Dual RIS-Assisted Monostatic L-Band Radar Target Detection in NLoS Scenarios

Ijaz Haider Naqvi; Nor Muzlifah Mahyuddin; Salman Liaquat

REVIEW 3 major objections 5 minor 26 references

Dual RIS-Assisted Monostatic L-Band Radar Target Detection in NLoS Scenarios

T0 review · 3 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read Two large RISs beat one RIS for radar detection behind obstacles.

desk verdict Routine extension of a single-RIS radar model to two RISs, with a central crossover claim that is invalidated by phase inconsistencies, unphysical independent forward/return phase shifts, and simulations run outside the model's far-field validity. read the letter →

arxiv 2507.11036 v1 pith:PJQEFORH submitted 2025-07-15 eess.SP

Salman Liaquat , Ijaz Haider Naqvi , Nor Muzlifah Mahyuddin This is my paper · ORCID

classification eess.SP

keywords reconfigurableintelligentsurfaceRIS-assistedradarmonostaticnon-line-of-sighttargetdetectionsignal-to-noiseratioL-bandarraygainpathloss

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 tries to establish that a monostatic radar can detect a target in a non-line-of-sight setting using two reconfigurable intelligent surfaces (RISs)—nearly passive panels of tunable elements that redirect radar signals—and that under the right conditions this beats using one RIS. The authors derive a closed-form signal-to-noise ratio (SNR) expression for the path radar–RIS-1–RIS-2–target–RIS-2–RIS-1–radar, showing how the received power factors into two independent array gains. The quantitative claim is that dual-RIS SNR overtakes single-RIS SNR once each surface has enough unit cells—about 37×37 or 46×46 elements at L-band in their simulation—and the surfaces are aligned so their radiation patterns overlap. The practical stakes: a radar operator could choose the number and placement of RISs to detect drones or vehicles behind obstacles without new transmit power, using this formula to predict when a second RIS pays off.

What carries the argument

The central object is the two-hop channel Radar→RIS-1→RIS-2→Target→RIS-2→RIS-1→Radar. The derivation models each RIS as an array of tunable unit cells and introduces per-unit-cell complex weights Wj,k and Wm,n that absorb the reflection coefficient, unit-cell radiation pattern, path distance, and phase. The received power then equals a product of two fourth-power array-factor sums, |ΣWj,k|^4 |ΣWm,n|^4 (Eq. 13), which under far-field and maximum alignment collapses to the closed-form SNR of Eq. (15) with element counts entering as $J^{4}$$K^{4}$$M^{4}$$N^{4}$ and the three hop distances entering as $r1^{4}$ $r2^{4}$ $r_RIS^{4}$.

What would settle it

Compute the exact near-field sum for the RIS-1-to-RIS-2 link at L-band with r_RIS = 50 m, keeping every unit-cell distance distinct, and compare the resulting dual-RIS SNR against the single-RIS baseline for 37×37 and 46×46 elements; if the SNR advantage disappears, the far-field factorization in Eq. (13) is what failed.

Watch

Extended reading notes

Core claim

The paper's central claim is that the SNR of a perfectly aligned dual-RIS monostatic radar, with the signal bouncing radar→RIS-1→RIS-2→target and back, can exceed the SNR of a single-RIS system, provided each RIS is large enough. The evidence is the derived closed-form SNR expression (Eq. 15), which grows like the fourth power of the element count of each RIS divided by the fourth power of the radar-to-RIS-1, RIS-1-to-RIS-2, and RIS-2-to-target distances. Under the simulation parameters—L-band, an inter-RIS distance of 50 m, and unit-cell spacing of λ/2—the dual-RIS configuration gives higher SNR for RIS sizes of 37×37 and 46×46 elements, while 10×10 and 19×19 surfaces lose to the additional path loss of the longer double-hop route. The paper interprets this as an 'RIS effect' that turns positive only beyond a size threshold, and concludes that adding RISs helps when the cumulative array gain outweighs the multiplicative path-loss penalty.

Load-bearing premise

The derivation assumes the two RISs are in each other's far field, so all unit-cell-to-unit-cell distances and angles collapse to one inter-RIS distance r_RIS; in the simulations r_RIS is only 50 m while the large RIS sizes that produce the claimed advantage have far-field distances of about 147 m and 227 m.

