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REVIEW 3 major objections 8 minor 12 references

Deployment Optimization for XL-IRS Assisted Multi-User Communications

T0 review · 3 major / 8 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read XL-IRS deployment should put the surface near the user for beamforming gain and near the base station for multiplexing.

desk verdict Plausible deployment rule for near-field XL-IRS, but the multi-user algorithm is mis-specified and the 'closed-form' is a search, so the evidence is not yet reliable. read the letter →

arxiv 2504.19550 v1 pith:A4QP5HF2 submitted 2025-04-28 eess.SP

Chao Zhou , Changsheng You , Tianyu Liu , Bin Lyu This is my paper · ORCID
classification eess.SP
keywords XL-IRSdeploymentoptimizationnear-fieldsphericalwavefrontsbeamforminggainmultiplexingeffectivedegreesoffreedomalternatingsuccessiveconvexapproximation
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 studies where to place an extremely large-scale intelligent reflecting surface (XL-IRS) in a multi-user system when the XL-IRS scatters signals modeled by near-field spherical wavefronts. For a single user, it shows that deploying the XL-IRS near the user rather than the base station yields a higher received SNR, and this preference strengthens as the number of base-station antennas grows. For multiple users, it shows that user-side placement buys near-field beam-focusing gain while BS-side placement buys extra spatial multiplexing from the additional effective degrees of freedom of the BS–IRS channel. The paper also derives a closed-form received-SNR expression that captures both the channel's eigenvalues and a correlation ratio, and reports that this expression tracks the alternating-optimization benchmark closely at far lower complexity.

What carries the argument

The argument runs on two quantities. The first is the beamforming-gain expression $\max_i\{\kappa_i \mu_i\}$, where $\mu_i$ are the eigenvalues of $\bar{G}=GG^H$ and $\kappa_i=\theta_i^H \bar{G}\theta_i/(\psi_i^H \bar{G}\psi_i)$ is the correlation ratio between the constant-modulus phase vector and the corresponding eigenvector; it decides whether phase compensation is almost perfect. The second is the effective degree of freedom $\mathrm{EDoF}=(\mathrm{tr}(\bar{G})/\|\bar{G}\|_F)^2$, which measures how many independent spatial streams the BS–IRS channel can support. The paper uses the first to explain why user-side placement wins for a single user, and the second to explain why BS-side placement wins for multiplexing.

What would settle it

Compute or measure the effective DoF (18) of the BS–IRS channel at the user-side location that Fig. 5 recommends. If $\mathrm{EDoF}(\bar{G})$ is clearly above 1 there, or if the rate achieved with closed-form (13) falls noticeably below the AO benchmark in a LoS experiment with the same geometry, the rank-one premise and the deployment rule would be contradicted.

Watch

Extended reading notes

Core claim

The central claim is that deployment side determines which near-field effect dominates. When the XL-IRS is placed close to the user, the BS–IRS channel $G$ becomes approximately rank-one (spherical wavefronts flatten to planar), so the reflecting surface can compensate all phase differences and nearly reach the upper-bound SNR $\beta M N^2/(d_{BI}^2 d_{I,1}^2 \sigma_1^2)$. When it is placed close to the base station, $G$ retains rank larger than one under spherical wavefronts, so phase compensation is imperfect and single-user beamforming gain is lower, but the extra rank supplies effective degrees of freedom that can be used to multiplex several users. The multi-user results show a concrete trade-off: BS-side placement can roughly double the sum-rate through spatial multiplexing, while user-side placement focuses all power into one user's beamforming gain. The closed-form SNR in Eq. (13), $\mathrm{SNR}^{closed}_1 = \beta N \max_i\{\kappa_i \mu_i\}/(d_{BI}^2 d_{I,1}^2 \sigma_1^2)$, is presented as more accurate than both the upper bound and the eigenvalue-only approximation.

Load-bearing premise

The user-side deployment recommendation assumes that when the XL-IRS is close to the user, the BS–IRS channel $\bar{G}$ has effective rank essentially one, so all its phases can be compensated; if that rank stays appreciably above one at user-side distances, the beamforming-gain advantage over BS-side deployment weakens.

