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REVIEW 3 major objections 5 minor 38 references

Joint Beamforming and Position Optimization for IRS-Aided SWIPT with Movable Antennas

T0 review · 3 major / 5 minor · reviewed 2026-08-03 · deepseek-v4-flash

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Pith's one-line read The paper claims that jointly optimizing beamforming, IRS phases, and movable-antenna positions raises the weighted sum-rate of information-decoding receivers while meeting energy-harvesting constraints, with IRS phase optimization the larg

desk verdict Solid but incremental joint IRS+MA SWIPT optimization; the feasibility characterization only certifies feasibility, not infeasibility. read the letter →

arxiv 2511.11148 v2 pith:IINTB7SA submitted 2025-11-14 cs.IT math.IT

Yanze Zhu , Qingqing Wu , Xinrong Guan , Ziyuan Zheng , Honghao Wang , Wen Chen , Yang Liu , Yuan Guo This is my paper
classification cs.ITmath.IT
keywords SWIPTintelligentreflectingsurface(IRS)movableantennas(MA)jointbeamformingandpositionoptimizationweightedsum-ratemaximizationfeasibilitycharacterizationWMMSEenergyharvestingreceivers
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 is the first to treat a SWIPT system that combines an IRS with movable antennas at the base station, assuming the direct BS-to-user links are severely blocked and all information and power flow through the IRS. It tries to establish that jointly optimizing BS beamforming, IRS phase shifts, and MA positions can substantially raise the weighted sum-rate of information-decoding receivers while keeping each energy-harvesting receiver above its required received power. Because the resulting optimization is highly nonconvex, the authors build an algorithm from WMMSE, BCD, MM, and PDD, and supplement it with a feasibility test that decides whether the EHR power requirements are achievable at all. Simulation results support the algorithm's convergence and show that in the modeled scenario, optimizing the IRS phases yields a larger performance gain than moving antennas, a comparative finding that would inform how to spend hardware resources.

What carries the argument

The load-bearing construction is the equivalent cascaded channel h_i = h_{r,i} Θ G(tB), built from field-response matrices for the movable-antenna array and the IRS, plus the WMMSE transformation that turns the sum-of-log-rates objective into a weighted mean-square-error form. The solution then alternates: BS beamforming is updated by convexifying the EHR power constraints with first-order Taylor lower bounds (MM); IRS phases are updated by PDD with an auxiliary unit-modulus variable and a closed-form phase projection; and each MA position is updated via MM using quadratic upper/lower surrogates of the trigonometric field-response terms. Feasibility of the EHR power constraints is decided by

What would settle it

Simulate the same system with a non-negligible direct BS–receiver channel (for example, path loss 15–20 dB below the cascaded BS–IRS–receiver path) and compare FPA-OPS against MA-RPS: if MA position optimization yields a larger sum-rate gain than IRS phase optimization, the paper's scenario-dependent comparative conclusion would be overturned.

Watch

Extended reading notes

Core claim

The central claim is that the weighted sum-rate maximization for an IRS-aided SWIPT system with movable antennas can be solved effectively by a decomposition that iterates among WMMSE updates, IRS phase optimization via penalty dual decomposition, and MM-based position updates, while guaranteeing each EHR's received power. The paper further claims that the solution outperforms benchmarks with fixed antennas and/or random IRS phases, and that under the adopted severely-blocked propagation model, optimizing the IRS configuration contributes more to sum-rate than optimizing antenna positions.

Load-bearing premise

The system assumes the direct links between the base station and all information/energy receivers are severely blocked, so every signal path goes through the IRS; if real direct links are not negligible, the relative benefit of IRS phase tuning versus antenna movement could differ.

