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REVIEW 4 major objections 5 minor 1 cited by

Dual UAV Cluster-Assisted Maritime Physical Layer Secure Communications via Collaborative Beamforming

T0 review · 4 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read Twin UAV swarms, one relaying and one jamming, can give maritime links both long range and physical-layer secrecy at lower flight energy than multi-hop relays.

desk verdict The dual-cluster maritime relay-and-jammer concept is coherent, but the SINR equations that carry the whole evaluation are dimensionally broken, so the reported results do not support the claims. read the letter →

arxiv 2412.05949 v1 pith:QIJXQZIX submitted 2024-12-08 cs.DC cs.CR

Jiawei Huang , Aimin Wang , Geng Sun , Jiahui Li , Jiacheng Wang , Hongyang Du , Dusit Niyato This is my paper · ORCID
classification cs.DCcs.CR
keywords maritimecommunicationsUAVrelaycollaborativebeamformingphysicallayersecurityvirtualantennaarraymulti-objectiveoptimizationimprovedmayflyalgorithmfriendlyjamming
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 tries to establish that maritime wireless links can be made both longer and secret by dispatching two clusters of UAVs, one cluster forming a virtual antenna array to relay data to a legitimate ship and the other forming a virtual antenna array to beam jamming noise at an eavesdropping ship. The mechanism is collaborative beamforming: synchronizing the phases of many UAV-mounted antennas creates a strong directional signal at the intended receiver without any UAV flying the full distance. The paper formulates the problem as a three-objective optimization, namely maximize the legitimate ship's SINR, minimize the eavesdropper's SINR, and minimize total UAV flight energy, and solves it with an improved mayfly swarm algorithm. Simulation results claim the dual-cluster collaborative-beamforming approach beats non-CB, single-CB, and multi-hop baselines, and that the improved algorithm finds better frontier solutions than four comparison algorithms.

What carries the argument

The central object is the maritime UAV-enabled virtual antenna array (MUVAA), a swarm of synchronized UAVs whose collective array factor $AF_r(\theta,\phi)$ (relay) or $AF_j(\theta',\phi')$ (jammer), together with the antenna gain normalization and path-loss models, determines the SINR at Bob and Willie. The array factor and gain equations convert UAV positions and excitation current weights into directional gain toward each vessel; the SINR expressions in Eqs. (7) and (8) then combine the relay gain, jammer gain, and path losses into the objectives $f_1$ and $f_2$, while the propulsion energy model in Eqs. (9)-(10) gives $f_3$. The proposed IMOMA carries the optimization: Tent-chaotic initialization spreads the initial solution population, and hybrid WOA/AOA update rules move the relay and jammer sets with different step sizes and boundary handling.

What would settle it

Re-run the simulations with the dB path losses in Eqs. (3) and (6) explicitly converted to linear factors, $PL_{\text{lin}}=10^{-PL_{\text{dB}}/10}$, before they enter Eqs. (7) and (8). If the reported SINR values, for example about 20.8 dB for Bob and -39.9 dB for Willie in the larger network, change materially, then the dimensional slip in the objective functions is what produced the headline separation, and the comparison to multi-hop, non-CB, and single-CB baselines needs to be recomputed.

Watch

Extended reading notes

Core claim

On its own terms, the paper's discovery is that two UAV clusters working as beamforming arrays can serve as a long-range relay and a long-range jammer at the same time, and that the joint placement and current-weight design of both arrays can be posed as a Pareto optimization that meaningfully separates the legitimate and eavesdropping vessels. The key outcome is a clean separation in SINR: in the larger-scale simulation the legitimate vessel's SINR is positive (about 20.8 dB) while the eavesdropper's is deeply negative (about -39.9 dB), and the relay-and-jammer arrangement achieves this with total flight energy about an order of magnitude below the multi-hop baseline. If the model holds, this means friendly jamming does not have to be close to the eavesdropper: collaborative beamforming lets a distant UAV swarm concentrate jamming power on the eavesdropper while a second swarm concentrates data power on the legitimate ship.

Load-bearing premise

The load-bearing premise is that the signal-loss numbers the paper computes in decibels are converted to ordinary multiplying factors before being fed into the signal-to-interference-plus-noise ratio formulas; if they are not, those formulas have no meaningful units.