Editorial extensions

If this is right

  • In a single-user XL-IRS link with many BS antennas, moving the IRS from the BS side to the user side can raise the achievable rate from about 5.4 to 7.9 bit/s/Hz in the paper's $M=64$ setup.
  • For multi-user operation, placing the XL-IRS near the BS can approximately double the sum-rate relative to the single-user benchmark, because the near-field channel provides extra effective DoF for spatial multiplexing.
  • Once the IRS is far enough from the BS (about 42 m in the simulation), the sum-rate collapses onto the single-user curve, indicating all spatial resources are being spent on focus rather than multiplexing.
  • The closed-form SNR expression (13) offers a low-complexity deployment predictor that does not require iterating transmit beamforming and reflection phases.

Reading between the lines

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

  • Editorial inference: the same rank-one argument predicts that with rich multi-path scattering, the BS–IRS channel may not collapse to rank one even at the user side, so the user-side advantage would shrink; testing the deployment rule under multi-path channels is a natural next step.
  • Editorial inference: the effective-DoF criterion $\mathrm{EDoF}(G)\ge K$ could be turned into a feasibility constraint for placement, letting a system choose the nearest user-side location that still supports $K$ streams.
  • Editorial inference: for mobile users, the placement would need to track the user cluster; the curves in Fig. 6 suggest a threshold distance beyond which beamforming dominates, which could drive a simple handoff rule.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 8 minor

Summary. The paper studies deployment optimization for an XL-IRS assisted multi-user system with near-field spherical wavefront channels. For the single-user case, it proposes an AO-based algorithm and three analytical SNR expressions (an upper bound, an eigenvector-based approximation, and a so-called closed form) to argue that the XL-IRS should be deployed near the user rather than near the BS when the BS has many antennas. For the multi-user case, it proposes an AO-SCA algorithm to maximize the sum rate and introduces an effective DoF metric to characterize the beamforming-versus-multiplexing trade-off. Numerical results are used to support the deployment recommendations.

Significance. If the results are correct, the paper provides practically relevant insights for XL-IRS placement: near-optimal placement tends to be at either the BS or the user, and the preference shifts to the user side as the number of BS antennas grows, while multi-user spatial multiplexing benefits from placing the XL-IRS near the BS. The use of effective DoF to explain the trade-off between beamforming gain and multiplexing gain is a useful framing. However, two load-bearing technical issues—the nonconvexity of the multi-user reflection-optimization subproblem and the heuristic nature of the claimed closed-form SNR expression—prevent the quantitative claims (especially Fig. 6) from being reproducible from the text as written. These issues are correctable within the scope of the manuscript, so a major revision is warranted rather than rejection.

major comments (3)
  1. [§IV-B, problem (P3.2)]
  2. [§III, Eq. (13) and Remark 2]
  3. [§III, derivation of (P2.2)]
minor comments (8)
  1. [§III heading]
  2. [§II-A, Eq. (1)]
  3. [§II-A, Eq. (2)]
  4. [§III, Eq. (13) and following text]
  5. [§IV-B, convergence claim]
  6. [Remarks 1 and 3]
  7. [Fig. 3]
  8. [V, Fig. 6]

Circularity Check

0 steps flagged · score 1.0 of 10

No construction-level circularity: SNR expressions and deployment conclusions follow from the stated channel model; self-citations are ancillary and the main issue is a convexity gap, not circularity.

full rationale

The derivation chain is self-contained. (P2) is maximized by the matched-filter/phase-alignment pair (8)-(9); the M=1 SNR in (10) is the product-distance path-loss expression; (11) is the perfect-phase-offset upper bound; (12) is the relaxed principal-eigenvector approximation; and (13) is an explicit unit-modulus projection heuristic with theta_i = e^{j arg(psi_i)}, selecting the best i by max_i kappa_i mu_i. None of these expressions is fitted to the deployment claim or to the AO curve; they are compared against AO as benchmarks. The near-user placement conclusion relies on rank(G) approximately 1 near the user, imported from the external near-field tutorial [10], not from the paper's own target result. The EDoF metric (18) is taken from [8] as a standard measure and is used only to explain the multiplexing/beamforming trade-off, not to define it. The remaining author-overlap citations ([8], [11]) are to published work and are not load-bearing: [11] supplies a generic SCA template whose convergence claim is invoked for the AO algorithm. I do not count this as circular because the paper states the SCA lower bounds explicitly and the cited result is external. The one serious internal flaw is Section IV-B: (P3.2) is claimed to be convex because of the SCA lower bound, but the unit-modulus constraint |phi_n|^2 = 1 is nonconvex, so the CVX/convergence statement is not justified; this is a correctness/reproducibility concern, not a circular reduction of the paper's inputs to its outputs.

