dB gains may reflect differences in total power rather than in estimation accuracy.

minor comments (4)

[Sec. VI, Conclusions] The sentence "This paper, we study" should be "In this paper, we study".
[Throughout] The paper contains several garbled equation artifacts (e.g., "B.dsp" in Eqs. (11), (14), and (16)) and missing mathematical symbols; these should be corrected before publication.
[Sec. V, Fig. 2c] The caption of Fig. 2c refers to the "MG /alpha parameter" but the x-axis is described as "MG variance"; the paper should clarify which quantity is plotted.
[References] Several references are cited as arXiv preprints ([10], [12]); if final versions exist, the published citations should be used.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the estimation comparisons are driven by standard covariance models and external MG parameters, not by fitted or self-referential inputs.

full rationale

The paper's central numerical claims compare LMMSE/LS estimation accuracy under different channel-model assumptions. That comparison is self-contained: the channel covariance Rcc is computed from the geometric near/far-field correlation models in (11)-(16) and the standard mutual-coupling transformation in (23), and the same covariance enters both the theoretical NMSE expressions (25)/(28) and the Monte Carlo simulation. No target quantity is fitted to the data against which it is compared. The MG fading parameters are taken externally from a measurement study ('Measurement-based MG parameters are utilized for a realistic scenario [14]'), not fitted in this paper. The product-of-MG distributional result is cited to the external reference [16] and implemented via Gaussian-Laguerre quadrature; it is not a self-citation. Although the Kronecker construction in (16) is mathematically questionable -- a linear combination of independent Gamma-mixture variables is not generally itself a Gamma mixture, so h_RU and H_BR need not have MG marginals -- this is a modeling-correctness concern rather than a circularity: the estimation results do not reduce to their own inputs by construction, and no load-bearing claim depends on a self-citation chain. The authors' self-citations [1]-[3], [6] are used for background, propagation details, and simulator parameters, not as the sole justification of the conclusions. Accordingly, the paper is not circular under the stated criteria.

Assumptions & free parameters 7 free parameters · 5 assumptions · 0 invented entities

The central claims rest on the statistical fading model, the correlation construction, and independence assumptions. Most parameters are taken from prior measurements or chosen for simulation; the key unproven step is the linear transformation of MG variables.

free parameters (7)

MG component weights, shapes, and rates for UE-RIS channel = from [14]
Measurement-based MG parameters for outdoor THz fading are taken from [14] as inputs; the paper does not fit them but the numerical results depend on their values.
MG component weights, shapes, and rates for RIS-BS channel = from [14]
Same as above; the cascaded variance in Eq. (22) is computed from these parameters.
Scatterer ring radius R_g = 1.8 m near-field, 4 m far-field
Chosen to represent near-field and far-field regimes; affects the correlation matrices in Eqs. (11)-(14).
Cluster distances D_g and angles = randomly chosen within near-field
The distances D_g are set within the radiative near-field and angles are randomly chosen within [-pi/2, pi/2); these are simulation choices, not fitted.
von Mises concentration and mean for angular spread = not stated
The von Mises PDF in Eq. (13) requires kappa_g and mu_g; the paper does not report their values, which are needed to reproduce the correlation matrices.
Gaussian-Laguerre quadrature order N = not stated
The cascaded PDF and variance in Eqs. (20)-(22) use N-point Gaussian-Laguerre quadrature; the order is not specified.
Number of clusters F and F' = 3
Set to 3 in the simulation; a simulation choice that affects the channel statistics.

assumptions (5)

standard math Theorem 1 of [16]: the product of two independent MG random variables is MG-distributed with parameters given by a finite sum.
Used in Sec. III-C to derive the cascaded PDF (20). This theorem is cited, not proved in the paper.
ad hoc to paper The linear transformation h = R^{1/2} \tilde{h} produces a valid correlated MG channel whose entries remain MG-distributed.
This is the key assumption behind Eq. (16) and the subsequent distributional analysis. It is not true for sums of independent Gamma variables in general, and the paper does not justify it.
domain assumption The UE-RIS and RIS-BS subchannels are independent and the direct UE-BS path is blocked.
Stated in Sec. II; allows the cascaded channel to be written as C = H_BR diag(h_RU) and the covariance in Eq. (27).
domain assumption Scatterer angles follow a von Mises distribution.
Eq. (13) uses von Mises; this is a modeling choice from prior work [9], [20].
standard math Mutual coupling is modeled by the circuit-theory matrix M = (Z + R_l I)^{-1}.
Taken from [21]-[23]; the paper does not derive it.

