Latency Minimization for Multi-AAV-Enabled ISCC Systems with Movable Antenna

Chunjie Wang; Wenchao Liu; Xuhui Zhang; Yanyan Shen; Yinyu Wu; Yiyang Chen

REVIEW 4 major objections 6 minor 1 cited by

Latency Minimization for Multi-AAV-Enabled ISCC Systems with Movable Antenna

T0 review · 4 major / 6 minor · reviewed 2026-08-05 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read Jointly optimizing antenna positions, transmit beamforming, and base station computation allocation minimizes the worst-case latency of drone sensing tasks in an integrated sensing, communication, and computation system, with a two-layer al

desk verdict Competent engineering extension of MA to multi-drone ISCC; the central latency gains rest on an unproven rank-one relaxation, but the combination is worth refereeing. read the letter →

arxiv 2508.05574 v1 pith:SYPTMXCZ submitted 2025-08-07 cs.IT eess.SPmath.IT

Yiyang Chen , Wenchao Liu , Chunjie Wang , Yinyu Wu , Xuhui Zhang , Yanyan Shen This is my paper

classification cs.ITeess.SPmath.IT

keywords integratedsensingcommunicationandcomputation(ISCC)movableantenna(MA)autonomousaerialvehicles(AAVs)latencyminimizationtransmitbeamformingoffloadingparticleswarmoptimizationsemidefiniterelaxation

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

This paper studies drones that both sense a target area and offload the data they gather to a ground base station for computation, and asks how to get all of these tasks done as fast as possible. Its answer is to let the antennas on the drones move: by jointly optimizing the antenna positions, the transmit beamforming, and the base station's computation allocation, the worst-case processing latency across drones can be minimized. The paper formulates this as a non-convex problem, proposes a two-layer iterative algorithm that alternates particle swarm optimization over antenna positions with convex optimization over the remaining variables, and reports in simulation that this scheme converges in a moderate number of iterations and beats fixed-position and random-position antenna baselines. The reason this matters is that real-time applications such as wildfire monitoring and urban mapping depend on closing the loop between sensing, communication, and computation; the paper shows a concrete way to shave latency from that loop without extra spectrum or power.

What carries the argument

The central object is the joint optimization problem $\mathrm{P1}$, whose objective is the maximum (worst-case) latency over all drones, and whose key coupling is the steering vector $\mathbf{g}_m$ that maps each movable antenna's position to the sensing beampattern gain $|\mathbf{g}_m^H(\mathbf{w}_m\mathbf{w}_m^H + \mathbf{V}_m)\mathbf{g}_m|$. The algorithm's load-bearing mechanism is the two-layer decomposition: the outer layer runs particle swarm optimization over the antenna position variables, while the inner layer fixes positions and solves a convex reformulation (semidefinite relaxation plus Charnes-Cooper transformation) for the beamforming vectors $\mathbf{w}_m$, sensing covariance

What would settle it

Solve the inner-layer problem with the paper's parameters and inspect the rank of the relaxed beamforming covariance matrix; if the rank is ever greater than one, or if a beamformer reconstructed from the relaxed solution violates the sensing beampattern or transmit power constraints, the reported latency reductions over the fixed and random baselines would not be realizable in practice.

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Extended reading notes

Core claim

The paper's discovery is that movable antennas are a useful lever in integrated sensing, communication, and computation (ISCC) systems: they can be positioned to improve the trade-off between sensing beampattern gain and communication rate, which in turn lowers the time needed to transmit and process the tasks. The system has multiple hovering drones, each with a small array of movable antennas, sensing a clustered target area and sending its generated computation task to a base station over orthogonal frequency bands. The optimization problem $\mathrm{P1}$ minimizes the maximum of the sensing, transmission, and computation latencies across all drones, subject to a minimum sensing beampatter

Load-bearing premise

The inner optimization step relies on a convex relaxation that drops the requirement that the beamforming solution be a single beam, and this relaxation is never shown to be exact; if it fails, the latency gains reported over the baselines would not be achievable.