Editorial extensions

If this is right

At L-band, upgrading both RISs from 10×10 to 37×37 elements flips the comparison from single-RIS-favorable to dual-RIS-favorable, so the crossover is a design target rather than a universal property.
Doubling the number of elements along each axis of both RISs multiplies the SNR by 2^16 according to Eq. (15), so the formula predicts extremely steep returns to RIS size.
The fourth-power distance dependence means the inter-RIS and RIS-to-target distances dominate the SNR; a small increase in the gap between the RISs costs far more than the same increase in transmit power can recover.
The model identifies a threshold number of unit cells per RIS for a given geometry; below it, a second RIS is counterproductive, above it, it improves detection.

Reading between the lines

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

Beyond the paper: the far-field assumption between the two RISs is violated for the very sizes that produce the claimed crossover (4 m and 5 m RISs have far-field distances near 147 m and 227 m, but the simulation places them 50 m apart), so the factorized Eq. (13) and the crossover curves in Fig. 2 should be treated as predictions that need a near-field check.
Beyond the paper: the same product-of-array-factors structure suggests the derivation extends naturally to more than two RISs; each additional RIS multiplies the SNR by another fourth-power array-gain factor divided by the fourth power of the new hop distance, so a chain of many small RISs might match one large RIS.
Beyond the paper: the crossover element count should shrink at higher frequencies such as X-band because the same physical aperture contains more unit cells and the far-field distances are shorter, making dual-RIS setups practical for smaller surfaces.
Beyond the paper: a direct experiment—two 4 m×4 m RISs at 50 m separation at L-band—would settle whether the predicted 0.84 dB RIS effect at 37×37 elements survives near-field coupling.

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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

The paper extends the authors' single-RIS SNR formula to a dual-RIS monostatic radar. The extension is routine—multiply the single-hop gains and add the extra path loss—and the central crossover claim, that two RISs outperform one for sufficiently large arrays, is not supported by the derivation as written.

What's good: the authors attempt a step-by-step EM-style power budget with element patterns, physical dimensions, and separate radar, RIS-1, RIS-2, target hops. The closed-form SNR in Eq. (15) would be a useful design equation for deciding when a second RIS is worthwhile, if it were valid.

The problems are real and load-bearing. First, there's an internal inconsistency: Eq. (10) has a 2π r_r/λ phase in the first RIS-1 sum, while Eq. (11) defines W_{j,k} with 4π r_r/λ, and the final SNR uses the Eq. (11) version. That changes the coherent-combining condition. Second, the derivation gives each RIS independent forward and return phase shifts (φ and φ'). A passive reciprocal surface has one phase response per element; allowing two independent phase profiles on transmit and receive overstates what a static RIS can do and inflates the SNR. The maximum-alignment condition before Eq. (14) is asserted, not derived. Third—and this is the one that sinks the simulation—the factorization in Eq. (13) into |ΣW_{j,k}|^4 |ΣW_{m,n}|^4 assumes every inter-RIS element distance is the same constant. That is not what the far-field approximation gives; far field gives a linear phase taper across the array. And the simulations set r_RIS = 50 m while Table 2 lists far-field distances of 146.6 m and 226.6 m for the 37×37 and 46×46 RISs that produce the crossover. So Fig. 2's curves are computed with the model outside its own domain of validity.

I agree with the stress-test assessment. The crossover conclusion is therefore unsupported. The paper does not fit the data; it's a clean derivation attempt with errors. But the errors are in the load-bearing parts, and the physical phase model is wrong for a passive reciprocal RIS. I would not cite this, and I would not put it in front of a reviewer for a serious journal as is. For the ICCFI workshop it might pass as a discussion paper. A referee asked to evaluate it should recommend major revision or reject, with those three points spelled out.

Referee Report

3 major / 5 minor

Summary. The paper derives closed-form expressions for the received power, SNR, and path loss of a monostatic L-band radar whose signal propagates through two RISs in an NLoS scenario (Radar–RIS-1–RIS-2–Target–RIS-2–RIS-1–Radar). The authors then compare the dual-RIS SNR with a single-RIS baseline from their prior work and claim, based on simulations, that dual RISs outperform a single RIS when the RIS sizes are sufficiently large, specifically for 37x37 and 46x46 elements at the configured geometry. The main contribution is the SNR formula in Eq. (15) and the crossover claim in Fig. 2.

