arxiv: 2602.09419 · v2 · submitted 2026-02-10 · 📡 eess.SP

Channel Uncertainty-Aware Robust Beamforming for RIS-Assisted RSMA Communication With Movable Antennas

Muhammad Asif , Asim Ihsan , Zhongliang Wang , Manzoor Ahmed , Xingwang Li , Arumugam Nallanathan , Symeon Chatzinotas This is my paper

Pith reviewed 2026-05-16 03:45 UTC · model grok-4.3

classification 📡 eess.SP

keywords robust beamformingmovable antennasreconfigurable intelligent surfacesrate-splitting multiple accessimperfect CSIresource allocationsum-rate maximization

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The pith

Robust joint design keeps RSMA sum rates high with movable antennas

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

The paper develops a framework for optimizing a multi-user downlink system that combines movable antennas, reconfigurable intelligent surfaces, and rate-splitting multiple access. It accounts for imperfect channel state information by using a bounded uncertainty model and solves the resulting non-convex problem through successive convex approximations in an iterative manner. This approach allows joint design of precoding vectors, RIS phase shifts, common rate allocation, and antenna positions while respecting power, QoS, and mutual coupling constraints. A sympathetic reader would care because movable antennas add spatial degrees of freedom that can improve performance, but they also couple the optimization tightly with channel errors, making robustness essential for practical deployment.

Core claim

The central discovery is that an iterative robust optimization framework, which decomposes the sum-rate maximization into subproblems for active beamforming, RIS reflection matrix, and movable antenna positions, and employs tractable convex surrogate functions, can guarantee reliable performance under worst-case channel conditions modeled by bounded CSI uncertainty.

What carries the argument

The iterative robust optimization framework that successively decomposes the joint optimization problem into active beamforming, RIS reflection, and MA position subproblems using convex surrogates under a bounded CSI uncertainty model.

If this is right

The proposed framework exhibits fast and stable convergence behavior.
It achieves significant performance gains compared to benchmark schemes.
Enhanced robustness is ensured under practical imperfect CSI conditions.
System sum-rate is maximized subject to QoS, power, and mutual coupling constraints.

Where Pith is reading between the lines

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

Extending this to dynamic environments where antennas move continuously could further improve adaptability.
Comparing the bounded uncertainty model against probabilistic alternatives might reveal trade-offs in conservatism versus average performance.
Integrating learning-based methods for estimating the uncertainty bounds could reduce reliance on worst-case assumptions.

Load-bearing premise

The bounded CSI uncertainty model accurately captures real-world channel estimation errors and that convex surrogate approximations can be built to solve the original non-convex problem iteratively.

What would settle it

A simulation or measurement showing that the achieved sum-rate under the proposed method falls below that of a non-robust benchmark when actual channel errors exceed the assumed bound.

Figures

Figures reproduced from arXiv: 2602.09419 by Arumugam Nallanathan, Asim Ihsan, Manzoor Ahmed, Muhammad Asif, Symeon Chatzinotas, Xingwang Li, Zhongliang Wang.

**Figure 1.** Figure 1: System model. users under channel estimation errors. In the presence of environmental obstructions, the direct BS–user communication links are blocked, such that user transmission is realized exclusively through the RIS equipped with N reflecting elements. A. Channel Model In this work, we adopt a planar far-field response channel model [12], where the spatial extent of the MA transmit region is sufficien… view at source ↗

**Figure 2.** Figure 2: Simulation environment. 1 2 3 4 5 6 7 8 9 10 5 10 15 20 25 30 35 40 [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗

**Figure 3.** Figure 3: System convergence for different number of RIS elements. [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗

**Figure 6.** Figure 6: Sum-rate performance across increasing values of [PITH_FULL_IMAGE:figures/full_fig_p010_6.png] view at source ↗

**Figure 7.** Figure 7: Sum-rate performance for different number of MAs [PITH_FULL_IMAGE:figures/full_fig_p010_7.png] view at source ↗

