{"id":"7f61630e-be1d-4c5e-8d04-1ffe2a9e50d1","arxiv_id":"2507.20489","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":5.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":3,"one_line_summary":"A joint UAV trajectory and rotatable-array beamforming design improves secrecy energy efficiency against a location-uncertain eavesdropper in simulated LoS channels.","lead":"This paper simulates a drone that carries a rotatable antenna array and acts as a jammer, jointly optimizing the drone's flight path and the antenna's direction to maximize secure data rate per unit of energy. The authors report about 40% higher secrecy energy efficiency than a fixed-antenna setup, but they do not share code or data.","discovery_kind":"new_application","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Equation (8) is not a valid worst-case bound on Eve's rate: it omits the BS array gain and ignores jammer nulls, so the secrecy objective and the reported 40% SEE gain may be unsupported.","rationale":"The reader's weakest assumption (LoS-only channels) is a genuine robustness limitation, but the more immediate defect is internal: Eq. (8) claims an upper bound on Eve's rate while dropping the transmit array gains |g^H w|^2. In the numerator, the maximum over qe of PB|h_{B,e}^H wB|^2 is at least N_B times the path-loss term; ignoring N_B already makes the bound non-conservative. In the denominator, the optimizer can null the jammer toward Eve, so |h_{J,e}^H wJ|^2 may be near zero; using a positive path-loss lower bound overestimates jamming and understates Eve's rate. Both errors inflate the secrecy capacity lower bound that is the objective in (13)-(14). Because Section IV plots only this surrogate objective, the reported 40% and 10% improvements may not reflect actual secrecy performance. The concrete check above would settle this by recomputing true worst-case rates. I therefore move the verdict from CONDITIONAL to REJECT unless the authors replace Eq. (8) with a valid bound (e.g., including N_B and accounting for nulls) and rerun the comparisons.","tokens_in":8974,"tokens_out":9231,"duration_ms":107470,"concrete_test":"At the parameters of Table I, take the optimized {qJ[n], φJ[n], wJ[n]} returned by Algorithm 1 and recompute the exact worst-case secrecy rate: for each time slot n, evaluate γe[n] = PB|h_{B,e}^H wB|^2 / (PJ|h_{J,e}^H wJ|^2 + σ_e^2) on a fine grid (or via a semidefinite relaxation) over qe∈Θ using the full channel vectors from Eqs. (3)-(4), take the maximum over qe, and compute the exact Rsec and SEE rather than the bound in Eq. (8). If the corrected SEE is substantially below the Fig. 1 values, or if the ranking versus the fixed-antenna or Eve-oriented baselines changes, the headline claim is an artifact of the invalid bound. A quick arithmetic probe: inserting the missing N_B=4 factor into the numerator of Eq. (8) shifts the Eve-rate bound by 6 dB, which at the reported distances should visibly change the secrecy rate.","verdict_should_be":"REJECT","load_bearing_attack":"The central claim rests on the tractable lower bound on secrecy capacity built from Eq. (8). That equation upper-bounds Eve's rate by log2(1 + PB·β0/(||qB−q~e||−ε)^2 / (PJ·β0/(||qJ−q~e||+ε)^2 + σ_e^2)). This is not an upper bound. The actual numerator is PB|h_{B,e}^H wB|^2, whose maximum over qe is at least N_B times the path-loss term (or larger if the MRT beam illuminates Eve), so the missing array gain N_B=4 already makes the bound non-conservative by 6 dB. Conversely, the jammer denominator is PJ|h_{J,e}^H wJ|^2, which the optimizer can drive toward zero by nulling Eve; replacing it with a positive path-loss term PJ·β0/(d+ε)^2 overestimates jamming at Eve and therefore underestimates Eve's rate. Both effects inflate the secrecy capacity lower bound that is the objective in (13)-(14). Since Section IV reports performance of this surrogate objective, the claimed 40% and 10% SEE improvements may be artifacts of the invalid bound rather than of MA orientation. The paper never validates the bound against the exact max over qe in Eq. (7). The LoS assumption in Section II.A.2 is a secondary limitation; Eq. (8) is a correctness defect inside the paper's own model.","agreement_with_reader":"disagree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The manuscript proposes a framework for secure UAV-assisted communications in which a rotary-wing UAV equipped with a movable-antenna (MA) array acts as a friendly jammer. The authors jointly optimize the UAV trajectory, the MA orientation angles, and the jamming beamforming vector to maximize the sum