Significance. If the derivation were sound, the paper would provide a useful first-order model for cascaded RIS-assisted radar and a concrete quantitative prediction about when adding a second RIS is beneficial. The problem is clearly motivated and the authors make their assumptions explicit, which is commendable. However, the central result rests on a far-field factorization that is not justified and is evaluated in a regime that contradicts the paper's own far-field distances. The internal phase inconsistency between Eqs. (10) and (11) further weakens the derivation. As it stands, the quantitative crossover claim is not established, so the paper does not yet provide a reliable design guideline.

major comments (3)

[Section 2, Eqs. (10) and (11)] The factorization of the received power into |sum_{j,k} W_{j,k}|^4 |sum_{m,n} W_{m,n}|^4 is load-bearing for the entire paper, but it is not justified by the stated far-field approximation. The text immediately before Eq. (4) asserts that in the far field r_RIS,j,k = r_RIS,m,n for all elements, but equality of all element-pair distances is not a consequence of the far-field approximation; even for two finite apertures in the far field, the element-to-element distance contains a linear phase taper depending on both element indices. A standard far-field model can make this phase separable, and then the sums factor, but the paper does not present that derivation; it simply collapses every inter-RIS distance to a constant. Moreover, the simulation uses r_RIS = 50 m (Table 1) for RIS sizes whose far-field distances are 146.6 m and 226.6 m (Table 2), namely the 37x37 and 46x46 configurations that produce the claimed crossover. Thus Eq. (15) and Fig. 2 are evaluated in a regime where neither the stated assumption nor a separable far-field model is valid, and the central quantitative claim is unsupported.
[Section 2, before Eq. (14)] There is an internal inconsistency in the core derivation. The first sum in Eq. (10) contains the phase term exp(-j(2*pi*r_r,j,k/lambda - phi_1,j,k + 2*pi*r_RIS/lambda)), while W_{j,k} in Eq. (11) is defined with 4*pi*r_r,j,k/lambda, and the final SNR expression in Eq. (13) and Eq. (15) uses W_{j,k}. Since the factor 4*pi*r_r/lambda represents the two-way propagation between the radar and RIS-1, Eq. (10) should already contain that factor if it is the basis for Eq. (13). As written, the derivation does not connect Eq. (10) to Eq. (13). This is not a notational quibble: the phase is what determines the maximum-alignment condition that produces Eq. (14).
[Section 3, Tables 1-2 and Fig. 2] The maximum-alignment condition theta_t = theta_RIS and phi_t = phi_RIS + pi is asserted without proof. This condition is nontrivial because the sums in Eq. (13) include the radiation-pattern factors F, which depend on angles, and the target is modeled only through an RCS that is treated as a scalar. It is not obvious that this angle assignment simultaneously maximizes the two independent sums in Eq. (13). The authors should either prove this condition or provide a reference; without it, Eq. (14) and the subsequent SNR-maximization result rest on an unverified assumption.

minor comments (5)

[Section 3, Fig. 2] The single-RIS baseline SNR equation from [17] is not reproduced in the manuscript. Since the central comparison in Fig. 2 depends on that baseline, the authors should state the baseline equation and the parameter mapping used for the comparison so that the reader can verify the crossover is not an artifact of differing model assumptions.
[Table 2] The vertical lines in Fig. 2 are described as the minimum distance required for the far-field assumption, but it is unclear whether they refer to the target-RIS distance or the inter-RIS distance. Since r_RIS is fixed at 50 m in Table 1, the vertical lines cannot refer to the inter-RIS link for the larger RIS sizes; please clarify.
[Abstract] The quantity labeled 'RIS effect' is not defined anywhere in the paper. Please state its formula and explain how it is computed from Eq. (15) or the single-RIS baseline; otherwise the positive values for the 37x37 and 46x46 configurations are not interpretable.
[Throughout] The abstract claims that 'the required accuracy in target localization can be achieved' by controlling the number of RISs and unit cells, but the paper contains no localization accuracy analysis; it derives SNR and path loss only. Please rephrase to avoid overclaiming.
[Throughout] There are several typographical and notation issues, including 'rtn,m' in Eq. (6) instead of a properly subscripted distance, the P6/P7 label mismatch around Eqs. (9)-(10), and inconsistent use of 'r_RIS' versus 'r_RIS,j,k' in the derivation. A careful proofreading pass is needed.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the dual-RIS SNR result is derived from Friis-style power-transfer equations, not fitted to or assumed from the single-RIS conclusion.

