Pith. sign in

REVIEW 4 major objections 5 minor 1 cited by

Temperature-Resilient LC-RIS Phase-Shift Design for Multi-user Downlink Communications

T0 review · 4 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read The paper claims that a temperature-aware phase-shift design for liquid-crystal RISs preserves multi-user fairness when heat shrinks the available phase range.

desk verdict Temperature-aware LC-RIS fairness is a real problem, but the printed SCA linearization of the SINR constraint is algebraically inconsistent and the simulation gains are therefore not yet credible. read the letter →

arxiv 2505.05160 v1 pith:ZOYPUNFK submitted 2025-05-08 eess.SP

Nairy Moghadas Gholian , Mohamadreza Delbari , Vahid Jamali , Arash Asadi This is my paper · ORCID
classification eess.SP
keywords liquidcrystalRISreconfigurableintelligentsurfacetemperature-resilientphasedesignmax-minSINRfairnessmmWavemulti-userMISOalternatingoptimizationsuccessiveconvexapproximationshifterrangeshrinkage
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

The paper argues that liquid-crystal reconfigurable intelligent surfaces (LC-RISs) lose usable phase range when the ambient temperature rises, and that a phase-shift design which treats this shrinkage as a hard constraint keeps the weakest user's signal-to-interference-plus-noise ratio (SINR) high. It formulates the fairness goal as a max-min SINR problem with a temperature-dependent upper bound on every phase, then solves it by alternating a semidefinite-relaxation precoder step with a successive-convex-approximation phase step. The reported simulations show the proposed scheme beating a temperature-blind design, a random-phase design, and a no-RIS baseline across surface sizes, temperatures, and transmit powers. If the result holds, LC-RIS deployments in warm climates should re-optimize their phases within the shrunken range rather than trusting settings computed at a reference temperature.

What carries the argument

The load-bearing object is the temperature-dependent maximum phase shift $\theta_{\max}(T)=2\pi\left(\frac{T_c-T}{T_c-T_r}\right)^{\alpha}$, which converts ambient temperature into a per-element upper bound on the phase variables. The second mechanism is the successive convex approximation of the phase subproblem: expanding $e^{i\theta_n}$ to first order around the previous iterate turns the non-convex SINR inequalities into the linear constraint $\widetilde{C3}$, while the precoder subproblem is handled by semidefinite relaxation with bisection. Together these form the alternating optimization loop that the paper calls Algorithm 1.

What would settle it

On a small instance (for example $N=16$, $K=2$, $\theta_{\max}=\pi$), run the proposed algorithm and then evaluate the true SINR at the returned phase vector; if the achieved minimum SINR falls below the $\kappa$ value produced by the linearized constraint, the Taylor surrogate is not faithful. A second check is algebraic: substituting the expansions into the original SINR inequality should reproduce the linearized constraint term by term, so a symbolic or numeric expansion can settle whether that constraint actually represents the original SINR bound.

Watch

Extended reading notes

Core claim

The paper's central claim is that temperature is not a nuisance to be calibrated away but a first-class constraint on the RIS optimization: when the ambient temperature $T$ exceeds the reference temperature $T_r$, each LC element can only provide phases in $[0,\theta_{\max}(T)]$ with $\theta_{\max}(T)=2\pi\left(\frac{T_c-T}{T_c-T_r}\right)^{\alpha}$, and a design that respects this bound preserves multi-user fairness. The authors derive this bound from the birefringence model of nematic liquid crystals, embed it in a max-min SINR program, and solve the phase subproblem by a first-order Taylor expansion of $e^{i\theta_n}$ that renders the SINR constraints linear. Their simulations indicate that the temperature-aware design maintains a substantially higher minimum SINR than a design that ignores the temperature effect, with only $O(N)$ complexity per phase update.

Load-bearing premise

The phase-update step depends on the assumption that at every iteration each phase changes only a tiny amount, so that the squared terms dropped from the Taylor expansion really are negligible; the algorithm has no mechanism that forces such tiny changes.

