ROMA: ROtary and Movable Antenna

Bo Ai; Bokai Xu; Huahua Xiao; Jiayi Zhang; Wenhui Yi; Zhe Wang

REVIEW 4 major objections 5 minor 15 references

ROMA: ROtary and Movable Antenna

T0 review · 4 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read ROMA-aided multi-user MIMO, with panels that rotate in 3D while elements move within a region, beats fixed, movable-only, rotation-only, and selection baselines in average spectral efficiency via joint geometric optimization.

desk verdict A plausible new flexible-array idea with a serious typo in the rotation map; the simulation gains are believable but the paper needs a corrected Eq. (1) and a code release before I'd trust the numbers. read the letter →

arxiv 2501.13403 v2 pith:KQV4Q4OR submitted 2025-01-23 eess.SP cs.ITmath.IT

Jiayi Zhang , Wenhui Yi , Bokai Xu , Zhe Wang , Huahua Xiao , Bo Ai This is my paper · ORCID

classification eess.SPcs.ITmath.IT

keywords rotaryandmovableantennamulti-userMIMOspectralefficiencyalternatingoptimizationpositionarrayrotationmaximumratioprecoding

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

The paper is trying to establish that letting antenna panels both rotate in three dimensions and reposition their individual elements—a configuration it calls rotary and movable antenna (ROMA)—can substantially raise the average spectral efficiency of a downlink multi-user MIMO system without adding antennas or bandwidth. It develops a geometric model in which each panel's two rotation angles and each element's position enter the channel phases, derives an upper bound on spectral efficiency under maximum-ratio precoding, and proposes an alternating-optimization algorithm that alternately updates transmit-side positions, user-side positions, and rotation angles. Simulation results show the ROMA design outperforming fixed-position, movable-antenna, rotation-only, and antenna-selection baselines, with the gain growing as the movable region and transmit power increase. If correct, this makes antenna geometry itself a resource that can be optimized jointly with signal processing, with implications for future MIMO deployments.

What carries the argument

The central object is the ROMA panel, a uniform planar array whose plane can be rotated around the x-axis by angle $\alpha$ and tilted relative to the z-axis by angle $\beta$, while each element's position on the plane can be shifted within a region. The argument is carried by writing every channel entry as a sum of phase terms that depend on these rotation angles and positions, then invoking the linearly independent angle (LIA) condition and the arbitrarily large region (ALR) assumption to replace each entry by a single phase term $\|\mathbf{b}_{umn_u}\|_1 e^{j\pi v_u}$. That replacement yields the channel-gain quantity $G_u$ and the SE upper bounds in Theorem 1 and Corollary 1, which in turn define the objective for the optimization. The optimization itself is an alternating algorithm: it updates transmit-side and user-side element positions by variable splitting with a penalty term, enforces minimum-distance constraints through a geometric alternating-optimization step, and updates the two rotation angles by gradient descent using automatic differentiation.

What would settle it

Re-simulate Figs. 3 and 4 using the exact multipath channel of Eq. (3) instead of the approximation in Eq. (6), at the same settings ($A=2.5\lambda$, $L=15$); if the optimized ROMA configuration no longer beats the MA and RO baselines, or if the gap between the exact SE and the Theorem 1 upper bound is large, the central claim is falsified. A simpler check: at fixed transmit power, increase $L$ while keeping $A$ small—the approximation should degrade and the predicted SE gain should shrink.

Watch

Extended reading notes

Core claim

On its own terms, the paper's discovery is that joint control of two geometric degrees of freedom—panel rotation and element translation—yields spectral-efficiency gains that either degree alone cannot provide. For a system of ROMA panels at both base station and users, the paper shows that the average spectral efficiency under MR precoding is bounded by a closed-form expression involving the channel gain $G_u$ and inter-user interference, and that under line-of-sight propagation the bound takes an explicit trigonometric form depending on rotation angles and antenna spacings. It then establishes that maximizing the average SE over rotation angles and element positions can be carried out by an alternating algorithm that converges to a stationary point, and verifies by simulation that the resulting configuration outperforms fixed-position arrays, movable antennas without rotation, rotatable arrays without moving elements, and antenna selection.