Editorial extensions

If this is right

  • Operators can use the proposed algorithm to jointly tune beamforming, IRS phases, and MA positions, improving IDR weighted sum-rate for a fixed BS power budget while keeping each EHR above its required received power.
  • The feasibility characterization gives a yes/no check for whether a given EHR power requirement and BS power budget are compatible; if the minimized slack β* is positive, the problem is infeasible.
  • The comparative simulations suggest that in severely blocked direct-link scenarios, investing in IRS phase optimization yields larger returns than antenna repositioning, so system designers may prioritize IRS phase tuning.
  • Under the modeled path-response statistics, increasing the number of MAs and expanding the allowed array region improve performance, though gains saturate at large array sizes.
  • The proposed decomposition inherits monotonic convergence behavior, with the main algorithm increasing the objective across BCD iterations; in tests it converges within tens of iterations.

Reading between the lines

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

  • If the direct BS–receiver link is not negligible, the comparison between IRS phase gains and MA position gains could change; a natural extension is to simulate partial blockage and observe the crossover.
  • The feasibility characterization developed here does not depend on the sum-rate objective and could be lifted into any IRS/MA system with power or QoS constraints, including wirelessly powered IoT networks with strict energy floors.
  • Because MA gains in the field-response model come from selecting favorable phases across multiple propagation paths, the comparative advantage of IRS phases may weaken as the number of paths grows large; testing with larger L would clarify whether the conclusion is an artifact of the L=5 setting.
  • Extending the design to movable IRS elements as well as movable BS antennas is a next step the current model does not include; the same MM/PDD machinery would likely apply.
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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 / 5 minor

Summary. The paper studies an IRS-aided SWIPT system in which the BS is equipped with movable antennas (MAs). It formulates a weighted sum-rate maximization problem for the information-decoding receivers subject to power-harvest constraints at the energy-harvesting receivers, a transmit power budget, IRS phase-shift constraints, and MA position/decoupling constraints. The authors propose a solution based on WMMSE, BCD, majorization-minimization, and penalty dual decomposition, and they additionally present a feasibility characterization method for the EHR power constraints. Simulation results show monotone convergence behavior and performance gains over fixed-position-antenna and/or random-IRS baselines, with the qualitative finding that IRS phase optimization contributes more than MA position optimization under the considered relay-dominated propagation scenario.

Significance. If the results hold, the paper provides one of the first joint treatments of IRS and movable-antenna techniques in a SWIPT system, together with a concrete algorithmic recipe. The algorithmic framework is standard in structure, but its assembly for this problem is nontrivial. The paper is also explicit about its simulation setup and gives a useful comparison across four schemes (FPA/MA with optimized/random IRS). The strongest advertised novelty, however, is the feasibility characterization for the EHR power constraints; this contribution is currently not sound, as detailed below. The sum-rate optimization results are plausible, but the feasibility claim needs substantial correction before the paper can be accepted.