Editorial extensions

If this is right

  • Operators deploying LC-RIS in hot environments should treat the surface's phase range as a known function of current temperature and re-solve the phase configuration when the temperature changes.
  • The $O(N)$ phase-update complexity makes the approach practical for large LC-RIS panels, where a competing semidefinite-relaxation approach would scale as $O(N^3)$.
  • Temperature-blind phase designs lose most of the RIS beamforming gain once $T$ exceeds $T_r$; the paper's numbers show that simply raising transmit power does not recover that loss efficiently.
  • The same temperature constraint can be ported to any LC-based phase shifter network, including reflectarrays and transmit arrays, not only RISs.

Reading between the lines

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

  • The hardware model suggests a material-level design lever the paper does not optimize: a liquid crystal with a higher clearing temperature $T_c$, or a cell gap chosen to leave margin at the reference temperature, would delay the phase-range shrinkage and could be combined with the proposed algorithm.
  • A trust-region or step-size control that enforces tiny phase updates would make the Taylor surrogate internally consistent; without it, the algorithm's achieved SINR should be checked directly against the true constraint at the returned phases.
  • Because the phase bound applies element-wise, the same formulation carries over to the paper's stated future scenarios of inhomogeneous temperature across the surface and multiple LC-RISs, using per-element temperature maps.
  • The max-min SINR objective could be replaced by sum-rate or energy-efficiency objectives under the same temperature bound; the linearization would need to be re-derived for those metrics.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper considers an LC-RIS-assisted multi-user MISO downlink system and studies the impact of ambient temperature on the achievable phase-shift range of the RIS elements. The authors formulate a max-min SINR optimization problem with a temperature-dependent phase constraint and propose an alternating optimization algorithm: the BS precoder is optimized via SDR with bisection, and the RIS phase shifts are optimized via successive convex approximation (SCA) based on a first-order Taylor expansion of the SINR constraints. Simulation results are presented showing that the proposed temperature-resilient design outperforms a temperature-neglecting baseline, a random-phase baseline, and a no-RIS baseline across different RIS sizes, temperatures, and transmit powers. The claimed contribution is a scalable O(N) phase-shift optimization that accounts for temperature-induced phase-range shrinkage.

Significance. If the proposed method is correct, it addresses a practical and under-studied problem: the thermal sensitivity of LC-RIS phase shifters. The temperature model itself is a strength: it is externally grounded in the LC physics literature (Eqs. (3)-(5) from [15,16]) and the paper re-derives the temperature-dependent maximum phase shift in Eq. (9) rather than introducing an ad-hoc model. The idea of handling the reduced phase range via a constraint is natural, and the SCA approach is, in principle, more scalable than the O(N^3) SDR used in the prior single-user work [13]. However, the central derivation of the linearized SINR constraint contains algebraic inconsistencies and the algorithm lacks a mechanism to keep the linearization valid, so the simulation evidence for the headline improvement is not currently backed by a valid optimization formulation.