Editorial extensions

If this is right

Deploying movable antennas on hovering drones can cut worst-case processing latency without extra transmit power or spectrum.
The proposed two-layer algorithm reaches its reported latency within roughly 140 iterations, making it a practical design tool for network planning.
Latency gains from antenna movement widen as the number of antennas per drone or the base station's computation budget grows.
The scheme's gains hold in an orthogonal-access (FDMA) setting, so they isolate the benefit of antenna positioning rather than interference management.
With more drones, total sensing tasks grow and latency rises; optimized antenna placement still beats fixed and random arrays in that regime.

Reading between the lines

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

Because the inner layer relies on semidefinite relaxation without a proof of rank-one tightness, the reported latency values are best read as upper bounds on achievable performance; checking the rank of the relaxed covariance matrix across the simulated settings would turn the numerical claim into a certified one.
The same two-layer decomposition could be applied to other ISCC objectives, such as minimizing total energy or age of information, by swapping the inner objective while keeping the antenna-position search unchanged.
A natural stress test is to compare the PSO solution against exhaustive search over antenna positions for small arrays; that would reveal how close the heuristic is to the true global optimum.
If antenna movement is interpreted as shaping the channel phases seen by the base station, the results suggest that physical-layer reconfiguration can substitute for some computation or transmit-power resources at the system level.

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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 paper is a competent engineering extension: it takes movable-antenna degrees of freedom and puts them into a multi-AAV integrated sensing, communication, and computation latency-minimization problem. The problem statement is genuinely new as a combination, and the system model is clear. The two-layer algorithm—PSO for antenna positions, convex optimization for beamforming and resource allocation—is a reasonable heuristic, and the simulations are self-consistent: the proposed scheme beats the fixed and random antenna baselines in every figure.

Where it gets less solid: the inner SDR relaxation. In Section III-B, after Charnes-Cooper, the rank-one constraint on the information-beamforming covariance is dropped. There is no tightness proof, no Gaussian randomization, no eigenvalue check. If the relaxed solution has rank greater than one, the reported latency is not achievable by any single beamformer. Since the same relaxed objective is used for all baselines and all curves, the gains shown in Figs. 3–7 could be optimistic. The stress-test note lands here; this is the load-bearing unproven premise.

Secondary issue: Section III-C says the objective "does not increase during the iterations," but PSO with a penalty fitness does not guarantee monotonicity of the current iterate, only of the stored global best. That is a wording and convergence-claim problem, not a fatal one. Also, the baselines are fixed and random antenna positions, not state-of-the-art MA-aware or ISCC schedulers, so calling the improvement "significant" overstates the comparison. No code or data ship, so the only evidence is the simulation setup described in the text.

I do not think there is a new principle here; the gains are incremental degrees-of-freedom benefits. But the direction is plausible and the paper is honest in its own terms. A good referee could push on rank-one recovery and a stronger baseline, and the paper would be acceptable after revision. I would send it to review rather than desk-reject. I would not cite it in the next year unless I was working on MA-ISCC specifically; it might be useful for a reading group on movable antennas.

Referee Report

4 major / 6 minor

Summary. This paper studies a multi-AAV integrated sensing, communication, and computation (ISCC) system in which hovering AAVs equipped with movable-antenna arrays sense clustered ground targets and offload the resulting computation tasks to a ground BS. The authors formulate a max-latency minimization problem (P1) over the MA positions, transmit beamforming vectors, sensing-signal covariance matrices, and BS computation-resource allocation. They propose a two-layer iterative algorithm: an outer PSO layer updates antenna positions, and an inner convex layer (after a Charnes-Cooper transformation and semidefinite relaxation) solves the beamforming and resource-allocation subproblem. Numerical results compare the proposed scheme with fixed-position (FPA) and random-position (RPA) antenna baselines and report substantial latency reductions. The central claim is that this algorithm effectively minimizes the maximum processing latency for the considered system.

