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arxiv: 2604.11227 · v1 · submitted 2026-04-13 · 💻 cs.IT · eess.SP· math.IT

Prior-Guided Movable Antenna Control for Agile Multi-Path Sensing (extended version)

Jaehong Kim , Jihong Park , Changsheng You , Seung-Woo Ko This is my paper

Pith reviewed 2026-05-10 16:35 UTC · model grok-4.3

classification 💻 cs.IT eess.SPmath.IT
keywords movable antennamulti-path sensingangle of arrival estimationprior-guided controlFisher informationMAP estimation6G wireless sensing
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The pith

Movable antennas achieve multi-path sensing with one orientation control and two linear scans using weak prior angle information.

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

This paper develops a method for sensing multiple signal paths with a movable antenna that avoids time-consuming full scans by relying on approximate prior knowledge of arrival angles. The approach first adjusts the antenna plate's orientation once, chosen to maximize the information from all paths and keep them distinguishable based on statistical analysis. It then performs two specific straight movements across the plate to collect signal data, from which a statistical estimator incorporating the priors computes the angles. If this works, it would allow faster and more practical multi-path sensing for advanced wireless networks without sacrificing much accuracy.

Core claim

With weak prior AoA statistics as side information, the framework optimizes the movable plate's three-dimensional orientation only once to maximize path visibility while preserving path discriminability via Fisher information analysis, then performs only two predetermined linear MA scans to estimate the elevation and azimuth AoAs using a maximum a posteriori algorithm that incorporates the priors, achieving AoA estimation accuracy approaching the single-path benchmark with significantly reduced control overhead and latency.

What carries the argument

Fisher-information-based optimization of a single three-dimensional orientation combined with a maximum a posteriori estimator applied to signals from two linear scans

Load-bearing premise

Weak prior statistics on the angles of arrival must be available and sufficiently accurate to guide the orientation optimization and the estimation process without introducing large bias or reducing the ability to distinguish paths.

What would settle it

An experiment measuring AoA estimation errors using the proposed one-orientation two-scan procedure versus exhaustive scanning, particularly when the prior statistics are perturbed by 10-20 degrees, to check if accuracy still approaches the single-path benchmark.

Figures

Figures reproduced from arXiv: 2604.11227 by Changsheng You, Jaehong Kim, Jihong Park, Seung-Woo Ko.

Figure 1
Figure 1. Figure 1: The geometries of movable plate and MA. (a) Coordinate system [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Graphical example of the effect of order-reversal constraint. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Joint AoA RMSE versus SNR V. SIMULATION RESULTS In this section, we evaluate the proposed MA-based AoA sensing framework. The carrier frequency is set to 28 GHz, the bandwidth to 50 MHz, and the number of subcarriers to K = 64. The MA scans M = 32 positions along each predetermined linear axis, with d = λ/2. We set L = 4 with prior mean AoAs (µℓ, ξℓ) given by (115◦ , 55◦ ), (100◦ , 115◦ ), (50◦ , 40◦ ), an… view at source ↗
read the original abstract

Multi-path sensing, which aims to extract the geometric attributes of multiple propagation paths, is expected to be a key functionality of 6G. A movable antenna (MA) can enable this functionality by creating a synthetic aperture through sequential mechanical motion. However, existing MA-based sensing methods typically rely on exhaustive scanning over the entire movable plate, resulting in significant control overhead and sensing latency, which limits their practicality for agile sensing. To address this challenge, this paper develops a prior-guided agile multi-path sensing framework that leverages weak prior angle-of-arrival (AoA) statistics as side information. The proposed framework comprises two steps. First, the movable plate's three-dimensional orientation is optimized only once to maximize path visibility while preserving path discriminability, both induced from Fisher information analysis. Second, only two predetermined linear MA scans are made on the tilted plate to estimate the elevation and azimuth AoAs from the resulting sequence of received signals. By incorporating the prior AoA statistics, a maximum a posteriori (MAP)-based AoA estimation algorithm is developed. With only one orientation control and two linear scans, the proposed framework enables agile multi-path sensing with significantly reduced control overhead and latency, while achieving AoA estimation accuracy approaching that of the single-path benchmark.