full rationale

The central dual-RIS SNR expression (Eq. 15) follows from a step-by-step power-transfer derivation (Eqs. 2-13). The inter-RIS and RIS-target sums become magnitude sums only under the explicitly stated far-field and maximum-alignment assumptions; no constant is fitted to the dual-RIS SNR curve. The crossover against the single-RIS system (Fig. 2, Table 2) is obtained by comparing Eq. 15 with the single-RIS formula from the authors' prior work [17], used as an input baseline rather than as the target conclusion. The dual-RIS expression does not assume dual superiority; its M^4N^4/r_RIS^4 factor is a derived consequence of coherent summation over the second RIS. The manuscript's own far-field collapse r_RIS_j,k = r_RIS_m,n stated before Eq. (4) and the use of r_RIS = 50 m with RIS sizes whose Table 2 far-field distances are 146.6 m and 226.6 m is a substantive validity/correctness concern, but it is not circularity: the factorization in Eq. (13) is not obtained by defining the comparison to be true or by renaming a fitted parameter. No circular step is present.

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

The central claim rests on the far-field factorization of the two RIS arrays, the reciprocity of the antenna, the point-scatterer target model, and the unstated ability of each RIS to use different phase profiles on the forward and return paths. No free parameters are fitted to data; the simulation inputs are chosen constants. No new entities are introduced.

assumptions (5)

domain assumption The two RISs are in each other's far field, so r_RIS,j,k = r_RIS,m,n and the inter-RIS angles coincide.
Invoked before Eq. (4) and used to factor the sums in Eqs. (10)-(13). Violated in the simulations with r_RIS=50 m for RIS sizes of 3-5 m, whose far-field distances are 84-227 m (Table 2).
ad hoc to paper Each RIS unit cell can impose independent phase shifts phi and phi' on the forward and return passages.
phi1' and phi2' appear in Eqs. (10)-(12) without definition. A passive reciprocal surface has one reflection phase per element, so this over-parameterization is not physically justified unless the surface is reconfigured between the two passages, which the paper does not discuss.
domain assumption The target behaves as a point scatterer with radar cross section sigma and reradiates isotropically.
Used in Eqs. (6)-(9) to relate the power returned to RIS-2 to the power incident on the target. Standard for the radar equation but an idealization.
ad hoc to paper The maximum received power condition is theta_t = theta_RIS and phi_t = phi_RIS + pi.
Stated immediately before Eq. (14) without proof. The reciprocity argument that would justify using the same RIS phases for the return path is not presented.
domain assumption The radar antenna has identical transmit and receive radiation patterns and gains (monostatic reciprocity).
Invoked in Section II when the received power is computed with Frad and Gt on both the transmit and receive legs. Standard for monostatic radar.

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Cite this review

Pith. "Pith review of Dual RIS-Assisted Monostatic L-Band Radar Target Detection in NLoS Scenarios." pith.science (2026). https://pith.science/paper/PJQEFORH

@misc{pith2026250711036,
  author       = {Pith},
  title        = {Pith review of: Dual RIS-Assisted Monostatic L-Band Radar Target Detection in NLoS Scenarios},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PJQEFORH}},
  note         = {Machine review of arXiv:2507.11036}
}

read the original abstract

The use of a single Reconfigurable Intelligent Surface (RIS) to boost the signal-to-noise ratio (SNR) at the radar offers significant improvement in detecting targets, especially in non-line-of-sight (NLoS) scenarios. However, there are scenarios where no path exists between the radar and the target, even with a single RIS-assisted radar, due to other present obstacles. This paper derives an expression for SNR in target detection scenarios where dual RISs assist a monostatic radar in NLoS situations. We calculate the power received at the radar through a dual RIS configuration. We show that the SNR performance of RIS-assisted radars can improve with known locations of the radar and RISs. Our results demonstrate that the required accuracy in target localization can be achieved by controlling the number of RISs, the number of unit cells in each RIS, and properly selecting the locations of RISs to cover the desired region. The performance of dual RIS-assisted radar systems can surpass that of single RIS-assisted radar systems under favourable alignment and sufficiently large RIS sizes.