Significance. The scenario is relevant and timely, and the two-layer decomposition is a practical way to approach a strongly coupled nonconvex problem. The numerical study covers the main system parameters and provides an empirical convergence check, which is useful. The FDMA assumption cleanly removes inter-AAV interference and makes the inner layer tractable. However, the paper does not supply machine-checked proofs, reproducible code, or parameter-free derivations; it is a heuristic-algorithm-plus-simulation contribution. Two premises are load-bearing for the reported numbers: (i) the inner SDR admits a rank-one optimal beamforming matrix, and (ii) the PSO outer loop produces feasible, near-optimal antenna positions. Neither is established rigorously, and the convergence statement in Section III-C is incorrect as written. If the SDR issue is resolved, the paper would be a competent incremental contribution of interest to the ISAC/MA community; in its present form, the quantitative gains are not fully justified.

major comments (4)

[Section III-B, inner-layer SDR (P4)] The inner-layer problem P4 is solved by SDR after a Charnes-Cooper transformation, dropping rank(W_i)=1 that encodes W_i=w_i w_i^H. The manuscript provides no proof that an optimal relaxed solution is rank-one, and Section IV reports no eigenvalue-based rank check or Gaussian-randomization recovery. If rank(W_i^*)>1, the relaxed objective is not achievable by any single w_i, and reconstruction can violate sensing/power constraints. Since the same relaxed objective is used for the proposed and FPA/RPA schemes in Figs. 3-7, the reported gains may be optimistic. This is a load-bearing internal-validity issue. Please add a tightness proof or, at minimum, a rank-one recovery step with feasibility projection, and report the post-reconstruction objective and constraint violations.
[Section III-C, convergence of Algorithm 1] The statement "As the objective function of the problem P1 does not increase during the iterations, Algorithm 1 is ensured to converge [19]" is not correct for the PSO outer loop. In Algorithm 1, the current particle position u_i^{(t+1)} from (22) can increase the penalized fitness (23); only the stored global best is non-increasing. Moreover, (23) is a penalized fitness, not the original P1 objective, so monotonicity of the best fitness does not imply monotonicity of the actual maximum latency. The cited reference [19] does not contain a PSO convergence theorem. Please correct the statement: either prove convergence of the global-best sequence under the given penalty dynamics, or describe Fig. 3 as empirical convergence of the best-so-far objective and weaken the claim accordingly.
[Section III-C / Eq. (23), penalty-based feasibility] The outer PSO uses penalty terms delta1*Psi1 and delta2*Psi2 to handle constraints (12b) and (12d), and Eq. (23) assigns a penalized fitness. A penalized fitness does not guarantee that the returned global-best particle is feasible; the paper reports no constraint-violation metrics, no feasibility-restoration step, and no post-processing check. Because the final MA positions determine the channel and hence the reported latency, any residual violation can make the numerical results infeasible. Please verify and report the constraint residuals of the output solution, or add a projection/restoration step and use only feasible solutions in the latency curves.
[Section IV, RPA benchmark] The RPA baseline is described as selecting antenna positions randomly while satisfying constraints (12a)-(12b), but the paper does not state how many random realizations are used or whether results are averaged. A single random draw can be arbitrarily unfavorable, so the "significant improvement over RPA" in Figs. 3-7 cannot be assessed statistically. If the RPA curve is a single draw, the comparison is not meaningful; if it is averaged, the number of trials and variance/confidence intervals must be reported. The authors should also clarify whether the proposed and baseline curves use the same inner-layer SDR objective (and thus inherit the same rank-one issue) or a feasible reconstruction.

minor comments (6)