major comments (4)
  1. [§III-C, Eqs. (23)-(24)] The printed linearized constraint cC3 is algebraically inconsistent with the first-order expansion of the SINR constraint (14)-(15). In Eq. (15), the noise term appears as -κ σ_k^2, but Eq. (23) contains -κ(Σ_{j≠k}|s_{k,j}|^2 - σ_k^2), which flips the sign to +κ σ_k^2. Moreover, Eq. (23) has only a single sum Σ_n c_n^{(k,j)} with no sum over the interfering users j≠k, even though the interference term in Eq. (15) is a sum over j. Equation (24) defines c_n^{(k,j)} for one fixed j and therefore omits the required double summation over j and n. The desired-signal term 2Re{s_k^{(t-1)*} i e^{iθ_n^{(t-1)}} a_n^{(k)} δθ_n} and the interference term should appear as separate sums; folding them into one coefficient indexed by (k,j) is not a faithful linearization. As written, the constraint cannot be used to reproduce the expansion of Eq. (14).
  2. [§III-C, just after Eq. (22)] The second-order terms |δs_k|^2 and |δs_{k,j}|^2 are dropped on the grounds that δθ_n ≪ 1, but problem P3 in Eq. (25) imposes no trust region, step-size bound, or penalty to keep δθ_n small. Constraint C2 only restricts the absolute value of each θ_n, not the change per iteration. A feasibility solve over the linearized constraint can therefore return large updates that violate the assumption underlying the Taylor expansion, so the iterates may not converge to a point satisfying the true SINR constraint. The authors should add a trust region (e.g., ||θ - θ^{(t-1)}||∞ ≤ Δ) or a backtracking line search to enforce validity of the linearization, and should state the resulting convergence properties.
  3. [§IV, Figs. 3-4] Because of the issues with Eqs. (23)-(24), the reported max-min SINR gains are not verifiable from the manuscript. If the implementation followed the printed equations, the final θ may violate the true SINR constraints and the reported κ would not be an achievable SINR. If the implementation used a corrected but unprinted linearization, the paper is not reproducible from the derivation. The authors need to provide either a corrected derivation of the linearized constraint, a detailed statement of the actual constraint used in the simulations, or release the code; without this, the simulation evidence for the headline claim is inconclusive.
  4. [§III-C, Complexity Analysis] The claim that the phase-shifter subproblem is solved in O(N) complexity is not supported. Even a linear feasibility problem with N variables and one constraint solved via a general-purpose interior-point solver like CVX typically has complexity O(N^3) or higher, not O(N). The total complexity expression O(Imax((KM)^3.5 + N)) therefore understates the actual cost. This does not invalidate the temperature-resilient design, but it weakens the scalability comparison with the O(N^3) SDR of [13] and should be corrected or justified with a specific solver and iteration count.
minor comments (5)
  1. [§II, Eq. (12b)] Constraint C1 is written as Σ_{k=1}^K ||w_k||^2 ≤ P, ∀k, but the '∀k' is inappropriate; the constraint is a single global power budget, not a per-user constraint.
  2. [§III-C, Eq. (24)] Equation (24) uses δθ_n without the iteration superscript (t), and the expression ends with a stray '·' after the bracket. Please make the notation consistent with Eq. (18).
  3. [§IV-A, Table I] The temperature T is given as 55°C while Tr and Tc are in Kelvin. Since Eq. (9) requires absolute temperature, the conversion from Celsius to Kelvin should be stated explicitly in the text and figure axes should be labeled consistently.
  4. [References [18] and [20]] There are typos in the references: 'beamforing' should be 'beamforming', and 'V eh.' should be 'Ve h.' in [18]; reference [20] has an inconsistent version/access date format.
  5. [§III-C, Eq. (23)] The naming 'cC3' is confusing; please use a more descriptive name such as C3_linear or C̄3, and ensure the notation for the constraint matches between Eqs. (23) and (25).

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the temperature model is re-derived from external LC physics and the claimed gains are evaluated against baselines, not generated by fitted inputs.

full rationale

The paper's load-bearing derivation is the temperature-dependent phase-shift range in Eqs. (3)-(9). Although Eq. (9) is attributed to the authors' prior work [13], the paper re-derives it inside the text from the external four-parameter refractive-index model [15,16], the birefringence expression, and the reference-temperature calibration theta_max(T_r)=2*pi. The central claim that a temperature-resilient phase-shift design improves max-min SINR is supported by a standard AO/SDR/SCA pipeline and by simulation comparisons against a temperature-neglecting baseline, a random-phase baseline, and a no-RIS baseline. No fitted parameter from these simulations is repackaged as a prediction, and no constraint in P1-P3 is defined in terms of the claimed output. The self-citations in [12]-[14] and [21] concern hardware assumptions, a prior single-user problem, and a saturation example; none shoulders the central derivation. The algebraic concerns about Eqs. (23)-(24), such as the missing j-summation, the sign of sigma_k^2, and the absence of a trust region, are correctness and fidelity issues of the SCA surrogate rather than circularity: a poorly matched linear constraint does not make the result equivalent to its inputs by construction. Therefore no circular step is exhibited.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The central claim rests on an empirical temperature-phase model (Eq. 9) imported from LC physics and on the algorithm's linearization being a faithful surrogate. The paper does not fit any constants to its own data, but it omits the values of α and σ, which prevents reproduction.