Figures

Figures reproduced from arXiv: 2507.11036 by the authors.

**Figure 2.** SNR performance comparison between single and dual RIS-assisted radar [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

26 extracted references · 26 canonical work pages

[1]

IEEE Transactions on Cognitive Communications and Network- ing 6(3), 990–1002 (2020) 12 S

ElMossallamy, M.A., Zhang, H., Song, L., Seddik, K.G., Han, Z., Li, G.Y.: Recon- figurable intelligent surfaces for wireless communications: Principles, challenges, and opportunities. IEEE Transactions on Cognitive Communications and Network- ing 6(3), 990–1002 (2020) 12 S. Liaquat et al

work page 2020
[2]

IEEE Transactions on Communi- cations 70(9), 6259–6276 (2022)

Tang, W., Chen, X., Chen, M.Z., Dai, J.Y., Han, Y., Di Renzo, M., Jin, S., Cheng, Q., Cui, T.J.: Path loss modeling and measurements for reconfigurable intelligent surfaces in the millimeter-wave frequency band. IEEE Transactions on Communi- cations 70(9), 6259–6276 (2022)

work page 2022
[3]

IEEE Communications Surveys & Tutorials 23(3), 1546–1577 (2021)

Liu, Y., Liu, X., Mu, X., Hou, T., Xu, J., Di Renzo, M., Al-Dhahir, N.: Recon- figurable intelligent surfaces: Principles and opportunities. IEEE Communications Surveys & Tutorials 23(3), 1546–1577 (2021)

work page 2021
[4]

IEEE Communications Magazine 58(12), 90–96 (2020)

Björnson, E., Özdogan, O., Larsson, E.G.: Reconfigurable intelligent surfaces: Three myths and two critical questions. IEEE Communications Magazine 58(12), 90–96 (2020)

work page 2020
[5]

IEEE Access 7, 116753–116773 (2019)

Basar, E., Di Renzo, M., De Rosny, J., Debbah, M., Alouini, M.S., Zhang, R.: Wireless communications through reconfigurable intelligent surfaces. IEEE Access 7, 116753–116773 (2019)

work page 2019
[6]

Nature Electronics 3(3), 165–171 (2020)

Zhang, X.G., Jiang, W.X., Jiang, H.L., Wang, Q., Tian, H.W., Bai, L., Luo, Z.J., Sun, S., Luo, Y., Qiu, C.W., et al.: An optically driven digital metasurface for programming electromagnetic functions. Nature Electronics 3(3), 165–171 (2020)

work page 2020
[7]

IEEE Communications Magazine 58(1), 106–112 (2020)

Wu, Q., Zhang, R.: Towards smart and reconfigurable environment: Intelligent reflecting surface aided wireless network. IEEE Communications Magazine 58(1), 106–112 (2020)

work page 2020
[8]

IEEE Journal on Selected Areas in Communications 38(11), 2450–2525 (2020)

Di Renzo, M., Zappone, A., Debbah, M., Alouini, M.S., Yuen, C., de Rosny, J., Tretyakov, S.: Smart radio environments empowered by reconfigurable intelligent surfaces: How it works, state of research, and the road ahead. IEEE Journal on Selected Areas in Communications 38(11), 2450–2525 (2020)

work page 2020

Show all 26 references

[9]

IEEE Signal Processing Letters 28, 1315–1319 (2021)

Buzzi, S., Grossi, E., Lops, M., Venturino, L.: Radar target detection aided by reconfigurable intelligent surfaces. IEEE Signal Processing Letters 28, 1315–1319 (2021)

2021
[10]

Proceedings of the IEEE 110(9), 1164–1209 (2022)