Editorial extensions

If this is right

  • If the central claim is correct, a shore station can talk to a distant vessel through one tightly packed UAV swarm, and another swarm can shield that link from a known eavesdropper location without either swarm flying close to the vessels.
  • The reported SINR separation means physical-layer security can be achieved as a by-product of array geometry and current-weight design, rather than requiring cryptography or high-power jamming near the target.
  • The large energy gap versus multi-hop relaying would make the CB-based system the preferred architecture when UAV endurance is the constraint, provided the synchronization overhead is as small as the paper states.
  • The IMOMA's improvement on the eavesdropper-side objective (up to 43.20%) indicates that most of the algorithm's gain is in shaping the jamming array, which could be the deciding criterion in selecting an optimizer for this problem class.

Reading between the lines

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

  • An untested but direct extension is to track vessels in motion rather than fixed positions; the paper itself lists this as future work, and a dynamic version would need the energy model to include trajectory, which the current start/end formula cannot capture.
  • The same two-array geometry should transfer to non-maritime long-range settings, such as rural or disaster-area links, where one swarm relays data and a second swarm protects against eavesdroppers; nothing in the SINR math is specific to sea-surface propagation except the path-loss constants.
  • A fair numerical test would recompute the SINR objectives after converting the dB path-loss values in Eqs. (3) and (6) into linear factors; if the reported results survive that conversion, the comparison claims are credible, and if not, the quantitative comparisons would need to be redone.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper proposes a dual-UAV-cluster maritime secure communication system in which one cluster forms a virtual antenna array relay and another forms a virtual antenna array jammer. It formulates a three-objective problem (maximize Bob SINR, minimize Willie SINR, minimize UAV flight energy), proposes an improved multi-objective mayfly algorithm with chaotic initialization and hybrid update strategies, and reports simulations claiming that the CB-based approach outperforms non-CB, single-CB, and multi-hop baselines and that IMOMA outperforms several metaheuristics. The system concept is relevant, but the stated SINR metric is dimensionally invalid and the energy objective is underspecified; the paper's central claims are therefore not supported.

Significance. If the system model were correct, the paper would address a timely problem: long-range maritime secure communications using UAV clusters with collaborative beamforming. The paper also has positive elements: it considers conflicting objectives, compares several baselines, and provides convergence diagnostics (IGD, ACR, solution distributions). However, the central quantitative claims rest on SINR expressions that mix dB and linear quantities and on an energy model that is not connected to any specified trajectory. The claimed CB advantage and the reported numerical comparisons are therefore not physically interpretable, and the significance of the findings cannot be evaluated.

major comments (4)
  1. [§III-B, Eqs. (7)–(8) and (3), (6)] The SINR expressions insert the dB path-loss values PL_B and PL'_B directly as multiplicative factors in linear power ratios. Since Eqs. (3) and (6) define path loss in dB (including 20log10 terms), the correct linear attenuation factor is 10^{-PL_dB/10} (or the equations must be rewritten in linear units before insertion). The same issue affects the noise term: Table IV lists σ² as -150 dBm, a logarithmic power, while Eq. (7) uses σ² as a linear noise power. Because f1 and f2 are defined directly as these SINRs in Eqs. (11)–(12), every objective value and every comparison in Section VI is computed from a dimensionally invalid metric. This is a central, load-bearing error.
  2. [§III-B, Eqs. (2) and (7)–(8)] The factor N_UR in Eq. (7) appears to double-count the array size: G_B in Eq. (2) already contains |AF_r|^2, which for in-phase excitations is proportional to N_UR^2 (and the unnormalized array factor in Eq. (1) already sums over all relay UAVs). Unless the authors define P_UR as total power and G_B as a per-element gain, the extra N_UR multiplies the array gain a second time; the identical issue holds for N_UJ in the denominator. No such clarification is given, so the SINR values (e.g., f1=15.5, f2=-27.9 in Table V) are not physically interpretable. This extra factor also inflates the apparent advantage of CB over the non-CB baseline, making the conclusion in Section VIII an artifact of the formula rather than a demonstrated system result.
  3. [§IV-C, Problem Analysis] The NP-hardness claim is not established. The argument discretizes the continuous f3 and then states that the transformed problem 'can be regarded as a combinatorial optimization problem which is NP-hard [54]', without giving a reduction from a known NP-hard problem or specifying how the constraints in Eqs. (14d)–(14g) encode such a problem. The same holds for f1/f2, which are asserted to be 'usually NP-hard' by citation. The algorithm itself is a heuristic and does not require a rigorous NP-hardness proof, but the paper's motivation for a metaheuristic is weakened by the unsupported claim.
  4. [§III-C and §IV-B, Eq. (10) and Eq. (13)] The energy objective f3 is underdetermined. Eq. (10) defines energy as an integral over a trajectory v(t), yet the optimization variables in SEMCMOP are only final UAV positions and excitation weights, and no trajectory is specified in the constraints or in the simulation setup. The values of f3 in Table V and the convergence experiments therefore cannot be reproduced or physically interpreted. If the authors intend straight-line constant-speed flight between initial and final positions, that assumption must be stated explicitly and included in Eq. (10) and in the constraint set.
minor comments (5)
  1. [§IV-C] In the large-scale dimension count, the jammer-set variables are written as (Xr, Yr, Zr, Ir) twice; the second should be (Xj, Yj, Zj, Ij).
  2. [Table V] The entries '6.6×1046.6×1046.6×104' and '1.4×1051.4×1051.4×105' appear corrupted by duplicated typesetting and should be corrected.
  3. [Algorithm 3] The comment '#Exploration phase' after Eq. (21) is inside the exploitation branch; it should read '#Exploitation phase'.
  4. [Section VII] The claims that data-sharing overhead is 10–20 seconds and that the method saves 50–90% of time are not derived in this paper; they need a supporting calculation or a clearer reference to [74].
  5. [§II-B, last paragraph] The paper acknowledges that CB has limitations such as communication overhead and limited multi-user support, but these costs are not reflected in f1–f3; the Discussion in Section VII only addresses data-sharing overhead qualitatively.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the CB-vs-non-CB result follows from the array-gain model, and the IMOMA comparisons are self-contained simulations, while the dB-unit SINR issue is a correctness defect rather than a circular reduction.

