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arxiv: 2607.02384 · v1 · pith:NBER4IUOnew · submitted 2026-07-02 · 📡 eess.SP

Robust Transmission Design for RIS-Assisted RSMA-SWIPT Systems With Movable Antennas Under Hardware Distortions

Muhammad Asif , Asim Ihsan , Irfan Muhammad , Mohd Hamza Naim Shaikh , Syed Tariq Shah , Zhu Shoujin , Symeon Chatzinotas This is my paper

Pith reviewed 2026-07-03 07:24 UTC · model grok-4.3

classification 📡 eess.SP
keywords robust resource allocationreconfigurable intelligent surfacerate-splitting multiple accesssimultaneous wireless information and power transfermovable antennashardware impairmentsCSI uncertaintysum-rate maximization
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The pith

A joint optimization framework maximizes sum-rate in RSMA-SWIPT systems assisted by RIS and movable antennas under CSI uncertainty and hardware impairments.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper develops a transmission design for multi-user systems that combine rate-splitting multiple access with simultaneous wireless information and power transfer, using reconfigurable intelligent surfaces and movable antennas. It accounts for imperfect channel knowledge and hardware distortions that affect signal quality. The approach creates a framework to optimize several variables together: common rate allocation, beamforming, surface reflections, power splitting for energy harvesting, and antenna locations. By breaking the hard optimization into smaller convex problems, it finds solutions that improve overall data rates while staying robust to uncertainties.

Core claim

The central discovery is that a robust resource allocation framework, which jointly optimizes common-rate allocation, transmit beamforming, RIS reflection coefficients, power-splitting ratios, and movable antenna positions, can maximize the achievable sum-rate in RSMA-SWIPT systems subject to CSI uncertainty and residual hardware impairments, by decomposing the problem into subproblems and constructing tractable convex surrogate functions for each.

What carries the argument

The robust resource allocation framework that decomposes the coupled non-convex problem into active beamforming, RIS reflection design, power-splitting ratio optimization, and movable antenna position optimization subproblems, using convex surrogate functions to handle non-convexity while preserving robustness.

If this is right

  • The achievable sum-rate increases substantially compared with benchmark schemes.
  • The design maintains performance under CSI uncertainty and hardware impairments.
  • The optimization algorithm shows improved convergence performance.
  • Practical constraints on power, rates, and hardware are satisfied in the solutions.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Similar decomposition methods might apply to systems with multiple movable surfaces or larger user counts.
  • Real-world prototypes could test how often antenna repositioning is needed in changing environments.
  • The framework could be extended to include energy efficiency as a second objective alongside sum-rate.

Load-bearing premise

The non-convex joint optimization can be decomposed into subproblems for which convex surrogate functions can be built that still ensure robustness to uncertainties and near-optimal rates.

What would settle it

A simulation where the proposed surrogates produce sum-rates lower than a simple fixed-position benchmark under high CSI error variance would disprove the effectiveness claim.

Figures

Figures reproduced from arXiv: 2607.02384 by Asim Ihsan, Irfan Muhammad, Mohd Hamza Naim Shaikh, Muhammad Asif, Syed Tariq Shah, Symeon Chatzinotas, Zhu Shoujin.

Figure 1
Figure 1. Figure 1: Illustration of system model. II. SYSTEM MODEL AND PROBLEM FORMULATION We consider a downlink RSMA-SWIPT framework incor￾porating RIS under CSI uncertainty and transceiver HIs, as depicted in [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Simulation setup. with identical numbers of transmit-side and receive-side propa￾gation paths, i.e., Kt = Kr. The angular parameters associated with the propagation paths, namely θ e i , ϑ e j , θ a i , and ϑ a j , are independently generated according to the uniform distribution on [0, π], where 1 ≤ i ≤ Kt and 1 ≤ j ≤ Kr. Unless otherwise stated, all numerical results are obtained by aver￾aging over 104 i… view at source ↗
Figure 4
Figure 4. Figure 4: System convergence under different values of [PITH_FULL_IMAGE:figures/full_fig_p010_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Performance under increasing transceiver hardware impair [PITH_FULL_IMAGE:figures/full_fig_p010_5.png] view at source ↗
Figure 8
Figure 8. Figure 8: Achievable sum-rate versus RIS size Q for different values of ϱt and ϱr. 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1 9 10 11 12 13 14 15 16 17 [PITH_FULL_IMAGE:figures/full_fig_p011_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: System performance versus channel uncertainty level [PITH_FULL_IMAGE:figures/full_fig_p011_9.png] view at source ↗
Figure 12
Figure 12. Figure 12: Comparative performance evaluation under different numbers [PITH_FULL_IMAGE:figures/full_fig_p012_12.png] view at source ↗
Figure 11
Figure 11. Figure 11: Comparative performance analysis under different numbers [PITH_FULL_IMAGE:figures/full_fig_p012_11.png] view at source ↗
read the original abstract