Load-bearing premise

The load-bearing premise is that the multipath arrivals have sufficiently independent angles and the antenna movable region is large enough that every channel entry can be replaced by one phase term; the paper's own simulations use a small region ($A=2.5\lambda$) with 15 paths, a setting where that premise is not strictly guaranteed, and if it fails the SE upper bound and the optimization's theoretical justification do not hold.

Editorial extensions

If this is right

For a fixed number of antennas, ROMA can be treated as a software-reconfigurable geometry layer: the same hardware can be steered toward user clusters as their spatial distribution changes, improving average SE without additional spectrum or power.
The closed-form SE bound makes the geometric design problem differentiable, so gradient-based tools can be used for real-time reconfiguration once CSI is available.
Gains grow with the movable-region size and with transmit power, so ROMA is most valuable in high-power, spatially constrained deployments where fixed-position, rotation-only, and antenna-selection designs saturate.
Because the boundedness argument extends to other precoding schemes, the same alternating-optimization framework can be applied beyond MR precoding, for instance with the zero-forcing precoding already used in the algorithm's precoding subproblem.

Reading between the lines

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

The mechanism behind the gain is likely angular separation: rotating a panel changes the effective direction of the user's signal in the array manifold, which for users with overlapping arrival angles can reduce inter-user interference more than moving elements alone; this suggests ROMA's advantage will be largest in crowded angular scenarios and near-zero when users are already well separated.
The small-regime inconsistency between the ALR assumption and the simulated $A=2.5\lambda$ region implies the reported gains may be partly an artifact of the approximation; a natural test is to run the same AO algorithm on the exact channel and compare, which the paper does not do.
The framework suggests a design spectrum: at one end pure movable antennas (translation only), at the other pure rotation; ROMA's joint optimization indicates there is an efficient frontier between translation and rotation that a hardware designer could trade off against motor cost.
In near-field or strong-scattering settings where the LIA/ALR conditions fail, a robust extension would replace the single-phase-term approximation with the full sum-of-phases channel and optimize the same geometry by sampling or surrogate models.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The idea is simple and worth a look: combine panel rotation with movable elements, and jointly optimize both for multi-user MIMO. That is genuinely a new twist relative to MA, 6DMA, and rotate-only baselines. The paper shows consistent SE gains over FPA, MA, RO, and AS across region size and transmit power. I believe the gains are probably real, but there is one landmine and one approximation issue.

First, Eq. (1) is not a rigid rotation as printed. At α=β=0 it maps (X,0,Z) to (X,X,Z), and it does not preserve distances. The rest of the paper—specifically Corollary 1 and Appendix A—implicitly uses the correct formula with y = X sin α + Z sin β cos α. So the printed coordinate transform is a typo, likely a transcription error. But without a code release, I cannot verify which map drove the simulation curves. If the printed map was actually used, the ROMA gains could be an artifact of array stretching. That is the first thing a referee must ask for.

Second, the SE upper bound in Theorem 1 is built on the LIA/ALR approximation from [11], with a generic δ and no error bound. For A=2.5λ and L=15, ALR is not satisfied, so the 'upper bound' may not be an upper bound. That said, the optimization objective in P1 is the exact SE (5), and the simulations report exact SE, so the central numerical claim does not collapse if the bound is loose. Still, the paper's theoretical framing overreaches.

Minor: the AO convergence claim relies on monotonicity and boundedness, which is plausible but not proven; simulation details are thin (number of channel drops, user distribution, computational cost). These are standard letter-level omissions.

The paper is for people working on movable/fluid antennas and 6G flexible arrays. It provides a useful positional contribution and a simulation data point. It deserves a serious referee, primarily to check the rotation-map issue and request code. I would send it out rather than desk reject.