full rationale

The derivation chain is forward-defined: array factors in Eqs. (1) and (4), antenna gains in Eqs. (2) and (5), path losses in Eqs. (3) and (6), SINRs in Eqs. (7) and (8), and objectives in Eqs. (11)-(13) are all model statements, not quantities fitted from the results they are later used to support. The conclusion that the CB-based method outperforms non-CB is a direct consequence of the array-gain model, which is the intended simulation scenario rather than a fitted prediction; no parameter is inferred from the outputs and then renamed as a finding. The IMOMA-versus-MOMA/MODA/MOMVO/MALO comparisons are self-contained benchmark runs on the same objectives, so they do not reduce to their inputs by construction. The paper does cite prior work by overlapping authors, notably [7], [40], and [66], for standard claims such as the N_U^2 array gain and the energy benefit of clustered UAVs, but those claims are also embodied in Eqs. (1)-(2) and are externally checkable antenna-array results, so the self-citations are not load-bearing in the circularity sense. The more serious concerns in the paper are dimensional: Eqs. (7) and (8) appear to insert dB-valued path losses directly into a linear power ratio, and the N_UR/N_UJ factors may double-count the array gain already contained in G_v/G'_v. Those are modeling and correctness defects that invalidate the quantitative comparisons, but they do not make an output equivalent to an input by construction, and they are not circularity under the required standard. Similarly, the multi-hop row in Table V assigns f1=f2=0 rather than deriving those values from an explicit link calculation; that is an unsupported baseline choice, not a circular derivation. Overall, no significant circularity is present, so the score is 1.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The central claims rest on a set of channel and platform assumptions inherited from the authors' prior collaborative-beamforming line of work, plus several algorithm hyperparameters that are never specified; none of these are given independent experimental validation in the paper.

free parameters (5)
  • Tent map parameter a
    Eq. (16) uses a in [0,1] to control chaotic initialization, but the paper never lists its value; results depend on it.
  • AOA coefficients M and M'
    Eqs. (20)-(27) and Table III define M and M' as AOA coefficients, but Table IV does not set their values.
  • AOA control parameters mu, Min, Max
    Eqs. (20), (21), (23), (24), (26), (27), and (29) use mu, Min, and Max to tune exploration and exploitation; no numeric values are given.
  • WOA parameters l and H
    Eqs. (19), (22), and (25) use spiral and encircling parameters l and H from [64]; values are not provided.
  • Population size and maximum iteration = N=30, tmax=500
    Chosen by hand in Section VI-A; typical for MOEAs, but no sensitivity analysis is run.
assumptions (5)
  • domain assumption UAVs in the same virtual antenna array are synchronized in carrier frequency, initial phase, and time.
    Stated in Section III-A; without it the coherent array factor in Eqs. (1) and (4) and the N^2 gain do not hold.
  • domain assumption Data sharing among UAVs in a cluster is reliable and does not constrain relay rates.
    Section III-B1 states that the G2A link and airborne sharing can be made reliable via caching and adaptive rates, removing the information dissemination constraint from the optimization.
  • domain assumption The air-to-sea path loss takes the elevated-LoS form of Eqs. (3) and (6) with parameters from satellite and UAV-terrestrial studies [51], [66].
    The numerical results depend on these channel parameters such as alpha_a, alpha_b, Cr, eta_LOS, and eta_NLOS; the paper does not validate them for maritime air-to-sea links.
  • domain assumption Propulsion energy of a rotary-wing UAV follows the model of Eqs. (9) and (10) from [53], and the energy of a 3D move can be closed-form approximated from initial and final positions without specifying the trajectory.
    The f3 objective uses Eq. (10) as a function of final positions only, but actual energy depends on the path; Section III-C explicitly calls this a heuristic closed-form approximation.
  • domain assumption Vessel positions, Bob and Willie, are fixed and known in advance.
    Section III-A fixes Bob and Willie at known coordinates; the paper acknowledges this and defers dynamic vessel movement to future work.