This paper investigates a robust transmission design for a multi-user rate-splitting multiple access (RSMA)-based simultaneous wireless information and power transfer (SWIPT) system empowered by movable antennas (MAs) and a reconfigurable intelligent surface (RIS) under channel state information (CSI) uncertainty and residual hardware impairments (HIs). The effective channels in MAs-enabled systems depend on antenna positions, causing CSI uncertainty to affect not only active and passive beamforming but also antenna position optimization. Furthermore, residual HIs distort the effective SINRs, creating additional coupling among beamforming, RIS reflection control, common-rate allocation, power-splitting ratio optimization, and antenna position optimization. Consequently, the joint impact of CSI uncertainty and HIs leads to a highly coupled and challenging resource allocation problem. To address this challenge, we propose a robust resource allocation framework that jointly optimizes common-rate allocation, transmit beamforming, RIS reflection coefficients, power-splitting ratios, and MAs positions to maximize the achievable sum-rate while satisfying practical system constraints. To obtain an efficient solution, the original problem is decomposed into active beamforming, RIS reflection design, power-splitting ratio optimization, and MAs position optimization subproblems, where tractable convex surrogate functions are constructed to handle the non-convex objective and constraints. Simulation results verify the effectiveness of the proposed framework and demonstrate substantial improvements in achievable sum-rate, robustness against CSI uncertainty and hardware impairments, and convergence performance compared with benchmark schemes.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript investigates robust transmission design in a multi-user RSMA-SWIPT system assisted by RIS and movable antennas, subject to CSI uncertainty and residual hardware impairments. It proposes a joint optimization framework over common-rate allocation, transmit beamforming, RIS reflection coefficients, power-splitting ratios, and MA positions to maximize sum-rate under practical constraints. The non-convex problem is decomposed into four subproblems for which tractable convex surrogate functions are constructed; simulation results are presented to demonstrate gains in sum-rate, robustness, and convergence relative to benchmark schemes.

Significance. If the surrogate functions preserve worst-case robustness guarantees under the joint CSI uncertainty and hardware-impairment model while remaining sufficiently tight, the work would offer a practical resource-allocation approach for emerging RIS-MA systems. The explicit inclusion of movable-antenna position optimization together with RSMA and SWIPT under impairments addresses a timely coupling that is not fully treated in prior literature.

major comments (2)
  1. [Abstract (paragraph on the proposed framework)] The central algorithmic claim rests on the construction of convex surrogate functions for the four subproblems (active beamforming, RIS reflection, power-splitting ratios, MA positions). The abstract asserts that these surrogates are tractable and handle the non-convex objective and constraints, yet supplies neither the explicit functional forms nor any tightness or error-bound analysis. Without such details it is impossible to confirm that the worst-case robustness to CSI uncertainty and residual HIs is retained after approximation.
  2. [Abstract (paragraph on the proposed framework)] The robustness claims depend on the joint effect of CSI uncertainty and hardware impairments being correctly captured in the effective SINR expressions after decomposition. The manuscript does not indicate whether the surrogate objectives or constraints are derived from a worst-case formulation that remains equivalent (or conservatively bounded) under the combined impairment model.
minor comments (1)
  1. [Abstract (simulation-results sentence)] The abstract refers to 'substantial improvements' in simulations but provides no information on the number of Monte-Carlo trials, confidence intervals, or the specific ranges of CSI uncertainty and hardware-impairment parameters used; this limits reproducibility and assessment of statistical significance.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment below and agree to revise the abstract to provide additional clarity on the surrogate constructions while preserving the paper's technical contributions.

read point-by-point responses

  1. Referee: [Abstract (paragraph on the proposed framework)] The central algorithmic claim rests on the construction of convex surrogate functions for the four subproblems (active beamforming, RIS reflection, power-splitting ratios, MA positions). The abstract asserts that these surrogates are tractable and handle the non-convex objective and constraints, yet supplies neither the explicit functional forms nor any tightness or error-bound analysis. Without such details it is impossible to confirm that the worst-case robustness to CSI uncertainty and residual HIs is retained after approximation.

    Authors: The explicit functional forms are derived in Sections III-B through III-E using successive convex approximation (SCA) via first-order Taylor expansions of the non-convex terms in the worst-case SINR expressions. These surrogates are tight at each iteration point, with monotonic convergence guaranteed as shown in Appendix A. The worst-case robustness is retained because the approximations are constructed as conservative lower bounds on the original robust objective. We will revise the abstract to reference the SCA method and the preservation of robustness guarantees. revision: yes

  2. Referee: [Abstract (paragraph on the proposed framework)] The robustness claims depend on the joint effect of CSI uncertainty and hardware impairments being correctly captured in the effective SINR expressions after decomposition. The manuscript does not indicate whether the surrogate objectives or constraints are derived from a worst-case formulation that remains equivalent (or conservatively bounded) under the combined impairment model.

    Authors: Section II formulates the effective SINRs under the joint bounded CSI uncertainty and additive hardware distortion model, yielding a worst-case robust problem. The decomposition in Section III solves each subproblem while retaining the uncertainty sets, and the surrogates are derived as conservative bounds on the worst-case rates. We will update the abstract to explicitly note that the framework employs a worst-case formulation with conservatively bounded surrogates. revision: yes

Circularity Check

0 steps flagged

No significant circularity; standard decomposition and surrogate construction

full rationale

The paper's core contribution is a proposed decomposition of a joint non-convex optimization problem (common-rate allocation, beamforming, RIS phases, power splitting, MA positions) into four subproblems, followed by construction of tractable convex surrogates. The abstract and reader's summary contain no equations or claims that reduce any result to its inputs by definition, no fitted parameters renamed as predictions, and no load-bearing self-citations whose uniqueness theorems are invoked to force the outcome. Simulation results are presented as external verification of the framework rather than a tautological restatement of the objective. This matches the default expectation for an optimization paper whose derivation chain remains independent of its own fitted values or prior self-citations.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the existence of effective convex surrogates for a highly coupled non-convex problem; because only the abstract is available, the ledger cannot enumerate specific fitted parameters or invented entities.

axioms (1)
  • domain assumption Convex surrogate functions can be constructed that adequately approximate the original non-convex objective and constraints while preserving robustness properties.
    Invoked when the original problem is decomposed into tractable subproblems (abstract).

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discussion (0)

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