free parameters (3)
  • alpha (LC molecule structure exponent) = not specified in paper (range 0.20-0.25 from [16])
    Appears in Eq. (9), θmax(T) = 2π((Tc-T)/(Tc-Tr))^α. The paper never states the value used in simulations, so the temperature-dependent phase range, and hence all results, are not reproducible.
  • Tc (clearing temperature) = 400 K (Table I)
    Material-specific parameter chosen for the simulation. The phase range formula and the operating temperature T=55°C depend on it; different LC mixtures have different Tc.
  • Pathloss exponent sigma = not specified
    The distance-dependent pathloss model PL = C0(d0/d)^σ is defined in Section IV-A, but σ never appears in Table I or the text. Channel gains and thus SINR values depend on it.
assumptions (4)
  • domain assumption The four-parameter model for ne(T) and no(T) (Eqs. 3-4) faithfully describes nematic LC refractive indices over the operating range.
    Taken from [15,16]; the temperature model (9) is derived from it. If the empirical model fails outside the tested range, the constraint C2 is wrong.
  • domain assumption Each LC-RIS element can realize any phase in [0, θmax(T)] continuously and with unit amplitude reflection |β_n|=1.
    Stated in Section II-A and used in C2 (Eq. 12c). Real hardware may have discrete or non-uniform phase responses; the paper assumes a perfect continuous range.
  • ad hoc to paper The first-order Taylor expansions in Eqs. (19)-(22) are accurate approximations for the SINR constraints (δθ_n << 1).
    The paper states second-order terms are negligible but provides no step-size control or convergence proof; the validity of this assumption determines whether the SCA updates solve the original problem.
  • domain assumption Perfect instantaneous CSI is available at the BS and RIS controller.
    The paper does not discuss channel estimation; the optimization assumes known channels G, hk, hd,k.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Temperature-Resilient LC-RIS Phase-Shift Design for Multi-user Downlink Communications." pith.science (2026). https://pith.science/paper/ZOYPUNFK

@misc{pith2026250505160,
  author       = {Pith},
  title        = {Pith review of: Temperature-Resilient LC-RIS Phase-Shift Design for Multi-user Downlink Communications},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZOYPUNFK}},
  note         = {Machine review of arXiv:2505.05160}
}
read the original abstract

The reflecting antenna elements in most reconfigurable intelligent surfaces (RISs) use semiconductor-based (e.g., positive-intrinsic-negative (PIN) diodes and varactors) phase shifters. Although effective, a drawback of this technology is the high power consumption and cost, which become particularly prohibitive in millimeter-wave (mmWave)/sub-Terahertz range. With the advances in Liquid Crystals (LCs) in microwave engineering, we have observed a new trend in using LC for realizing phase shifter networks of RISs. LC-RISs are expected to significantly reduce the fabrication costs and power consumption. However, the nematic LC molecules are sensitive to temperature variations. Therefore, implementing LC-RIS in geographical regions with varying temperatures requires temperature-resilient designs. The mentioned temperature variation issue becomes more significant at higher temperatures as the phase shifter range reduces in warmer conditions, whereas it expands in cooler ones. In this paper, we study the impact of temperature on the operation of LC-RISs and develop a temperature-resilient phase shift design. Specifically, we formulate a max-min signal-to-interference-plus-noise ratio optimization for a multi-user downlink mmWave network that accounts for the impact of temperature in the LC-RIS phase shifts. The simulation results demonstrate a significant improvement for the considered set of parameters when using our algorithm compared to the baseline approach, which neglects the temperature effects.

Figures

Figures reproduced from arXiv: 2505.05160 by the authors.

Figure 1
Figure 1. An LC-RIS assisted mmWave wireless network, where the LoS link between BS and UE is blocked. LC-RIS deployed as a uniform planar array (UPA) comprises N = Nx×Nz antenna elements, where Nx and Nz denote the numbers of reflecting elements arranged along the horizontal (x-axis) and vertical (z-axis) dimensions, respectively. Both the BS and RIS antenna arrays maintain half-wavelength spacing dc = λ/2, where λ denotes t… view at source ↗
Figure 2
Figure 2. A single LC-RIS antenna element shown under two conditions: (a) unbiased, and (b) fully biased, aligning the LC molecules for maximum permittivity [14]. B. Impact of temperature on LC-RIS unit-cells With the temperature alteration, the generated phase shift of each LC cell changes. Typically, the LC-RIS is designed such that each LC cell can provide any phase in the range of [0, 2π] at a reference temperature, denot… view at source ↗
Figure 3
Figure 3. Minimum SINR performance comparison under different configura￾tions. (a) Impact of the number of RIS elements on the minimum SINR. (b) Impact of ambient temperature (in ◦C) on the minimum SINR. 15 20 25 30 35 40 −20 −10 0 10 20 30 40 Transmit Power (dBm) Minimum SINR (dB) Upper bound (no temperature change) Proposed (temperature-resilient) Benchmark 1 (temperature-neglecting) Benchmark 2 (random phase-shift) Benchma… view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Impact of BS transmit power (dBm) on the minimum SINR perfor￾mance. e.g., [21, [PITH_FULL_IMAGE:figures/full_fig_p006_4.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

