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arxiv: 2510.19759 · v3 · pith:4HW4GTH4new · submitted 2025-10-22 · 💻 cs.IT · math.IT

Weighted Sum Rate Optimization for Movable Antenna Enabled Near-Field ISAC

Nemanja Stefan Perovi\'c , Keshav Singh , Chih-Peng Li , Mark F. Flanagan This is my paper

Pith reviewed 2026-05-21 20:52 UTC · model grok-4.3

classification 💻 cs.IT math.IT
keywords movable antennasnear-field ISACweighted sum ratealternating optimizationintegrated sensing and communicationspherical wave channel
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The pith

Movable antennas in near-field ISAC systems raise the achievable weighted sum rate for communication users while meeting a sensing requirement.

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

The paper establishes that movable antennas can be positioned to improve overall system performance in near-field integrated sensing and communication setups. It develops an alternating optimization procedure that jointly tunes antenna locations with precoders, beamformers, and combiners to maximize the weighted sum rate subject to a minimum sensing SINR threshold. If the approach holds, networks could deliver higher communication throughput without sacrificing sensing capability when users operate close to the base station. Simulations indicate clear gains relative to fixed-antenna baselines under the same near-field conditions.

Core claim

The central claim is that an alternating optimization algorithm, which iteratively refines the sensing receive combiner, communication precoding matrices, sensing transmit beamformer, and the physical positions of the movable antennas, maximizes the weighted sum rate for communication users while enforcing a minimum sensing signal-to-interference-plus-noise ratio in a near-field ISAC system. This joint optimization exploits the spherical-wave channel structure to yield higher rates than configurations limited to fixed antenna positions.

What carries the argument

Alternating optimization that cycles through updates to the sensing receive combiner, communication precoding matrices, sensing transmit beamformer, and movable-antenna positions.

If this is right

  • Allocating larger weights to communication users closer to the base station produces the highest weighted sum rate.
  • Raising the minimum sensing SINR threshold degrades sensing performance more sharply than communication performance.
  • Movable antennas deliver substantial weighted-sum-rate gains over fixed-antenna near-field ISAC systems.
  • The optimization framework remains feasible when the number of movable antennas is modest.

Where Pith is reading between the lines

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

  • Real-time repositioning of antennas could adapt to user mobility without requiring additional fixed hardware.
  • The same alternating procedure might extend to multi-base-station deployments where sensing targets move across cells.
  • Reducing the density of fixed antennas while retaining movable ones could lower deployment cost in dense near-field scenarios.

Load-bearing premise

The near-field channel is accurately captured by the spherical-wave model and the alternating updates converge to a high-quality operating point rather than a poor local solution.

What would settle it

A measurement campaign or refined simulation in which repositioning the antennas produces no measurable weighted-sum-rate improvement over fixed locations, under identical power and sensing-SINR constraints, would falsify the reported advantage.

Figures

Figures reproduced from arXiv: 2510.19759 by Chih-Peng Li, Keshav Singh, Mark F. Flanagan, Nemanja Stefan Perovi\'c.

Figure 1
Figure 1. Figure 1: Convergence of the proposed algorithm. 1 1.5 2 2.5 3 3.5 4 5 6 7 8 9 Number of user antennas, Nk WSR (bit/s/Hz) Proposed Fix-User-Ant [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: WSR versus the number of user antennas. rate. In [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 5
Figure 5. Figure 5: WSR and sensing signal power versus the sensing SINR [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 4
Figure 4. Figure 4: WSR versus the maximum transmit power. a user located closer to the BS generally has a larger number of DoF, which also contributes to the increased achievable rate of that user. On the other hand, the lowest WSR occurs when w1 lies around 0.5, when the weighted rate contributions of both users to the WSR are approximately equal. Next, we study how the WSR varies with the maximum transmit power (i.e., Pmax… view at source ↗
read the original abstract

Integrated sensing and communication (ISAC) has been recognized as one of the key technologies capable of simultaneously improving communication and sensing services in future wireless networks. Moreover, the introduction of recently developed movable antennas (MAs) has the potential to further increase the performance gains of ISAC systems. Achieving these gains can pose a significant challenge for MA-enabled ISAC systems operating in the near-field due to the corresponding spherical wave propagation. Motivated by this, in this paper we maximize the weighted sum rate (WSR) for communication users while maintaining a minimal sensing requirement in an MA-enabled near-field ISAC system. To achieve this goal, we propose an algorithm that optimizes the sensing receive combiner, the communication precoding matrices, the sensing transmit beamformer and the positions of the users' MAs in an alternating manner. Simulation results show that using MAs in near-field ISAC systems provides a substantial performance advantage compared to near-field ISAC systems with only fixed antennas. Additionally, we demonstrate that the highest WSR is obtained when larger weights are allocated to the users placed closer to the BS, and that the sensing performance is significantly more affected by the minimum sensing signal-to-interference-plus-noise ratio (SINR) threshold compared to the communication performance.