major comments (3)
  1. [Sec. IV, Algorithm 4, Eqs. (25), (32), (56)] The feasibility characterization is one-sided and the claimed infeasibility certification is unsound. In (P6), the constraint is P_E,j - P_j ≤ β, and P_j is convex in the relevant block. All MM surrogates used in Algorithm 4 (Eqs. (25), (32), (56)) are global lower bounds on P_j, so the feasible set of each surrogate subproblem is a subset of the true feasible set. Consequently, the β returned by Algorithm 4 is an upper bound on the optimal value of (P6), not the optimal value itself. A returned β>0 therefore does not rule out the existence of a point outside the surrogate feasible set that satisfies P_E,j - P_j ≤ 0. The statement in Sec. IV-A that a positive β* implies infeasibility of (P1), and the corresponding branch in Algorithm 4, are valid only for a global minimizer of (P6), which BCD/MM does not provide. The figure in Fig. 3 interpreting a positive converged value as infeasibili
  2. [Sec. III-B, Algorithm 2] The paper provides no convergence proof for the joint BCD algorithm. Fig. 2 shows an empirical monotone increase of the WMMSE objective, but the constraint surrogates in the beamforming, IRS, and MA subproblems (e.g., (26a), (33a), (58a)) change the feasible set at every iteration. Standard BSUM or MM convergence results do not directly apply because the feasible sets are not fixed. Since the central claim is an efficient algorithm for the joint problem, the authors should either prove monotonicity/stationarity under the surrogate constraints or clearly state the exact stationarity conditions that the algorithm intends to meet. As written, the convergence is only empirically demonstrated.
  3. [Sec. III-A, Eq. (18)-(19)] The transformation from (P1) to (P2) is stated as an equivalence, but (18) contains only inequalities. The equivalence is standard in WMMSE, but it requires the argument that after optimizing the auxiliary variables v and w for fixed f, the lower bound becomes tight and the optimal value of (P2) equals that of (P1). The paper should either provide this lemma or give a precise citation to a theorem in [29] that establishes it. This is a minor technical gap, but it is load-bearing for the algorithmic derivation, so it should be addressed explicitly.
minor comments (5)
  1. [Sec. II-A, Eq. (5)] The channel G is written as G(˜tB) but its definition in (5) also depends on ˜tR through F_r^G(˜tR). The notation is misleading; either include both arguments or add a sentence that the IRS element positions are fixed for the channel G in the considered problem.
  2. [Sec. II-A] The assumption that direct links between the BS and IDRs/EHRs are severely blocked is load-bearing for the paper's qualitative conclusion that IRS optimization contributes more than MA positioning. The paper is careful to say 'under our considered scenario,' but a sensitivity study with a weak, non-zero direct-link component would help the reader understand the robustness of the comparative claim.
  3. [Sec. III-B, Algorithm 2 and Sec. IV-B, Algorithm 4] Both algorithms require a feasible starting point (e.g., 'Initialize feasible f(0), t_B(0), θ(0)'), but no procedure for constructing such a point is given. The feasibility algorithm of Sec. IV could be used for this purpose when it returns β≤0, but the paper does not explain how; a brief comment would make the algorithms more actionable.
  4. [Sec. IV-B, Algorithm 4] The fractional superscripts for the β updates (e.g., β^(n+1/(M+2))) are confusing. A single loop counter with separate block indices would be more readable.
  5. [Fig. 3] The caption and the text interpret the curve with PB=25 dBm as infeasible without acknowledging the surrogate-subset issue raised in the major comment. This should be revisited after the feasibility method is corrected.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: derivations are self-contained standard transforms; the feasibility concern is a correctness issue, not circularity.

full rationale

The derivation chain is self-contained: (P1) is transformed by the standard WMMSE inequality (18) to (P2), then BCD subproblems are solved by CVX with MM/PDD surrogates (Eqs. (25), (32), (42)-(57)), and the final sum-rate values are benchmarked against FPA/RPS baselines in Figs. 4-7. No parameter is fitted and then relabeled as a prediction; the algorithm optimizes the original variables and the comparisons are external baselines. Self-citations ([4], [8], [9], [17], [22], [25], [28]) appear in the introduction and background, but none supplies the load-bearing result: the channel model is from [15]/[16] and the algorithmic machinery from [29]-[32] is standard and independent. The Sec. IV feasibility criterion is an epigraph reformulation — β is the max violation of the EHR power constraints, so 'β*≤0 iff feasible' is true by construction; this is a legitimate exact reduction, not a self-referential prediction. The skeptical concern about Algorithm 4 is substantive but orthogonal to circularity: because (P7)-(P9) enforce only lower-bound surrogates, the returned β is an upper bound on the true β*, so β>0 cannot certify infeasibility. That is an algorithmic soundness flaw, not a derivation that reduces to its own inputs. No circular step.

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

No free parameters are fitted to data for the derivation; all listed values are simulation settings. The optimization framework is built on standard convex surrogates and does not introduce invented physical entities.