  1. Near-field Liquid Crystal RIS Phase-Shift Design for Secure Wideband Illumination

    eess.SP 2025-08 reject novelty 5.0 of 10

    A frequency-aware LC-RIS phase-shift design raises simulated worst-case secrecy rate to about 2 bits/symbol over 8 GHz at 60 GHz versus benchmarks that ignore the LC frequency response.

Reference graph

Works this paper leans on

21 extracted references · 12 canonical work pages · cited by 1 Pith paper

  1. [13]

    Temperature-aware phase-shift design of LC-RIS for secure communication,

    M. Delbari, B. Wang, N. M. Gholian, A. Asadi, and V . Jamali, “Temperature-aware phase-shift design of LC-RIS for secure communication,” in Proc. IEEE ICC, 2025, Accepted. arXiv:2411.12342. [Online]. Available: https://arxiv.org/abs/2411.12342

    arXiv 2025

  2. [1]

    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 Trans. Wireless Commun., vol. 18, no. 11, pp. 5394–5409, 2019

    2019

  3. [2]

    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 Trans. Wireless Commun. , vol. 18, no. 8, pp. 4157–4170, 2019

    2019

  4. [3]

    Robust and secure wireless communications via intelligent reflecting surfaces,

    X. Yu, D. Xu, Y . Sun, D. W. K. Ng, and R. Schober, “Robust and secure wireless communications via intelligent reflecting surfaces,” IEEE J. Sel. Areas Commun., vol. 38, no. 11, pp. 2637–2652, 2020

    2020

  5. [4]

    Smart and reconfigurable wireless communications: From IRS modeling to algorithm design,

    X. Yu, V . Jamali, D. Xu, D. W. K. Ng, and R. Schober, “Smart and reconfigurable wireless communications: From IRS modeling to algorithm design,” IEEE Wireless Commun. , vol. 28, no. 6, pp. 118– 125, 2021

    2021

  6. [5]

    A leakage-based method for mitigation of faulty reconfigurable intelligent surfaces,

    N. M. Gholian, M. Rossanese, P. Mursia, A. Garcia-Saavedra, A. Asadi, V . Sciancalepore, and X. Costa-Pérez, “A leakage-based method for mitigation of faulty reconfigurable intelligent surfaces,” in Proc. IEEE GLOBECOM, Dec. 2023, pp. 2009–2014

    work page 2023

  7. [6]

    Reconfigurable intelligent surface-based wireless communications: Antenna design, pro- totyping, and experimental results,

    L. Dai, B. Wang, M. Wang, X. Yang, J. Tan, S. Bi, S. Xu, F. Yang, Z. Chen, M. D. Renzo, C.-B. Chae, and L. Hanzo, “Reconfigurable intelligent surface-based wireless communications: Antenna design, pro- totyping, and experimental results,” IEEE Access , vol. 8, pp. 45 913– 45 923, 2020

    2020

  8. [7]

    A reconfigurable intelligent surface at mmWave based on a binary phase tunable metasurface,

    J.-B. Gros, V . Popov, M. A. Odit, V . Lenets, and G. Lerosey, “A reconfigurable intelligent surface at mmWave based on a binary phase tunable metasurface,” IEEE Open J. Commun. Soc. , vol. 2, pp. 1055– 1064, 2021

    work page 2021

Show all 21 references
  1. [8]

    mmWave metasurface unit cells achieving millisecond response through polymer network liquid crystals,

    R. Guirado, G. Perez-Palomino, M. Caño García, M. A. Geday, and E. Carrasco, “mmWave metasurface unit cells achieving millisecond response through polymer network liquid crystals,” IEEE Access, vol. 10, pp. 127 928–127 938, 2022