Di Renzo, M., Danufane, F.H., Tretyakov, S.: Communication models for recon- figurable intelligent surfaces: From surface electromagnetics to wireless networks optimization. Proceedings of the IEEE 110(9), 1164–1209 (2022)

2022
[11]

IEEE Sensors Letters 5(3), 1–4 (2021)

Lu, W., Lin, Q., Song, N., Fang, Q., Hua, X., Deng, B.: Target detection in in- telligent reflecting surface aided distributed mimo radar systems. IEEE Sensors Letters 5(3), 1–4 (2021)

2021
[12]

IEEE Transactions on Signal Processing 70, 1749–1763 (2022)

Buzzi, S., Grossi, E., Lops, M., Venturino, L.: Foundations of mimo radar de- tection aided by reconfigurable intelligent surfaces. IEEE Transactions on Signal Processing 70, 1749–1763 (2022)

2022
[13]

IEEE Transactions on Wireless Communications 21(9), 6994–7010 (2022)

Zhang, H., Zhang, H., Di, B., Bian, K., Han, Z., Song, L.: Metaradar: Multi- target detection for reconfigurable intelligent surface aided radar systems. IEEE Transactions on Wireless Communications 21(9), 6994–7010 (2022)

2022
[14]

Signal Processing 189, 108252 (2021)

Feng, Z., Wang, B., Zhao, Y., Luan, M., Hu, F.: Power optimization for target localization with reconfigurable intelligent surfaces. Signal Processing 189, 108252 (2021)

2021
[15]

IEEE Transactions on Vehicular Technology 70(10), 10735– 10749 (2021)

Aubry, A., De Maio, A., Rosamilia, M.: Reconfigurable intelligent surfaces for n- los radar surveillance. IEEE Transactions on Vehicular Technology 70(10), 10735– 10749 (2021)

2021
[16]

arXiv preprint arXiv:2207.05611 (2022)

Song, X., Xu, J., Liu, F., Han, T.X., Eldar, Y.C.: Intelligent reflecting surface en- abled sensing: Cram\’er-rao bound optimization. arXiv preprint arXiv:2207.05611 (2022)

2022 arXiv
[17]

In: 2024 47th International Conference on Telecommunications and Signal Processing (TSP)

Liaquat, S., Naqvi, I.H., Mahyuddin, N.M., Hassan, N.U.: The impact of swerling models on snr and path loss in ris-assisted monostatic radar under nlos condi- tions. In: 2024 47th International Conference on Telecommunications and Signal Processing (TSP). pp. 69–74 (2024) Titl...

2024
[18]

IEEE Wireless Communications 30(1), 50–57 (2023)

Liu, R., Li, M., Luo, H., Liu, Q., Swindlehurst, A.L.: Integrated sensing and com- munication with reconfigurable intelligent surfaces: Opportunities, applications, and future directions. IEEE Wireless Communications 30(1), 50–57 (2023)

2023
[19]

IEEE Signal Processing Letters (2023)

Grossi, E., Taremizadeh, H., Venturino, L.: Radar target detection and localiza- tion aided by an active reconfigurable intelligent surface. IEEE Signal Processing Letters (2023)

2023
[20]

IEEE Journal on Selected Areas in Communications 40(7), 2070–2084 (2022)

Shao, X., You, C., Ma, W., Chen, X., Zhang, R.: Target sensing with intelligent reflecting surface: Architecture and performance. IEEE Journal on Selected Areas in Communications 40(7), 2070–2084 (2022)

2022
[21]

IEEE Signal Processing Magazine 40(6), 41–62 (2023)

Chepuri, S.P., Shlezinger, N., Liu, F., Alexandropoulos, G.C., Buzzi, S., Eldar, Y.C.: Integrated sensing and communications with reconfigurable intelligent sur- faces: From signal modeling to processing. IEEE Signal Processing Magazine 40(6), 41–62 (2023)

2023
[22]

IEEE Communications Surveys & Tutorials (2023)

Chafii, M., Bariah, L., Muhaidat, S., Debbah, M.: Twelve scientific challenges for 6g: Rethinking the foundations of communications theory. IEEE Communications Surveys & Tutorials (2023)

2023
[23]