secrecy energy efficiency (SEE), subject to the UAV's velocity constraint, MA angle limits, and a transmit power constraint. The formulation uses a worst-case lower bound on secrecy capacity built from upper-bounding the eavesdropper's rate via simplified path-loss terms. An alternating optimization algorithm is developed, and simulations report roughly 40% SEE improvement over a fixed-antenna baseline and about 10% over an Eve-oriented baseline. The paper is a candidate for IEEE Wireless Communications Letters and is the first to consider an MA-equipped UAV as a jammer.","tokens_in":9305,"tokens_out":4024,"duration_ms":41632,"significance":"If the technical formulation were correct, the paper would make a novel and relevant contribution: combining UAV mobility with movable-antenna array reconfigurability for physical-layer security, and demonstrating energy-efficiency gains. The related work is appropriately cited, and the idea of exploiting MA orientation to form deep nulls and directional beams is timely. However, the central technical validity is compromised by an incorrect worst-case bound, and the numerical results are obtained by optimizing a surrogate objective that is not a valid lower bound on secrecy capacity. The paper also contains internal algorithm inconsistencies and an underived reformulation. These issues are load-bearing and must be resolved before the claims can be trusted.","major_comments":[{"comment":"The claimed upper bound on Eve's rate in Eq. (8) is not valid. The actual numerator of gamma_e in Eq. (6) is PB |h_{B,e}^H w_B|^2, which includes the channel array gain |g_{B,e}^H w_B|^2; with MRT toward the legitimate user this gain can be as large as N_B (here 4), and it can be even larger if the BS beam illuminates Eve. The proposed bound uses only the scalar path-loss term tilde{h}_{B,e}, omitting this array gain and therefore underestimating the numerator. Conversely, the denominator of gamma_e contains PJ |h_{J,e}^H w_J|^2, which the optimizer can drive toward zero by forming a null toward Eve; replacing it with a strictly positive path-loss term tilde{h}_{J,e} overestimates the jamming interference at Eve and underestimates Eve's rate. Both effects inflate the secrecy-capacity lower bound (13)-(14) that is the optimization objective. Since Section IV reports performance of this surrogate objective, the claimed 40% and 10% SEE improvements may be artifacts of the invalid bound rather than of MA orientation. The paper does not validate Eq. (8) against the exact max_{qe} in Eq. (7).","section":"II.B"},{"comment":"The reformulated secrecy rate expression in Eq. (16) is presented without any derivation, and its equivalence to the original rbar_sec is not established. Some terms use channel vectors h (e.g., the second term uses h_{J,e}^H w_J) while others use array-response vectors g (e.g., the first and third terms), and the auxiliary variables mu[n], nu[n], tau[n], chi[n] are introduced without explaining how they transform the objective and constraints. This makes the trajectory subproblem (18) unjustified; a reader cannot verify that maximizing (18) indeed maximizes (15). The authors should provide a clear derivation, including the exact substitutions and the reasons for the claimed convex behaviour.","section":"III.A"},{"comment":"Line 10 of Algorithm 1 states that the MA orientation angles are updated using a 'two-phase approach', but Section III-B describes a projected gradient (feasible direction) method with backtracking line search. These are not the same method, and the paper does not explain what the two-phase approach is or how it relates to the projected gradient update in Eq. (21). The convergence statement in Section III-B relies on the Armijo-Goldstein condition, but if a different update is used in the actual implementation, the convergence guarantee does not apply. Please clarify which method is implemented in the simulations and provide a consistent description.","section":"Algorithm 1"},{"comment":"The removal of the non-negative operator [.]^+ from the objective in Eq. (14) is justified solely by a reference to [4], with no proof for the MA-equipped system considered here. The claim that 'the optimization always leads to a non-negative objective value' is not self-evident, especially because the worst-case bound in Eq. (8) is not a true upper bound; if the bound is corrected, the secrecy lower bound can become negative. The paper should either prove the non-negativity for the actual objective or retain the [.]