    2022

  2. [9]

    Temperature characterization of liquid crystal dielectric image line phase shifter for millimeter-wave applications,

    H. Tesmer, R. Razzouk, E. Polat, D. Wang, R. Jakoby, and H. Maune, “Temperature characterization of liquid crystal dielectric image line phase shifter for millimeter-wave applications,” Crystals, vol. 11, no. 1, p. 63, 2021

    2021

  3. [10]

    A reconfigurable reflectarray antenna with an 8 µm-thick layer of liquid crystal,

    W. Zhang, Y . Li, and Z. Zhang, “A reconfigurable reflectarray antenna with an 8 µm-thick layer of liquid crystal,” IEEE Trans. Antennas Propag., vol. 70, no. 4, pp. 2770–2778, 2022

    2022

  4. [11]

    Reconfigurable intelligent surfaces with liquid crystal technology: A hardware design and communication perspective,

    A. Jiménez-Sáez, A. Asadi, R. Neuder, M. Delbari, and V . Jamali, “Reconfigurable intelligent surfaces with liquid crystal technology: A hardware design and communication perspective,” 2023, arXiv:2308.03065. [Online]. Available: https://arxiv.org/abs/2308.03065

    2023 arXiv

  5. [12]

    Fast transition-aware reconfiguration of liquid crystal-based RISs,

    M. Delbari, R. Neuder, A. Jiménez-Sáez, A. Asadi, and V . Jamali, “Fast transition-aware reconfiguration of liquid crystal-based RISs,” in Proc. IEEE ICC Workshops , 2024, pp. 214–219

    2024

  6. [14]

    Architecture for sub-100 ms liquid crystal reconfigurable intelligent surface based on defected delay lines,

    R. Neuder, M. Späth, M. Schüßler, and A. Jiménez-Sáez, “Architecture for sub-100 ms liquid crystal reconfigurable intelligent surface based on defected delay lines,” Commun. Eng. , vol. 3, no. 1, p. 70, 2024

    2024

  7. [15]

    Studies of liquid crystal response time,

    H. Wang, “Studies of liquid crystal response time,” Ph.D. dissertation, University of Central Florida, 2005, [Online]. Available: https://stars. library.ucf.edu/etd/632

    2005

  8. [16]

    Temperature effect on liquid crystal refractive indices,

    J. Li, S. Gauza, and S.-T. Wu, “Temperature effect on liquid crystal refractive indices,” J. of Applied Physics , vol. 96, no. 1, pp. 19–24, 2004

    2004

  9. [17]

    Boyd and L

    S. Boyd and L. Vandenberghe, Convex optimization . Cambridge University Press, 2004

    2004

  10. [18]

    Max- Min fair beamforing design for a RIS-assisted system with SWIPT,

    Y . Wei, Z. Peng, J. Tang, X. Zhang, K.-K. Wong, and J. Chambers, “Max- Min fair beamforing design for a RIS-assisted system with SWIPT,” IEEE Trans. V eh. Technol., vol. 73, no. 8, pp. 12 148–12 153, 2024

    2024

  11. [19]

    Graph implementations for nonsmooth convex programs,

    M. Grant and S. Boyd, “Graph implementations for nonsmooth convex programs,” in Recent Advances in Learning and Control , V . D. Blondel, S. P. Boyd, and H. Kimura, Eds. London: Springer London, 2008, pp. 95–110

    2008

  12. [20]

    CVX: Matlab Software for Disciplined Convex Programming, version 2.1,

    ——, “CVX: Matlab Software for Disciplined Convex Programming, version 2.1,” https://cvxr.com/cvx, 2014, accessed March 2025

    2014

  13. [21]

    Near-field multipath MIMO channel model for imperfect surface reflection,

    M. Delbari, G. C. Alexandropoulos, R. Schober, H. V . Poor, and V . Jamali, “Near-field multipath MIMO channel model for imperfect surface reflection,” in Proc. IEEE GLOBECOM Workshops , 2024, Accepted. arXiv:2409.17041. [Online]. Available: https://arxiv.org/pdf/ 2409.17041

    2024 arXiv

Pith tools

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