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

The paper relies on standard near-field LoS channel model assumptions; no free parameters are fitted, and no new physical entities are introduced. The 'correlation ratio' and the candidate set in (13) are mathematical constructs within the derivation, not independent postulates.

assumptions (5)
  • domain assumption LoS channel with uniform spherical wavefronts, amplitudes approximated by center distances d_BI and d_{I,k}
    Used in the channel model (1)-(2); the paper says it focuses on LoS for simplicity and the closed-form in (13) also applies to multi-path scenarios, but the analytical expressions are derived for LoS.
  • domain assumption Direct BS-user links are blocked
    Stated at the start of Section II; without this, the system would have direct signals and the deployment problem changes.
  • domain assumption Phase differences in r1 can be perfectly offset by the IRS phase shifts
    Used to simplify (P2.1) to (P2.2) and to derive (10)-(13); relies on the user-IRS channel amplitudes being uniform across elements, which holds under the center-distance amplitude model.
  • domain assumption When the XL-IRS is near the user, rank(G) approximately equals 1 (spherical wavefronts transition to planar)
    Invoked in Remark 1 and Remark 3 to argue that the BS-IRS channel can be phase-compensated perfectly, yielding the recommendation to place the XL-IRS near the user.
  • ad hoc to paper The SCA lower-bound problems (P3.1) and (P3.2) are convex
    The paper states 'Since problem (P3.2) is convex' after imposing unit-modulus constraints (16), which are nonconvex; this is a questionable assertion, not derived.

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Cite this review

Pith. "Pith review of Deployment Optimization for XL-IRS Assisted Multi-User Communications." pith.science (2026). https://pith.science/paper/A4QP5HF2

@misc{pith2026250419550,
  author       = {Pith},
  title        = {Pith review of: Deployment Optimization for XL-IRS Assisted Multi-User Communications},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/A4QP5HF2}},
  note         = {Machine review of arXiv:2504.19550}
}
read the original abstract

In this paper, we study the deployment optimization for an extremely large-scale intelligent reflecting surface (XL-IRS) assisted multi-user communication system, within which the channels between the XL-IRS and the BS (or user) are modeled by the near-field spherical wavefronts. To draw some valuable insights, we first consider the single-user case, where an alternating optimization (AO) based algorithm is devised to maximize the received signal-to-noise ratio (SNR) at the user. To address the high computational complexity issue incurred by the AO based algorithm, three approximate received SNR expressions are obtained to yield useful insights, corresponding to the upper bound, approximate expression, and closed-form. It is demonstrated that the XL-IRS ought to be positioned near the user (rather than the BS) to obtain a higher beamforming gain. Then, for the multi-user scenario, an efficient algorithm is proposed to obtain a high-quality XL-IRS placement solution by using the AO and successive convex approximation (SCA) techniques. Furthermore, the effective degree of freedom (DoF) of the BS-IRS channel is provided, which indicates that the additional effective DoF can be leveraged to improve multi-user spatial multiplexing. Last, numerical results confirm the existence of a trade-off between near-field beam-focusing gain and multiplexing gain.

Figures

Figures reproduced from arXiv: 2504.19550 by the authors.

Figure 1
Figure 1. An XL-IRS assisted multi-user system. II. SYSTEM MODEL AND PROBLEM FORMULATION As shown in [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Achievable rate under the single-user scenario with [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Normalized value. algorithm for maximizing the sum-rate through alternately optimizing the transmit vector wk and reflection matrix Θ. A. Optimization of transmit beamforming We first design the transmit beamforming vector wk given fixed Θ. The objective value is non-convex due to the complex signal-to-interference-plus-noise-ratio (SINR) and the quadratic form expressions. To address this issue, we employ the SCA t… view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: DoF versus horizontal location of XL-IRS. [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 5
Figure 5. Figure 5: Achievable rate under the single-user scenario with [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 6
Figure 6. Figure 6: Achievable sum-rate under the multi-user scenario. [PITH_FULL_IMAGE:figures/full_fig_p006_6.png]

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Reference graph

Works this paper leans on

12 extracted references · 7 canonical work pages

  1. [1]