[Section III-C, complexity] The complexity expression O(I_max(2N^2+K)^3.5 log(1/epsilon)) omits the PSO swarm size P and the per-particle inner-loop calls; it should include a factor P (and possibly the inner SDR iteration cost).
[Notation] The index i is used both for AAVs and for PSO particles; renaming the particle index (e.g., p) would avoid ambiguity in (20)-(23).
[Throughout] The rendered manuscript contains many missing mathematical symbols and equation references (indicated by '?'), and several pages/figures are not visible in the provided text. Please ensure a cleanly typeset PDF with all equations and figures embedded.
[Section V / Conclusion] The conclusion states that the maximum latency "is minimized," but the proposed method is a heuristic PSO+SDR algorithm with no global optimality certificate. Please qualify the claim, e.g., "is effectively reduced by the proposed algorithm."
[Section IV / Fig. 3] Please specify whether the plotted convergence curve is the penalized fitness (23) or the true maximum latency of the best-so-far feasible solution. If it is the penalized fitness, the curve may be misleading.
[Algorithm 1 input] The algorithm requires an initial feasible solution, but Section IV does not specify how the initial MA positions are chosen for the proposed method. Please state the initialization and whether it is the same for all compared schemes.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; the derivation is self-contained. Correctness risks (SDR rank-one tightness, PSO convergence assertion) are not circularity.

full rationale

The paper's optimization pipeline is assembled from an explicit system model (communication rate, computation latency, sensing beampattern constraint), and the objective is minimized by a two-layer algorithm. No parameter is fitted to a target and then renamed a prediction: the inner-layer solves a semidefinite relaxation and the outer-layer runs PSO over antenna positions, and the MA-position optimization is a superset of the fixed/random-position baselines, so the qualitative gains are an expected consequence of enlarging the feasible set rather than a circular reduction. Self-citations ([7], [10], [11], [18]) appear only as background/prior-art pointers in the introduction and are not load-bearing for the latency-minimization derivation. Two correctness concerns are real but distinct from circularity: (1) Section III-B drops the rank-one constraint after the Charnes-Cooper transformation without proving rank-one tightness or adding Gaussian randomization, so the reported beamforming gains may be optimistic; (2) Section III-C's assertion that 'the objective function of the problem P1 does not increase during the iterations' is not established for PSO and is in tension with the algorithm's use of a stored global best. Neither of these is a definitional or self-citation-based reduction of the claimed result to its own inputs. The numerical validation is internal (synthetic channels defined by the authors), but internal validation is not circularity. Score 0.

Assumptions & free parameters 3 free parameters · 7 assumptions · 0 invented entities

The central claim rests on standard domain assumptions for drone, channel, and computation modeling, plus two ad hoc algorithmic premises: the tightness of the semidefinite relaxation and the reliability of PSO convergence. No new physical entities are postulated. The free parameters are optimization hyperparameters chosen by hand; they affect the reported numerical gains but are not fitted to external data.

free parameters (3)

PSO penalty parameters δ1, δ2 = 100
Hand-chosen constants in the penalty-based fitness function (Eq. 23). They balance feasibility against objective value and can change the final solution.
PSO inertia weight bounds w_max, w_min = 0.9, 0.4
Hand-chosen bounds in the inertia update (Eq. 21); they strongly influence exploration and convergence of the outer layer.
PSO learning factors c1, c2 = 1.5
Hand-chosen cognitive and social acceleration coefficients in the PSO velocity update.

assumptions (7)

domain assumption The channel between each AAV and the BS is line-of-sight with known angles and distances, so the MA position affects only phase shifts.
Invoked in the MA channel model and the steering vector (Eq. (2)-(3)); if scattering is significant, the latency gains from MA positioning can vanish.
domain assumption The sensing target area can be represented by a single steering direction per AAV, with the beampattern gain as the sensing quality metric.
Used in the sensing constraint g_i^H (w_i w_i^H + V_i) g_i >= ...; real target areas have angular spread, so a single AoD is an idealization.
domain assumption FDMA guarantees no inter-AAV interference, so each AAV's rate depends only on its own signal and noise.
Used in the rate expression; in practice adjacent bands may leak, and perfect orthogonality is an assumption.
domain assumption The computation model assumes deterministic task sizes and a fixed number of CPU cycles per bit.
Used in the latency expression; random task arrivals and data-dependent computation costs are not modeled.
standard math Charnes-Cooper transformation preserves optimality for the fractional SINR objective.
Invoked in the inner-layer reformulation; this is a standard result for fractional programming.
ad hoc to paper Semidefinite relaxation yields a rank-one solution for the beamforming matrix.
The inner-layer solver relies on dropping the rank-one constraint; no proof of tightness is given, and no randomization recovery is mentioned.
ad hoc to paper The PSO algorithm converges to a feasible and good solution for P1.
The convergence claim cites monotonic non-increase of the objective, which is not a property of PSO iterates; the claim is unsupported.