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Cite this review

Pith. "Pith review of THz-Band Near-Field RIS Channel Modeling for Linear Channel Estimation." pith.science (2026). https://pith.science/paper/N5H7M6TA

@misc{pith2026250509767,
  author       = {Pith},
  title        = {Pith review of: THz-Band Near-Field RIS Channel Modeling for Linear Channel Estimation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/N5H7M6TA}},
  note         = {Machine review of arXiv:2505.09767}
}

read the original abstract

Reconfigurable intelligent surface (RIS)-aided terahertz (THz)-band communications are promising enablers for future wireless networks. However, array densification at high frequencies introduces significant challenges in accurate channel modeling and estimation, particularly with THz-specific fading, mutual coupling (MC), spatial correlation, and near-field effects. In this work, we model THz outdoor small-scale fading channels using the mixture gamma (MG) distribution, considering absorption losses, spherical wave propagation, MC, and spatial correlation across large base stations and RISs. We derive the distribution of the cascaded RIS-aided channel and investigate linear channel estimation techniques, analyzing the impact of various channel parameters. Numerical results based on precise THz parameters reveal that accounting for spatial correlation, MC, and near-field modeling substantially enhances estimation accuracy, especially in ultra-massive arrays and short-range scenarios. These results underscore the importance of incorporating these effects for precise, physically consistent channel modeling.

Figures

Figures reproduced from arXiv: 2505.09767 by the authors.

**Figure 2.** Comparison of channel estimation accuracy for diffe [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

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Reference graph

Works this paper leans on

23 extracted references · 18 canonical work pages

[20]

Near-ﬁeld spatial correlation for extremely large- scale array communications,

Z. Dong and Y . Zeng, “Near-ﬁeld spatial correlation for extremely large- scale array communications,” IEEE Commun. Lett. , vol. 26, no. 7, pp. 1534–1538, 2022

work page 2022
[19]

An overview of signal processing techniques for RIS/IRS- aided wireless systems,

C. Pan et al., “An overview of signal processing techniques for RIS/IRS- aided wireless systems,” IEEE J. Sel. Topics Signal Process. , vol. 16, no. 5, pp. 883–917, 2022

2022
[14]

Outdoor THz fading modeling by means of Gaussian and Gamma mixture distributions,

E. N. Papasotiriou, A.-A. A. Boulogeorgos, and A. Alexi ou, “Outdoor THz fading modeling by means of Gaussian and Gamma mixture distributions,” Sci. Rep. , vol. 13, no. 1, p. 6385, 2023

work page 2023
[1]

An overview of signal processing techniques for terahertz communication s,

H. Sarieddeen, M.-S. Alouini, and T. Y . Al-Naffouri, “An overview of signal processing techniques for terahertz communication s,” Proc. IEEE, vol. 109, no. 10, pp. 1628–1665, 2021

2021
[2]

Next generation terahertz communications: A rendezvous of sensing, imaging, and localization,

H. Sarieddeen et al. , “Next generation terahertz communications: A rendezvous of sensing, imaging, and localization,” IEEE Commun. Mag., vol. 58, no. 5, pp. 69–75, 2020

work page 2020
[3]

TeraMIMO: A channel simulator for wideband ultra- massive MIMO terahertz communications,

S. Tarboush et al., “TeraMIMO: A channel simulator for wideband ultra- massive MIMO terahertz communications,” IEEE Trans. V eh. Technol., vol. 70, no. 12, pp. 12 325–12 341, 2021

work page 2021
[4]