    Next generation advanced transceiver technologies for 6G and beyond,

    C. Y ou, Y . Cai, Y . Liu, M. Di Renzo, T. M. Duman, A. Y ener, an d A. L. Swindlehurst, “Next generation advanced transceiver technologies for 6G and beyond,” arXiv preprint arXiv:2403.16458 , 2024

    arXiv 2024

  2. [2]

    Target sensi ng with intelligent reflecting surface: Architecture and performa nce,

    X. Shao, C. Y ou, W. Ma, X. Chen, and R. Zhang, “Target sensi ng with intelligent reflecting surface: Architecture and performa nce,” IEEE J. Sel. Areas Commun. , vol. 40, no. 7, pp. 2070–2084, Jul. 2022

    2022

  3. [3]

    Intellige nt reflecting surface-aided wireless communications: A tutor ial,

    Q. Wu, S. Zhang, B. Zheng, C. Y ou, and R. Zhang, “Intellige nt reflecting surface-aided wireless communications: A tutor ial,” IEEE Trans. Commun., vol. 69, no. 5, pp. 3313–3351, May 2021

    2021

  4. [4]

    How to deploy intel ligent reflecting surfaces in wireless network: BS-side, user-sid e, or both sides?

    C. Y ou, B. Zheng, W. Mei, and R. Zhang, “How to deploy intel ligent reflecting surfaces in wireless network: BS-side, user-sid e, or both sides?” J. Commun. Inf. Netw. , vol. 7, no. 1, pp. 1–10, Mar. 2022

    work page 2022

  5. [5]

    Joint deploy ment and multiple access design for intelligent reflecting surfa ce assisted networks,

    X. Mu, Y . Liu, L. Guo, J. Lin, and R. Schober, “Joint deploy ment and multiple access design for intelligent reflecting surfa ce assisted networks,” IEEE Trans. Wireless Commun. , vol. 20, no. 10, pp. 6648– 6664, Oct. 2021

    work page 2021

  6. [6]

    Intelligent surfaces empowered wireless network: Recent advances and the road to 6G,

    Q. Wu, B. Zheng, C. Y ou, L. Zhu, K. Shen, X. Shao, W. Mei, B. D i, H. Zhang, E. Basar, L. Song, M. D. Renzo, Z.-Q. Luo, and R. Zhan g, “Intelligent surfaces empowered wireless network: Recent advances and the road to 6G,” Proc. IEEE, early access , pp. 1–40, 2024

    work page 2024

  7. [7]

    Base station beamfor ming design in near-field XL-IRS beam training,

    T. Wang, C. Y ou, F. Zhou, and C. Yin, “Base station beamfor ming design in near-field XL-IRS beam training,” IEEE Commun. Lett., vol. 28, no. 4, pp. 932–936, Apr. 2024

    work page 2024

  8. [8]

    A tutorial on near -field XL-MIMO communications towards 6G,

    H. Lu, Y . Zeng, C. Y ou, Y . Han, J. Zhang, Z. Wang, Z. Dong, S. Jin, C.-X. Wang, T. Jiang, X. Y ou, and R. Zhang, “A tutorial on near -field XL-MIMO communications towards 6G,” IEEE Commun. Surveys Tut., early access , 2024

    work page 2024

Show all 12 references
  1. [9]

    Intelligent reflecting surface enhan ced wireless network via joint active and passive beamforming,

    Q. Wu and R. Zhang, “Intelligent reflecting surface enhan ced wireless network via joint active and passive beamforming,” IEEE Trans. Com- mun., vol. 18, no. 11, pp. 5394–5409, Nov. 2019

    2019

  2. [10]

    N ear- field communications: A tutorial review,

    Y . Liu, Z. Wang, J. Xu, C. Ouyang, X. Mu, and R. Schober, “N ear- field communications: A tutorial review,” IEEE Open J. Commun. Soc. , vol. 4, pp. 1999–2049, 2023

    1999

  3. [11]

    Transmit power minimiza- tion for STAR-RIS empowered symbiotic radio communication s,

    C. Zhou, B. Lyu, Y . Feng, and D. T. Hoang, “Transmit power minimiza- tion for STAR-RIS empowered symbiotic radio communication s,” IEEE Trans. Cogn. Commun. Netw. , vol. 9, no. 6, pp. 1641–1656, Dec. 2023

    2023

  4. [12]

    CVX: Matlab software for discipli ned convex programming,

    M. Grant and S. Boyd, “CVX: Matlab software for discipli ned convex programming,” 2016. [Online]. Available: http://cvxr.co m/cvx

    2016

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