Referee Report

4 major / 5 minor

Summary. This letter introduces ROMA, an architecture in which uniform planar arrays at both the BS and the users can be rotated about two axes while the individual antenna elements are repositioned within a bounded region. The authors model the downlink channel as a sum of L multipath components, approximate each channel entry by a single absolute-sum phase term using the LIA/ALR approximation from [11], and derive an upper bound on the average SE under MR precoding (Theorem 1 and Corollary 1). They then formulate a joint optimization problem P1 over the precoder, element positions, and panel rotation angles, and propose an alternating optimization (AO) algorithm based on penalty decomposition and gradient descent. Section V reports simulations showing that ROMA outperforms FPA, MA, RO, and AS baselines in average SE.

Significance. If the geometric model and the reported gains are correct, ROMA is a plausible extension of movable-antenna and rotary-antenna ideas, and the comparison against four baselines is a useful first benchmark. The manuscript does not provide code, does not give an error bound for the core LIA/ALR approximation, and does not formally prove convergence of the AO algorithm, so the numerical results are the main evidence for the central claim. The contribution is therefore significant but conditional: the idea is worth publishing if the geometry is corrected and the simulations are made reproducible, but the current manuscript cannot be validated as written.

major comments (4)

[Section II, Eq. (1)] Equation (1) does not describe a rigid rotation of the panel. Setting alpha=beta=0 maps (X,0,Z) to (X,X,Z), and the Euclidean norm is not preserved for general angles. The Corollary 1 definitions sigma_{s,i}=d_sh cos(alpha) gamma + d_sh sin(alpha) eta and ς_{s,i}=d_sv cos(beta) vartheta + d_sv sin(beta) cos(alpha) eta - d_sv sin(beta) sin(alpha) gamma imply that r_{tm,y} has coefficient X sin(alpha), not X cos(alpha) as printed in (1). Since the channel phases, constraints (11)-(12), and all subsequent optimization and simulation results are built on these coordinates, this inconsistency is load-bearing. The authors must state the correct rotation matrix, confirm which map was actually used for Figures 2-4, and either rerun the simulations with the correct map or justify the printed map physically.
[Theorem 1 and Eq. (6)] The upper bound in Theorem 1 rests on the approximation in (6), taken from [11, Theorem 1] under the LIA and ALR conditions. The manuscript gives no quantitative error bound, and the simulations use L=15 multipath components with a movable region A=2.5λ, a setting in which the 'arbitrarily large region' condition cannot hold. The authors should either prove the approximation for the simulated regime or validate it numerically, and they should state how a failure of (6) affects the bound (7) and the convergence argument that relies on boundedness.
[Algorithm 1 and Section IV] The convergence statement following Algorithm 1 ('monotonically non-increasing... ensuring convergence to a stationary point') is not proven. Subproblem P1-b is solved by a greedy circle-intersection selection, and the claim that 'this intersection point is the optimal z_m' is not established for the coupled constraints (16) involving all antennas simultaneously. The penalty update with increasing rho also lacks a formal argument that stationary points of the penalized problem track stationary points of P1. A proof or a precise reference is needed before the convergence claim can be accepted.
[Section V] The simulation description omits several parameters needed for reproducibility: the number of channel paths for Figures 2 and 4, the angular distribution of the scatterers, the initial configuration, the minimum spacing D, the penalty schedule rho, and the number of channel realizations or Monte Carlo drops. Without these details and without code, the comparisons in Figures 3-4 cannot be checked. A complete parameter table and ideally the simulation code should be provided.

minor comments (5)

[Corollary 1, Eq. (8)] In Eq. (8), the second factor has denominator sin^2(pi/lambda (ς_{s,u}-ς_{s,j})) while the numerator uses ς_{s,j}-ς_{s,u}; the square makes the sign immaterial, but the notation should be made consistent.
[Introduction and Section II] The abstract and introduction say '3D rotation angles', but the model uses two rotation angles per panel; please clarify the axis convention and the meaning of 'tilt relative to the z-axis'.
[Footnote 3] The QoS constraint SE_u >= SE_min is mentioned in footnote 3 but is not incorporated into P1; either include it in the formulation or state explicitly that it is deferred to future work.
[Figure 2] The caption of Figure 2 says 'different transmit power p' but the curves are not labeled with the corresponding values of p; please add the values.
[Reference [6]] Reference [6] is cited as 'our previous study' but is an arXiv preprint; please update the reference if a peer-reviewed version is available.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: ROMA performance claims are evaluated with the exact SE objective, and external or baseline citations do not carry the derivation.