Wireless communications through reconﬁgurable intel- ligent surfaces,

E. Basar et al. , “Wireless communications through reconﬁgurable intel- ligent surfaces,” IEEE Access , vol. 7, pp. 116 753–116 773, 2019

2019
[5]

Reconﬁgurable intelligent surfaces for THz: Hardware impairments and switching technologies,

S. Matos et al. , “Reconﬁgurable intelligent surfaces for THz: Hardware impairments and switching technologies,” in Proc. Int. Conf. on Elec- tromagn. in Adv. Appl. (ICEAA) , 2024, pp. 415–420

work page 2024

Show all 23 references

[6]

Cross-ﬁeld c hannel esti- mation for ultra massive-MIMO THz systems,

S. Tarboush, A. Ali, and T. Y . Al-Naffouri, “Cross-ﬁeld c hannel esti- mation for ultra massive-MIMO THz systems,” IEEE Trans. Wireless Commun., vol. 23, no. 8, pp. 8619–8635, 2024

2024
[7]

Exploiting mutual coupl ing charac- teristics for channel estimation in holographic MIMO,

N. Kolomvakis and E. Björnson, “Exploiting mutual coupl ing charac- teristics for channel estimation in holographic MIMO,” arXiv preprint arXiv:2412.13683, 2024

2024 arXiv
[8]

Near-ﬁeld channel modeling for holographic MIMO communications,

T. Gong et al. , “Near-ﬁeld channel modeling for holographic MIMO communications,” IEEE Wireless Commun., vol. 31, no. 3, pp. 108–116, 2024

2024
[9]

Rayleigh fading modeli ng and channel hardening for reconﬁgurable intelligent surfaces,

E. Björnson and L. Sanguinetti, “Rayleigh fading modeli ng and channel hardening for reconﬁgurable intelligent surfaces,” IEEE Wireless Com- mun. Lett. , vol. 10, no. 4, pp. 830–834, 2021

2021
[10]

Far-versus near-ﬁeld RIS modeling and beam design,

M. Delbari et al., “Far-versus near-ﬁeld RIS modeling and beam design,” arXiv preprint arXiv:2401.08237 , 2024

2024 arXiv
[11]

Performance analysis of active RIS-aided systems in the face of imperfect CSI and phase shift noise,

Q. Li et al. , “Performance analysis of active RIS-aided systems in the face of imperfect CSI and phase shift noise,” IEEE Trans. V eh. Technol., vol. 72, no. 6, pp. 8140–8145, 2023

2023
[12]

Mutual coupling-aware channel estimation and beamforming for RIS-assisted communications,

P . Zheng et al. , “Mutual coupling-aware channel estimation and beamforming for RIS-assisted communications,” arXiv preprint arXiv:2410.04110, 2024

2024 arXiv
[13]

On the ergodic se crecy capacity of reconﬁgurable intelligent surface aided wirel ess systems under mixture Gamma fading,

A.-A. A. Boulogeorgos and A. Alexiou, “On the ergodic se crecy capacity of reconﬁgurable intelligent surface aided wirel ess systems under mixture Gamma fading,” in Proc. IEEE Wireless Commun. and Netw. Conf. (WCNC) , 2023, pp. 1–6

2023
[15]

Performance analysis of outdoor THz links under mixture Gamma fading with misalignment,

H. Jemaa et al. , “Performance analysis of outdoor THz links under mixture Gamma fading with misalignment,” IEEE Commun. Lett. , vol. 28, no. 11, pp. 2668–2672, 2024

2024
[16]

Analysis of IRS-assisted downlink wireless networks over generalized fading,

Y . Li and Y . J. Chun, “Analysis of IRS-assisted downlink wireless networks over generalized fading,” IEEE Trans. Wireless Commun. , vol. 23, no. 8, pp. 10 182–10 197, 2024

2024
[17]

Intelligent reﬂecting s urface with discrete phase shifts: Channel estimation and passive beam forming,