free parameters (3)
  • Path loss exponents = αG = αh,i = αg,j = 2.2
    Set as 2.2 in §V for all links, following [25]; the relative gain of IRS vs MA optimization could depend on these exponents.
  • Number of channel paths = L = 5
    All channels are modeled with L=5 paths in §V; the field-response model's richness affects the MA positioning gain.
  • Network geometry = dG=4m, dh∈[20,25]m, dg∈[4,4.5]m, A=2.5λc
    Chosen in §V; the qualitative conclusion that IRS yields larger gain than MA is tied to this geometry, as acknowledged by 'under our considered scenario'.
assumptions (4)
  • domain assumption Direct BS-user links are severely blocked
    Stated in §II-A; all information and power transfer go through the IRS, which makes IRS phase control the dominant lever and heavily influences the performance ranking.
  • domain assumption Perfect CSI is available
    Stated in Footnote 1; channel estimation errors are ignored, simplifying optimization but not reflecting practical imperfections.
  • domain assumption Quasi-static far-field field-response channel model
    Used in §II-A following [15],[16]; the sinusoidal phase dependence on antenna position is what enables MA position optimization.
  • standard math WMMSE surrogate equivalence
    Used in §III-A to transform the rate objective into surrogate (18); standard but equality holds only at fixed points, so the transformed problem is not globally equivalent in the nonconvex setting.

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Pith. "Pith review of Joint Beamforming and Position Optimization for IRS-Aided SWIPT with Movable Antennas." pith.science (2026). https://pith.science/paper/IINTB7SA

@misc{pith2026251111148,
  author       = {Pith},
  title        = {Pith review of: Joint Beamforming and Position Optimization for IRS-Aided SWIPT with Movable Antennas},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/IINTB7SA}},
  note         = {Machine review of arXiv:2511.11148}
}
read the original abstract

Simultaneous wireless information and power transfer (SWIPT) has been envisioned as a promising technology to support ubiquitous connectivity and reliable sustainability in Internet-of-Things (IoT) networks, which, however, generally suffers from severe attenuation caused by long distance propagation, leading to inefficient wireless power transfer (WPT) for energy harvesting receivers (EHRs). This paper proposes to introduce emerging intelligent reflecting surface (IRS) and movable antenna (MA) technologies into SWIPT systems aiming at enhancing information transmission for information decoding receivers (IDRs) and improving receive power of EHRs. We consider to maximize the weighted sum-rate of IDRs via jointly optimizing the active and passive beamforming at the base station (BS) and IRS, respectively, as well as the positions of MAs, while guaranteeing the requirements of all EHRs. To tackle this challenging task due to the non-convexity of associated optimization, we develop an efficient algorithm combining weighted minimal mean square error (WMMSE), block coordinate descent (BCD), majorization-minimization (MM), and penalty duality decomposition (PDD) frameworks. Besides, we present a feasibility characterization method to examine the achievability of EHRs' requirements. Simulation results demonstrate the significant benefits of our proposed solutions. Particularly, the optimized IRS configuration may exhibit higher performance gain than MA counterpart under our considered scenario.

Figures

Figures reproduced from arXiv: 2511.11148 by the authors.

Figure 1
Figure 1. IRS-aided SWIPT system with MAs. MAs and movable elements into the BS and IRS, respectively. Despite the aforementioned progresses that have been made in the existing literature, a thorough investigation for SWIPT systems empowered by both IRSs and MAs is still absent. Besides, although it has been demonstrated that SWIPT systems can dramatically benefit from IRSs or MAs [8]-[12], [20], [21], it still remains unknow… view at source ↗
Figure 2
Figure 2. Convergence behavior of Alg. 2. 0 10 20 30 40 50 Number of BCD iterations -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 Objective value of (P6) PB = 25 dBm PB = 28 dBm PB = 30 dBm [PITH_FULL_IMAGE:figures/full_fig_p011_2.png] view at source ↗
Figure 5
Figure 5. Impact of normalized BS array size on sum-rate. [PITH_FULL_IMAGE:figures/full_fig_p011_5.png] view at source ↗
Figures from the paper (2 more)
Figure 6
Figure 6. Figure 6: Sum-rate versus the number of BS antennas [PITH_FULL_IMAGE:figures/full_fig_p012_6.png]
Figure 7
Figure 7. Figure 7: Impact of distances between the IRS and IDRs on sum [PITH_FULL_IMAGE:figures/full_fig_p012_7.png]

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