^+ operator and handle it in the optimization.","section":"III"}],"minor_comments":[{"comment":"The numerical results are reported in vague terms ('around 40% improvement', 'about 10% higher') without exact numbers, confidence intervals, or a sensitivity analysis. Since the central claim is empirical, the paper should provide the precise SEE values and ideally some variation of parameters (e.g., epsilon, N_B, PJ) to show the robustness of the gains.","section":"IV"},{"comment":"There is a grammatical error in the sentence 'The total energy consumption is consists of propulsion power...' It should read 'consists of' or 'is composed of'.","section":"II.B.2"},{"comment":"The descriptions of Figures 1 and 2 are sparse; the text mentions energy consumption and efficiency plots but does not clearly state the axes, units, or the exact metrics displayed. Please add appropriate captions and refer to specific curves in the body.","section":"IV"}],"recommendation":"major_revision","confidential_remarks":"The paper addresses a relevant problem and the idea of using an MA-equipped UAV jammer is interesting. However, the core bound in Eq. (8) is not a valid worst-case upper bound, and this flaw propagates through the entire formulation and the numerical claims. The authors need to re-derive the bound with proper inclusion of array gains and the potential for jamming nulls, or else show numerically that the exact worst-case secrecy rate behaves as claimed. The algorithm also needs to be corrected and the reformulation in (16) justified. Given the letter format, these are substantial revisions, but I believe they are within the scope of a resubmission if the authors can fix the technical issues and re-run the simulations."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Hey,\n\nThe headline: the paper's novel piece is a rotatable (they call it movable) antenna array on a jamming UAV, optimized jointly with trajectory and beamforming for secrecy energy efficiency. That scenario doesn't appear in the prior work they cite, and the system model is assembled carefully enough. But the central numerical result—about 40% SEE gain over fixed antennas—is not supported, because the worst-case Eve rate bound in Eq. (8) is not a valid bound.\n\nHere's the problem. The actual Eve SINR uses |h_{B,e}^H wB|^2 and |h_{J,e}^H wJ|^2, which include array gains. The bound replaces them with path-loss-only terms: it drops the BS array gain (at least 6 dB for 4 antennas, possibly more) and assumes the jammer's interference is at its worst-case path-loss level, ignoring that wJ can null Eve. Both errors make the Eve rate smaller than it can actually be, which inflates the secrecy rate used as the objective. The paper never checks (8) against the exact maximization in (7). There's also an exponent inconsistency: (8) uses free-space distance-squared loss, while the channel model in Table I uses α=3.5 and 2.8 for those links. So the reported gains may be artifacts of the surrogate bound.\n\nOther issues are secondary. Section III-B describes projected gradient descent for the MA angles, but Algorithm 1 says \"two-phase approach.\" The reformulated rate in Eq. (16) appears without derivation. There's no code and no sensitivity analysis; the conclusions rest on a single deterministic scenario. On the plus side, the authors engage with the right literature, and the energy consumption model is standard. If the bound were fixed, this could be a modest but useful extension to UAV-PLS work.\n\nWho is this for? Readers who want to see how antenna orientation could be added to UAV jamming formulations. The system model is worth a look, but don't take the numbers at face value.\n\nI'd send it to peer review because the technical flaw warrants expert scrutiny, but I would not accept it in this form. If the authors can provide a valid bound or directly evaluate the worst-case rate, the paper might become publishable.","headline":"The paper's novel scenario is undercut by an invalid worst-case bound on Eve's rate, so the reported 40% secrecy-energy-efficiency gain is likely an artifact.","tokens_in":9782,"tokens_out":6864,"would_cite":false,"duration_ms":67592,"reading_group":"maybe","serious_thinker":"no","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"UAVs with movable-antenna arrays can raise secrecy energy efficiency by about 40 percent over fixed-antenna jammers, via joint optimization of flight path, antenna orientation, and beamforming.","keywords":["Movable-antenna array","UAV trajectory optimization","Secrecy energy efficiency","Physical layer security","Beamforming","Cooperative jamming","Alternating optimization","Line-of-sight