**Figure 10.** Figure 10: Joint effect of Lp and L on system sum-rate performance. than to the number of movable antennas. V. CONCLUSION This paper presented a robust joint optimization framework for a MAs-supported RIS-enabled multi-user RSMA downlink system under channel estimation errors. By jointly exploiting the spatial adaptability offered by MAs, the propagation reconfigurability of RIS, and the interference management ca… view at source ↗

read the original abstract

This work investigates a robust resource allocation framework for a downlink multi-user communication system integrating movable antennas (MAs) and reconfigurable intelligent surfaces (RISs) under the rate-splitting multiple access (RSMA) transmission protocol. Unlike conventional fixed-position antenna architectures, the considered MAs-enabled system introduces spatially adaptive channel variations in which antenna positions directly influence the effective channel responses. Consequently, under imperfect channel state information (CSI), the impact of CSI uncertainty propagates not only through active and passive beamforming design, but also through the antenna position optimization process, leading to a highly coupled robust optimization problem. To address this challenge, we formulate a system sum-rate maximization problem by jointly optimizing the transmit precoding vectors, RIS reflection matrix, common-rate allocation, and MAs positions, subject to quality-of-service (QoS), power-budget, common-rate decoding, and mutual coupling constraints. The resulting non-convex problem is efficiently handled through an iterative robust optimization framework, where the original problem is successively decomposed into active beamforming, RIS reflection matrix, and MAs position optimization subproblems, and tractable convex surrogate functions are constructed to enable iterative optimization. Moreover, system robustness is ensured by incorporating a bounded CSI uncertainty model that explicitly captures channel estimation errors and guarantees reliable communication performance under worst-case channel conditions. Finally, extensive simulation results demonstrate that the proposed framework achieves significant performance gains and enhanced robustness compared with benchmark schemes, while also exhibiting fast and stable convergence behavior under practical imperfect CSI conditions.

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.

Desk Editor's Note private letter to a colleague

The paper adds movable antennas to a RIS-RSMA robust design by jointly optimizing positions with precoding and reflection under bounded CSI error, but the SCA surrogates lack a clear argument that they preserve worst-case guarantees after nonlinear position updates.

read the letter

This paper brings movable antennas into a RIS-assisted RSMA downlink under imperfect CSI. The core contribution is a joint optimization that treats antenna positions as decision variables alongside the precoders, RIS phases, and common-rate split, all under a bounded uncertainty model on the channels. They decompose the sum-rate problem into three subproblems and replace each with a convex surrogate solved iteratively. The setup includes realistic constraints like mutual coupling and QoS, which is a step beyond fixed-antenna treatments. Simulations are said to show rate gains and faster convergence than fixed-position or non-robust baselines, which is plausible given the extra spatial degrees of freedom from the MAs. The bounded-error model is standard and the decomposition follows common successive convex approximation practice, so the algorithmic structure is straightforward to follow. The soft spot is the robustness claim. Antenna positions enter the effective channel through nonlinear phase terms, so moving an antenna changes which channels are uncertain and how the uncertainty set maps to the composite link. The paper applies the same bounded model to each subproblem but does not supply an explicit bound or monotonicity argument showing that the surrogate worst-case rate remains an upper bound on the true worst-case rate once the position is updated. Without that step, the fixed point of the iteration may not solve the original robust problem. This is a concrete but scoped contribution aimed at researchers working on RIS, RSMA, or reconfigurable arrays who want to see how position optimization interacts with robust allocation. It is worth sending to peer review so the convergence and robustness details can be examined; the integration itself is timely even if the analysis needs tightening.

Referee Report

1 major / 2 minor

Summary. The paper proposes a robust sum-rate maximization framework for a downlink multi-user RIS-assisted RSMA system with movable antennas under bounded CSI uncertainty. It jointly optimizes transmit precoding, RIS reflection matrix, common-rate allocation, and MA positions subject to QoS, power, and mutual-coupling constraints, and solves the resulting non-convex problem via an iterative decomposition into active-beamforming, RIS, and MA-position subproblems, each replaced by a tractable convex surrogate.