C. Y ou, B. Zheng, and R. Zhang, “Intelligent reﬂecting s urface with discrete phase shifts: Channel estimation and passive beam forming,” in Proc. IEEE Int. Conf. Commun. (ICC) , 2020, pp. 1–6

2020
[18]

An optimal channel esti mation scheme for intelligent reﬂecting surfaces based on a minimu m variance unbiased estimator,

T. L. Jensen and E. De Carvalho, “An optimal channel esti mation scheme for intelligent reﬂecting surfaces based on a minimu m variance unbiased estimator,” in Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Process. (ICASSP) , 2020, pp. 5000–5004

2020
[21]

Toward a circuit theory of commu- nication,

M. T. Ivrla ˇc and J. A. Nossek, “Toward a circuit theory of commu- nication,” IEEE Trans. Circuits Syst. I , vol. 57, no. 7, pp. 1663–1683, 2010

2010
[22]

Holographic MIMO com munica- tions: What is the beneﬁt of closely spaced antennas?

A. A. D’Amico and L. Sanguinetti, “Holographic MIMO com munica- tions: What is the beneﬁt of closely spaced antennas?” IEEE Trans. Wireless Commun., vol. 23, no. 10, pp. 13 826–13 840, 2024

2024
[23]

C. A. Balanis, Antenna theory: Analysis and design . John wiley & sons, 2016

2016

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.

[1] [20]

Near-ﬁeld spatial correlation for extremely large- scale array communications,

Z. Dong and Y . Zeng, “Near-ﬁeld spatial correlation for extremely large- scale array communications,” IEEE Commun. Lett. , vol. 26, no. 7, pp. 1534–1538, 2022

work page 2022

[2] [19]

An overview of signal processing techniques for RIS/IRS- aided wireless systems,

C. Pan et al., “An overview of signal processing techniques for RIS/IRS- aided wireless systems,” IEEE J. Sel. Topics Signal Process. , vol. 16, no. 5, pp. 883–917, 2022

2022

[3] [14]

Outdoor THz fading modeling by means of Gaussian and Gamma mixture distributions,

E. N. Papasotiriou, A.-A. A. Boulogeorgos, and A. Alexi ou, “Outdoor THz fading modeling by means of Gaussian and Gamma mixture distributions,” Sci. Rep. , vol. 13, no. 1, p. 6385, 2023

work page 2023

[4] [1]

An overview of signal processing techniques for terahertz communication s,

H. Sarieddeen, M.-S. Alouini, and T. Y . Al-Naffouri, “An overview of signal processing techniques for terahertz communication s,” Proc. IEEE, vol. 109, no. 10, pp. 1628–1665, 2021

2021

[5] [2]

Next generation terahertz communications: A rendezvous of sensing, imaging, and localization,

H. Sarieddeen et al. , “Next generation terahertz communications: A rendezvous of sensing, imaging, and localization,” IEEE Commun. Mag., vol. 58, no. 5, pp. 69–75, 2020

work page 2020

[6] [3]

TeraMIMO: A channel simulator for wideband ultra- massive MIMO terahertz communications,

S. Tarboush et al., “TeraMIMO: A channel simulator for wideband ultra- massive MIMO terahertz communications,” IEEE Trans. V eh. Technol., vol. 70, no. 12, pp. 12 325–12 341, 2021

work page 2021

[7] [4]

Wireless communications through reconﬁgurable intel- ligent surfaces,

E. Basar et al. , “Wireless communications through reconﬁgurable intel- ligent surfaces,” IEEE Access , vol. 7, pp. 116 753–116 773, 2019

2019

[8] [5]

Reconﬁgurable intelligent surfaces for THz: Hardware impairments and switching technologies,

S. Matos et al. , “Reconﬁgurable intelligent surfaces for THz: Hardware impairments and switching technologies,” in Proc. Int. Conf. on Elec- tromagn. in Adv. Appl. (ICEAA) , 2024, pp. 415–420

work page 2024