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Pith. "Pith review of Dual UAV Cluster-Assisted Maritime Physical Layer Secure Communications via Collaborative Beamforming." pith.science (2026). https://pith.science/paper/QIJXQZIX

@misc{pith2026241205949,
  author       = {Pith},
  title        = {Pith review of: Dual UAV Cluster-Assisted Maritime Physical Layer Secure Communications via Collaborative Beamforming},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/QIJXQZIX}},
  note         = {Machine review of arXiv:2412.05949}
}
read the original abstract

Unmanned aerial vehicles (UAVs) can be utilized as relay platforms to assist maritime wireless communications. However, complex channels and multipath effects at sea can adversely affect the quality of UAV transmitted signals. Collaborative beamforming (CB) can enhance the signal strength and range to assist the UAV relay for remote maritime communications. However, due to the open nature of UAV channels, security issue requires special consideration. This paper proposes a dual UAV cluster-assisted system via CB to achieve physical layer security in maritime wireless communications. Specifically, one UAV cluster forms a maritime UAV-enabled virtual antenna array (MUVAA) relay to forward data signals to the remote legitimate vessel, and the other UAV cluster forms an MUVAA jammer to send jamming signals to the remote eavesdropper. In this system, we formulate a secure and energy-efficient maritime communication multi-objective optimization problem (SEMCMOP) to maximize the signal-to-interference-plus-noise ratio (SINR) of the legitimate vessel, minimize the SINR of the eavesdropping vessel and minimize the total flight energy consumption of UAVs. Since the SEMCMOP is an NP-hard and large-scale optimization problem, we propose an improved swarm intelligence optimization algorithm with chaotic solution initialization and hybrid solution update strategies to solve the problem. Simulation results indicate that the proposed algorithm outperforms other comparison algorithms, and it can achieve more efficient signal transmission by using the CB-based method.

Figures

Figures reproduced from arXiv: 2412.05949 by the authors.

Figure 1
Figure 1. A CB-based dual UAV cluster-assisted maritime secure communication [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Evolutionary outline based on the MOMA. perform synchronized flights over water to attract females. In response, female mayflies approach the swarm for mating. This process is more effective in balancing exploration and exploitation [59]. Then, after mating, the female mayflies produce offspring, of which only the healthier ones can survive after hatching. If the offspring demonstrates superior fitness, it will disp… view at source ↗
Figure 3
Figure 3. Gain distributions optimized by the IMOMA in larger scale network. [PITH_FULL_IMAGE:figures/full_fig_p013_3.png] view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: Movement paths optimized by the IMOMA in larger scale network. [PITH_FULL_IMAGE:figures/full_fig_p014_4.png]
Figure 5
Figure 5. Figure 5: The values of SINR of Bob and Willie obtained by the approaches [PITH_FULL_IMAGE:figures/full_fig_p014_5.png]
Figure 6
Figure 6. Figure 6: A UAV multi-hop maritime communication system. [PITH_FULL_IMAGE:figures/full_fig_p014_6.png]
Figure 7
Figure 7. Figure 7: Solution distributions obtained by different algorithms in larger and [PITH_FULL_IMAGE:figures/full_fig_p015_7.png]
Figure 8
Figure 8. Figure 8: The optimization objective values obtained by different algorithms in [PITH_FULL_IMAGE:figures/full_fig_p015_8.png]
Figure 9
Figure 9. Figure 9: The optimization objective values obtained by different algorithms of [PITH_FULL_IMAGE:figures/full_fig_p016_9.png]
Figure 10
Figure 10. Figure 10: Convergence analysis of the IMOMA. REFERENCES [1] J. Huang, A. Wang, G. Sun, and J. Li, “Jamming-aided maritime physical layer encrypted dual-UAVs communications exploiting collaborative beamforming,” in IEEE CSCWD, 2023, pp. 1142–1147. [2] H. Zhang, T. Zhou, T. Xu, M…

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Pith tools

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