Significance. If the surrogates preserve worst-case robustness despite the nonlinear mapping of CSI uncertainty through MA positions, the framework would provide a concrete, implementable method for joint hardware reconfiguration and robust beamforming in RSMA systems. The reported simulation gains over benchmarks and stable convergence under imperfect CSI would be practically relevant for next-generation systems that exploit spatial adaptability.

major comments (1)

[Iterative Optimization Framework] The description of the iterative framework (abstract and §III) constructs convex surrogates for the MA-position subproblem without supplying an explicit error bound or monotonicity argument showing that the worst-case rate evaluated under the surrogate remains an upper bound on the true worst-case rate after each position update. Because MA coordinates enter the effective channel through nonlinear array-response phase terms, the original bounded uncertainty set on the channels maps to a position-dependent, non-convex set on the composite channel; the paper does not demonstrate that the surrogate construction respects this mapping.

minor comments (2)

[Simulation Results] The convergence analysis is stated only qualitatively (fast and stable behavior); quantitative iteration complexity or a proof that the sequence of surrogate objectives is monotonically non-decreasing would strengthen the claims.
[System Model] Notation for the bounded uncertainty radius and the mapping from MA coordinates to the effective channel matrix should be introduced earlier and used consistently when defining the worst-case rate expressions.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the detailed and constructive review. The major comment highlights an important aspect of the robustness analysis in our iterative framework. We address it below and will revise the manuscript to strengthen the presentation.

read point-by-point responses

Referee: The description of the iterative framework (abstract and §III) constructs convex surrogates for the MA-position subproblem without supplying an explicit error bound or monotonicity argument showing that the worst-case rate evaluated under the surrogate remains an upper bound on the true worst-case rate after each position update. Because MA coordinates enter the effective channel through nonlinear array-response phase terms, the original bounded uncertainty set on the channels maps to a position-dependent, non-convex set on the composite channel; the paper does not demonstrate that the surrogate construction respects this mapping.

Authors: We agree that an explicit argument linking the surrogate to the true worst-case rate under the nonlinear MA-induced mapping would strengthen the paper. In the revised manuscript we will add a new subsection in §III that derives a conservative first-order Taylor approximation of the array response around the current MA positions. We show that the resulting convex surrogate yields a lower bound on the achievable worst-case rate for any position update within the feasible set, thereby preserving monotonicity of the robust objective. The proof relies on the fact that the bounded CSI uncertainty set is mapped through a Lipschitz-continuous phase function whose gradient is bounded by the maximum array aperture; this allows us to construct a position-independent uncertainty ball that over-approximates the composite channel error. We will also include a short numerical verification confirming that the surrogate rate remains below the true worst-case rate evaluated by exhaustive search over the uncertainty set. revision: yes

Circularity Check

0 steps flagged

No circularity: standard SCA applied to externally modeled robust problem

full rationale

The paper formulates a joint non-convex sum-rate maximization under bounded CSI uncertainty and decomposes it into subproblems solved via successive convex surrogates. The bounded uncertainty model is an external assumption, not derived from the paper's outputs. No equation reduces to a fitted parameter renamed as prediction, no self-citation chain carries the central claim, and the MA-position subproblem uses standard array-response functions without self-referential closure. The framework is self-contained against external benchmarks and does not exhibit any of the enumerated circular patterns.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 0 invented entities

The central claim rests on the standard bounded CSI uncertainty model and the effectiveness of successive convex approximation for the non-convex problem; no new entities are introduced.

free parameters (1)

CSI uncertainty bound radius
The bounded model requires a radius parameter that defines the worst-case error set and is typically chosen from estimation quality.

axioms (2)

domain assumption Channel estimation errors lie inside a known bounded set and the worst-case realization must be protected against.
Invoked when formulating the robust optimization problem under imperfect CSI.
domain assumption Convex surrogate functions can be constructed that preserve feasibility and yield monotonic improvement in the original objective.
Required for the iterative decomposition to be tractable.

pith-pipeline@v0.9.0 · 5596 in / 1395 out tokens · 71283 ms · 2026-05-16T03:45:19.838232+00:00 · methodology

discussion (0)

Reference graph

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