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Cite this review

Pith. "Pith review of Latency Minimization for Multi-AAV-Enabled ISCC Systems with Movable Antenna." pith.science (2026). https://pith.science/paper/SYPTMXCZ

@misc{pith2026250805574,
  author       = {Pith},
  title        = {Pith review of: Latency Minimization for Multi-AAV-Enabled ISCC Systems with Movable Antenna},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SYPTMXCZ}},
  note         = {Machine review of arXiv:2508.05574}
}

read the original abstract

This paper investigates an autonomous aerial vehicle (AAV)-enabled integrated sensing, communication, and computation system, with a particular focus on integrating movable antennas (MAs) into the system for enhancing overall system performance. Specifically, multiple MA-enabled AVVs perform sensing tasks and simultaneously transmit the generated computational tasks to the base station for processing. To minimize the maximum latency under the sensing and resource constraints, we formulate an optimization problem that jointly coordinates the position of the MAs, the computation resource allocation, and the transmit beamforming. Due to the non-convexity of the objective function and strong coupling among variables, we propose a two-layer iterative algorithm leveraging particle swarm optimization and convex optimization to address it. The simulation results demonstrate that the proposed scheme achieves significant latency improvements compared to the baseline schemes.

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

UAV-Enabled Fluid Antenna Systems for Multi-Target Wireless Sensing over LAWCNs
cs.IT 2025-09 reject novelty 4.0 of 10

A joint trajectory, beamforming, and fluid-antenna-position optimization is proposed to minimize the average Cramér-Rao bound for multi-target sensing from a UAV.

Reference graph

Works this paper leans on

19 extracted references · 17 canonical work pages · cited by 1 Pith paper

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Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.

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Z. Xiao, X. Pi, L. Zhu, X.-G. Xia, and R. Zhang, “Multiuser commu- nications with movable-antenna base station: Joint antenna positioning, receive combining, and power control,” IEEE Trans. Wireless Commun., vol. 23, no. 12, pp. 19744–19759, Dec. 2024

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Z. Wei, H. Qu, Y . Wang, X. Yuan, H. Wu, Y . Du, K. Han, N. Zhang, and Z. Feng, “Integrated sensing and communication signals toward 5G-A and 6G: A survey,” IEEE Internet Things J. , vol. 10, no. 13, pp. 11068– 11092, Jul. 2023

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G. Cheng, X. Song, Z. Lyu, and J. Xu, “Networked ISAC for low- altitude economy: Coordinated transmit beamforming and UA V trajec- tory design,” IEEE Trans. Commun. , to appear, 2025

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W. Liu, Z. Jin, X. Zhang, W. Zang, S. Wang, and Y . Shen, “AoI-aware UA V-enabled marine MEC networks with integrated sensing, computa- tion, and communication,” in Proc. IEEE/CIC Int. Conf. Commun. China Workshops (ICCC Workshops), pp. 1–6, Dalian, China, Aug. 2023

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H. Xie, T. Zhang, X. Xu, D. Yang, and Y . Liu, “Joint sensing, com- munication, and computation in UA V-assisted systems,” IEEE Internet Things J., vol. 11, no. 18, pp. 29412–29426, Sep. 2024

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Y . Cang, M. Chen, and Z. Yang, “Cooperative detection for MEC aided multi-static ISAC systems,” in Proc. IEEE V eh Technol Conf (VTC2024- Fall), pp. 1–6, Washington, DC, United states, Oct. 2024

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Movable antennas meet low-altitude wireless networks: Fundamentals, opportunities, and future directions,

W. Liu, X. Zhang, C. Wang, J. Ren, and W. Yuan, “Movable antennas meet low-altitude wireless networks: Fundamentals, opportunities, and future directions,” arXiv preprint arXiv:2506.13250 , 2025

arXiv 2025