channel"],"falsifier":"Run the same optimization with the LoS channel replaced by a Rician or multipath channel in which the direct component contributes only part of the received power; if the roughly 40% secrecy-energy-efficiency gain over fixed antennas drops toward zero, the central claim's reliance on deterministic LoS array responses is confirmed.","tokens_in":8802,"feed_emoji":"🚁","tokens_out":7130,"duration_ms":70266,"temperature":0.7,"pith_summary":"This paper argues that a UAV carrying a movable-antenna array can act as an energy-efficient friendly jammer, and that the key to making it work is to optimize its flight path, array orientation, and beamforming jointly. In the simulated setup, the proposed joint optimization reaches roughly 40% higher secrecy energy efficiency than a fixed-antenna jammer and roughly 10% higher than a strategy that simply aims the array at the eavesdropper. The practical stake is that physical-layer security can be bought with antenna flexibility instead of extra flight distance or transmit power.","feed_headline":"UAV jammers with movable antennas gain about 40% secrecy efficiency","feed_subtitle":"Jointly steering drone path, antenna tilt, and beams blocks eavesdroppers while saving flight energy.","key_machinery":"The load-bearing object is the movable-antenna array mounted on the UAV's underside, whose orientation is described by rotations about three local axes. Because the aerial-ground channels are modeled as pure line-of-sight, the array response vector is a deterministic function of the rotation angles, which means steering the beam and digging nulls can be done by rotating the panel. Around this object the paper builds an alternating optimization: trajectory updates use successive convex approximation and the Dinkelbach method for the fractional objective, orientation updates use projected gradient ascent with backtracking line search, and beamforming updates use rank-relaxed semidefinite programming with Gaussian randomization to recover the beamforming vector.","core_discovery":"The central claim is that movable antennas give a jamming UAV a spatial degree of freedom that fixed antenna panels do not: by rotating the panel, the UAV can point a strong jamming beam at the eavesdropper while steering a null at the legitimate user, all without changing its position. This lets the UAV follow a shorter, less curved trajectory than a fixed-antenna jammer, which must fly around the user to avoid harming its reception. The paper makes the optimization tractable by bounding the worst-case eavesdropper rate over an uncertainty region, and by solving the resulting fractional, non-convex problem with alternating updates of trajectory, orientation angles, and beamforming vector. The simulation comparison shows the proposed MA-based method outperforming both the fixed-antenna and the Eve-oriented baselines in secrecy energy efficiency.","pith_inferences":["If the LoS assumption is relaxed to a multipath or Rician channel, the reported 40% gain is likely to shrink, but the relative value of MA rotation should persist whenever the direct path dominates.","The approach could naturally extend to multiple users or eavesdroppers by scheduling rotation angles across time slots to shape several beams and nulls over the flight.","A prototype test with measured array responses and real actuation costs would be the cleanest way to tell how much of the gain survives outside the simulation."],"forward_implications":["A jamming UAV can trade flight distance for antenna steering, so secrecy constraints no longer force long detours around the legitimate user.","Adding movable antennas to existing UAV jamming platforms can raise secrecy energy efficiency by about 40% in LoS-dominated settings without requiring more transmit power.","The worst-case bound on the eavesdropper's rate means the gains are promised even when only an approximate eavesdropper position is known.","The same alternating, Dinkelbach-based structure can be reused for other fractional secrecy objectives in UAV-enabled networks."],"supporting_citations":[{"why":"Provides the LoS aerial-ground channel model and the earlier result that a fixed-antenna jamming UAV takes a detour when user and eavesdropper align.","marker":"[4]"},{"why":"Motivates movable-antenna arrays with flexible position and rotation as a source of spatial degrees of freedom.","marker":"[5]"},{"why":"Supplies the MA array model and the background on reconfigurable antenna positions used throughout.","marker":"[6]"},{"why":"Justifies the projected-gradient and