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 / 0 minor

Summary. The manuscript proposes a prior-guided framework for agile multi-path sensing with movable antennas (MA). It performs a single 3D orientation optimization of the movable plate, derived from Fisher information analysis to maximize path visibility while preserving discriminability using weak prior AoA statistics. This is followed by two predetermined linear MA scans on the tilted plate and a MAP-based estimator that incorporates the priors to recover elevation and azimuth AoAs. The central claim is that this reduces control overhead and latency substantially while achieving AoA accuracy approaching the single-path benchmark.

Significance. If the derivations and empirical results hold, the work offers a practical advance for 6G multi-path sensing by limiting mechanical control to one orientation adjustment plus two scans. The combination of information-theoretic orientation design with Bayesian estimation under weak priors is a coherent extension of standard array signal processing tools and could lower latency in synthetic-aperture applications.

major comments (2)
  1. Abstract and framework overview: the claim that AoA estimation accuracy approaches the single-path benchmark rests on the Fisher-information orientation step and the subsequent MAP estimator, yet the provided text contains no explicit FIM expressions, optimization formulation, bias/variance analysis, or simulation results that would allow verification of this performance claim.
  2. The weakest assumption—that weak prior AoA statistics are sufficiently accurate to guide both the orientation optimization and MAP estimation without substantial bias or loss of path discriminability—is load-bearing for the entire two-step procedure but receives no robustness analysis or sensitivity study in the manuscript.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments. We address each major comment point by point below, indicating the revisions we will incorporate to strengthen the manuscript.

read point-by-point responses

  1. Referee: Abstract and framework overview: the claim that AoA estimation accuracy approaches the single-path benchmark rests on the Fisher-information orientation step and the subsequent MAP estimator, yet the provided text contains no explicit FIM expressions, optimization formulation, bias/variance analysis, or simulation results that would allow verification of this performance claim.

    Authors: We agree that the abstract and high-level framework overview do not contain the explicit FIM expressions, full optimization formulation, or bias/variance analysis. The full manuscript derives the relevant Fisher information matrix in Section III for the 3D orientation optimization and presents the MAP estimator in Section IV, along with simulation results comparing multi-path performance to the single-path benchmark. To address the concern, we will revise the abstract to briefly reference the FIM-based design and optimization objective, add a concise summary of the bias/variance properties, and ensure the simulation results are highlighted with additional verification plots in the revised version. revision: yes

  2. Referee: The weakest assumption—that weak prior AoA statistics are sufficiently accurate to guide both the orientation optimization and MAP estimation without substantial bias or loss of path discriminability—is load-bearing for the entire two-step procedure but receives no robustness analysis or sensitivity study in the manuscript.

    Authors: We acknowledge that the manuscript lacks a dedicated robustness or sensitivity analysis for the weak prior AoA statistics. This assumption is indeed central, and the absence of such a study limits verification of performance under prior mismatch. In the revision, we will add a new subsection (or appendix) with sensitivity simulations that introduce controlled errors into the prior statistics and evaluate the resulting degradation in orientation optimization and AoA estimation accuracy, thereby quantifying the framework's robustness. revision: yes

Circularity Check

0 steps flagged

No significant circularity in the derivation chain

full rationale

The paper's two-step framework derives orientation optimization from Fisher information analysis on path visibility and discriminability, followed by two linear scans and MAP estimation that directly incorporates the stated external weak prior AoA statistics. These steps rely on standard information-theoretic tools and array-signal assumptions without any reduction of the target AoA accuracy claim to a fitted parameter, self-definition, or load-bearing self-citation chain. The performance comparison to the single-path benchmark is presented as an external reference rather than an internally constructed equivalence. The derivation remains self-contained against the provided inputs and standard methods.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Based on abstract only; the central claim rests on the domain assumption that usable weak AoA priors exist and on standard Fisher-information and MAP machinery.

axioms (1)
  • domain assumption Weak prior AoA statistics are available as side information
    Invoked to guide orientation optimization and MAP estimation.

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Reference graph

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