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 paper claims to maximize the weighted sum rate (WSR) for communication users in a movable antenna (MA) enabled near-field ISAC system while satisfying a minimum sensing SINR requirement. An alternating optimization algorithm is proposed to jointly optimize the sensing receive combiner, communication precoders, sensing transmit beamformer, and MA positions. Simulations demonstrate substantial WSR gains over fixed-antenna systems, with insights on user weight allocation and sensitivity to the sensing threshold.

Significance. This work addresses an emerging topic at the intersection of movable antennas and near-field ISAC, which could offer practical performance improvements in future wireless systems. The simulation results, if robust, provide evidence for the advantages of MA position optimization. However, the reliance on heuristic optimization without convergence assurances limits the significance until these aspects are addressed.

major comments (2)
  1. [Section IV] The alternating optimization procedure lacks any convergence analysis or guarantee of reaching a high-quality stationary point. Since the MA position optimization subproblem is non-convex (MA positions enter the spherical-wave channel model nonlinearly), the substantial performance gains reported may correspond to local optima rather than a reliable advantage over fixed antennas.
  2. [Section V] Details on the optimization initialization, number of trials to avoid poor local optima, and exact parameter settings for the channel model and convergence criteria are only summarized at a high level. This weakens the support for the central simulation-based claim of substantial advantage.
minor comments (1)
  1. The paper would benefit from a table summarizing all simulation parameters (e.g., array sizes, distances, noise powers) for easier reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on our manuscript. We address each major comment below and describe the changes planned for the revised version.

read point-by-point responses

  1. Referee: [Section IV] The alternating optimization procedure lacks any convergence analysis or guarantee of reaching a high-quality stationary point. Since the MA position optimization subproblem is non-convex (MA positions enter the spherical-wave channel model nonlinearly), the substantial performance gains reported may correspond to local optima rather than a reliable advantage over fixed antennas.

    Authors: We agree that the alternating optimization lacks a theoretical convergence guarantee to a stationary point because the MA-position subproblem is non-convex. This limitation is inherent to the problem structure. Nevertheless, the algorithm exhibits reliable empirical convergence in all simulated scenarios, and the reported performance advantage over fixed-antenna baselines remains consistent across multiple random initializations. In the revision we will add a dedicated paragraph in Section IV discussing the observed convergence behavior, iteration statistics, and the effect of different initializations, thereby clarifying that the gains are not isolated local-optima artifacts. revision: partial

  2. Referee: [Section V] Details on the optimization initialization, number of trials to avoid poor local optima, and exact parameter settings for the channel model and convergence criteria are only summarized at a high level. This weakens the support for the central simulation-based claim of substantial advantage.

    Authors: We appreciate the referee highlighting the need for greater reproducibility. In the revised manuscript we will expand Section V with explicit descriptions of the initialization procedure for all optimization variables, the number of independent random trials performed, the precise numerical values used for the near-field channel parameters, and the exact convergence thresholds applied to each subproblem. revision: yes

Circularity Check

0 steps flagged

No circularity: standard alternating optimization on independently defined objective

full rationale

The paper defines the WSR objective and spherical-wave channel model from first principles, then applies a standard block-alternating procedure over precoders, combiners, beamformer and MA positions. None of the subproblems or the overall result is shown to reduce by the paper's own equations to a quantity defined solely by fitted parameters or by a self-citation chain. The simulation claims rest on numerical evaluation rather than any tautological renaming or self-referential prediction. This is the normal non-circular outcome for an optimization paper whose central contribution is algorithmic rather than a closed-form derivation that collapses to its inputs.

Axiom & Free-Parameter Ledger

2 free parameters · 1 axioms · 0 invented entities

The work rests on standard domain assumptions about near-field propagation and the practical convergence of alternating optimization for non-convex beamforming problems.

free parameters (2)
  • user weights for WSR
    Weights assigned to each communication user; simulations note highest WSR when larger weights go to closer users.
  • minimum sensing SINR threshold
    Design parameter whose value significantly affects reported sensing performance.
axioms (1)
  • domain assumption Spherical wave propagation model accurately describes near-field channels
    Invoked to justify the near-field ISAC setting and optimization difficulty.

pith-pipeline@v0.9.0 · 5764 in / 1248 out tokens · 58101 ms · 2026-05-21T20:52:39.128157+00:00 · methodology

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Reference graph

Works this paper leans on

12 extracted references · 12 canonical work pages

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