IEEE Aerospace and Elec- tronic Systems Magazine pp

Liaquat, S., Mahyuddin, N.M., Naqvi, I.H.: An end-to-end modular framework for radar signal processing: A simulation-based tutorial. IEEE Aerospace and Elec- tronic Systems Magazine pp. 1–17 (2023)

2023
[24]

Ain Shams Engineering Journal 15(5), 102707 (2024)

Liaquat, S., Faizan, M., Chattha, J.N., Butt, F.A., Mahyuddin, N.M., Naqvi, I.H.: A framework for preventing unauthorized drone intrusions through radar detection and gps spoofing. Ain Shams Engineering Journal 15(5), 102707 (2024)

2024
[25]

Radar handbook 2, 21 (1962)

Skolnik, M.I.: Introduction to radar. Radar handbook 2, 21 (1962)

1962
[26]

In: International Con- ference on Robotics, Vision, Signal Processing and Power Applications

Liaquat, S., Mahyuddin, N.M., Naqvi, I.H.: Drone detection using swerling-i model with l-band/x-band radar in free space and raining scenario. In: International Con- ference on Robotics, Vision, Signal Processing and Power Applications. pp. 421–

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.

[1] [1]

IEEE Transactions on Cognitive Communications and Network- ing 6(3), 990–1002 (2020) 12 S

ElMossallamy, M.A., Zhang, H., Song, L., Seddik, K.G., Han, Z., Li, G.Y.: Recon- figurable intelligent surfaces for wireless communications: Principles, challenges, and opportunities. IEEE Transactions on Cognitive Communications and Network- ing 6(3), 990–1002 (2020) 12 S. Liaquat et al

work page 2020

[2] [2]

IEEE Transactions on Communi- cations 70(9), 6259–6276 (2022)

Tang, W., Chen, X., Chen, M.Z., Dai, J.Y., Han, Y., Di Renzo, M., Jin, S., Cheng, Q., Cui, T.J.: Path loss modeling and measurements for reconfigurable intelligent surfaces in the millimeter-wave frequency band. IEEE Transactions on Communi- cations 70(9), 6259–6276 (2022)

work page 2022

[3] [3]

IEEE Communications Surveys & Tutorials 23(3), 1546–1577 (2021)

Liu, Y., Liu, X., Mu, X., Hou, T., Xu, J., Di Renzo, M., Al-Dhahir, N.: Recon- figurable intelligent surfaces: Principles and opportunities. IEEE Communications Surveys & Tutorials 23(3), 1546–1577 (2021)

work page 2021

[4] [4]

IEEE Communications Magazine 58(12), 90–96 (2020)

Björnson, E., Özdogan, O., Larsson, E.G.: Reconfigurable intelligent surfaces: Three myths and two critical questions. IEEE Communications Magazine 58(12), 90–96 (2020)

work page 2020

[5] [5]

IEEE Access 7, 116753–116773 (2019)

Basar, E., Di Renzo, M., De Rosny, J., Debbah, M., Alouini, M.S., Zhang, R.: Wireless communications through reconfigurable intelligent surfaces. IEEE Access 7, 116753–116773 (2019)

work page 2019

[6] [6]

Nature Electronics 3(3), 165–171 (2020)

Zhang, X.G., Jiang, W.X., Jiang, H.L., Wang, Q., Tian, H.W., Bai, L., Luo, Z.J., Sun, S., Luo, Y., Qiu, C.W., et al.: An optically driven digital metasurface for programming electromagnetic functions. Nature Electronics 3(3), 165–171 (2020)

work page 2020

[7] [7]

IEEE Communications Magazine 58(1), 106–112 (2020)

Wu, Q., Zhang, R.: Towards smart and reconfigurable environment: Intelligent reflecting surface aided wireless network. IEEE Communications Magazine 58(1), 106–112 (2020)

work page 2020

[8] [8]

IEEE Journal on Selected Areas in Communications 38(11), 2450–2525 (2020)

Di Renzo, M., Zappone, A., Debbah, M., Alouini, M.S., Yuen, C., de Rosny, J., Tretyakov, S.: Smart radio environments empowered by reconfigurable intelligent surfaces: How it works, state of research, and the road ahead. IEEE Journal on Selected Areas in Communications 38(11), 2450–2525 (2020)

work page 2020