backtracking-line-search update for MA orientation angles.","marker":"[9]"},{"why":"Supplies the eavesdropper position-uncertainty model and the first-order Taylor linearization for trajectory optimization.","marker":"[12]"},{"why":"Provides the rotary-wing UAV propulsion power model and the numerical parameters used in simulations.","marker":"[13]"},{"why":"Defines the power consumption and operation-time model for MA rotation.","marker":"[14]"},{"why":"Gives the Dinkelbach algorithm used to solve the fractional secrecy-energy-efficiency objective.","marker":"[15]"},{"why":"Supplies the parallel convex-approximation technique used to form the concave lower bound in beamforming optimization.","marker":"[16]"}],"fun_headline_variants":["Movable antennas on UAVs boost secure link efficiency by 40%","Joint UAV path and antenna orientation cuts eavesdropping energy waste","UAV movable antennas: 40% more secrecy per joule","Rotating antennas let UAVs jam eavesdroppers with 40% less energy","Fly shorter, steer beams: UAV secrecy efficiency up 40%"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The aerial-ground channels are modeled as strictly line-of-sight, so the array response is a deterministic function of the panel orientation; if multipath is present, the deep nulls and beam alignment that generate the reported gains would be degraded.","fun_headline_variants_meta":{"raw":{"variants":["Movable antennas on UAVs boost secure link efficiency by 40%","Joint UAV path and antenna orientation cuts eavesdropping energy waste","UAV movable antennas: 40% more secrecy per joule","Rotating antennas let UAVs jam eavesdroppers with 40% less energy","Fly shorter, steer beams: UAV secrecy efficiency up 40%"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000743,"raw_usage":{"total_tokens":3249,"prompt_tokens":811,"completion_tokens":2438,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":427,"completion_tokens_details":{"reasoning_tokens":2344}},"tokens_in":427,"tokens_out":2438,"duration_ms":18195,"temperature":1.0,"reasoning_tokens":2344,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-15T17:43:04.086480+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Run the same optimization with the LoS channel replaced by a Rician or multipath channel in which the direct component contributes only part of the received power; if the roughly 40% secrecy-energy-efficiency gain over fixed antennas drops toward zero, the central claim's reliance on deterministic LoS array responses is confirmed.","supporting_citations":[{"cited_title":"Securing UA V Communications via Joint Trajectory and Power Control,","cited_arxiv_id":null,"evidence_quote":"Provides the LoS aerial-ground channel model and the earlier result that a fixed-antenna jamming UAV takes a detour when user and eavesdropper align."},{"cited_title":"6DMA Enhanced Wireless Network wit h Flexible Antenna Position and Rotation: Opportunities and Challenges,","cited_arxiv_id":null,"evidence_quote":"Motivates movable-antenna arrays with flexible position and rotation as a source of spatial degrees of freedom."},{"cited_title":"A Tutorial on Movable Antennas for Wi reless Networks,","cited_arxiv_id":null,"evidence_quote":"Supplies the MA array model and the background on reconfigurable antenna positions used throughout."},{"cited_title":"6D Movable Antenna Enhanced Interference Mitigation for Cellular-Connected UA V Communications,","cited_arxiv_id":null,"evidence_quote":"Justifies the projected-gradient and backtracking-line-search update for MA orientation angles."},{"cited_title":"Secure UA V Communication wi th Cooperative Jamming and Trajectory Control,","cited_arxiv_id":null,"evidence_quote":"Supplies the eavesdropper position-uncertainty model and the first-order Taylor linearization for trajectory optimization."},{"cited_title":"Energy Efficient UA V Com- munication with Energy Harvesting,","cited_arxiv_id":null,"evidence_quote":"Provides the rotary-wing UAV propulsion power model and the numerical parameters used in simulations."},{"cited_title":"Fractional Programming. II, On Dinkelba ch’s Algorithm,","cited_arxiv_id":null,"evidence_quote":"Gives the Dinkelbach algorithm used to solve the fractional secrecy-energy-efficiency objective."},{"cited_title":"Parallel and Distributed Methods for Constrained Nonconvex Optimization-Part II: Applications in Communic ations and Machine Learning,","cited_arxiv_id":null,"evidence_quote":"Supplies the parallel convex-approximation technique used to form the concave lower bound in beamforming optimization."}],"review_version":2}