full rationale

The paper's claimed ROMA gains are not circular. The optimization problem P1 (Eq. 9) maximizes the actual per-user SE expression in Eq. (5), not the approximate upper bound; the upper bound in Theorem 1 and Corollary 1 is used for analysis and is derived from the external LIA/ALR approximation of [11, Theorem 1], not from the authors' own results. The LIA/ALR condition is an explicit assumption, and whether it is violated at A=2.5λ is a validity concern, not a circularity. The authors' own prior work [6] is cited only as a baseline DE algorithm for comparison in Fig. 2, and the other self-references are background or channel-estimation citations; none carries the load of the claim that ROMA outperforms FPA/MA/RO/AS. The simulation comparisons are self-contained against independent baselines and report SE from Eq. (5) under optimized variables, so no fitted parameter is renamed as a prediction. Separately, Eq. (1)'s rotation map appears not to preserve inter-antenna distances, which is a serious modeling-consistency error, but it does not constitute definitional circularity under the requested categories.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The central derivation leans on the LIA/ALR approximation imported from [11] and on perfect CSI. No new physical entities are introduced; ROMA is a system design. Free parameters are algorithmic (penalty factor) and an unspecified minimum spacing constraint.

free parameters (2)

Minimum antenna spacing D
Constraint (11)-(12) requires a minimum spacing D, but its value is never specified in the paper; the simulation results may depend on this choice.
Penalty factor rho = small initial value, gradually increased
Algorithm hyperparameter in Eq. (14) and (17); its schedule is not quantified, affecting convergence and the final solution.

assumptions (4)

ad hoc to paper LIA condition and ALR assumption hold for all channel paths
Invoked in Section III Eq. (6) and Appendix A Eq. (20) to approximate channel entries as a single term; not justified for finite movable regions used in simulation.
domain assumption Perfect channel state information at both ends
Stated in the footnote at Eq. (3); the analysis and optimization assume no CSI error.
domain assumption Isotropic scattering model with L=15 paths from [7]
Used in simulations; the LIA/ALR approximation may conflict with finite L.
domain assumption Approximation theorem from [11] is exact enough to yield an upper bound
The paper relies on [11, Theorem 1] (Eq. 20) to replace the channel with a rank-one-like form; the error bound delta is not quantified here.

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Cite this review

Pith. "Pith review of ROMA: ROtary and Movable Antenna." pith.science (2026). https://pith.science/paper/KQV4Q4OR

@misc{pith2026250113403,
  author       = {Pith},
  title        = {Pith review of: ROMA: ROtary and Movable Antenna},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KQV4Q4OR}},
  note         = {Machine review of arXiv:2501.13403}
}

read the original abstract

The rotary and movable antenna (ROMA) architecture represents a next-generation multi-antenna technology that enables flexible adjustment of antenna position and array rotation angles of the transceiver. In this letter, we propose a ROMA-aided multi-user MIMO communication system to fully enhance the efficiency and reliability of system transmissions. By deploying ROMA panels at both the transmitter and receiver sides, and jointly optimizing the three-dimensional (3D) rotation angles of each ROMA panel and the relative positions of antenna elements based on the spatial distribution of users and channel state information (CSI), we can achieve the objective of maximizing the average spectral efficiency (SE). Subsequently, we conduct a detailed analysis of the average SE performance of the system under the consideration of maximum ratio (MR) precoding. Due to the non-convexity of the optimization problem in the ROMA multi-user MIMO system, we propose an efficient solution based on an alternating optimization (AO) algorithm. Finally, simulation results demonstrate that the AO-based ROMA architecture can significantly improve the average SE. Furthermore, the performance improvement becomes more pronounced as the size of the movable region and the transmission power increase.

Figures

Figures reproduced from arXiv: 2501.13403 by the authors.

**Figure 3.** Average SE versus the normalized region size A for different system architectures with p = 30 dBm. -5 0 5 10 15 20 25 30 4 6 8 10 12 14 16 [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

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Reference graph

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Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.

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