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REVIEW 3 major objections 5 minor 49 references

On secure UAV-aided ISCC systems

T0 review · 3 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read This paper argues that a single drone can simultaneously provide edge computing, radar sensing, and secure jamming, and that jointly optimizing offloading ratio, scheduling, beamforming, and trajectory minimizes total ground-user energy.

desk verdict A well-motivated and cleanly presented secure UAV-aided ISCC optimization paper whose central trajectory convexity claim is false, so the proposed algorithm's guarantee does not hold as written. read the letter →

arxiv 2506.13137 v2 pith:WTAX7AXP submitted 2025-06-16 cs.IT eess.SPmath.IT

Hongjiang Lei , Congke Jiang , Ki-Hong Park , Mohamed A. Aboulhassan , Sen Zhou , Gaofeng Pan This is my paper
classification cs.ITeess.SPmath.IT
keywords integratedsensingcommunicationandcomputingISCCphysical-layersecurityUAVtrajectoryoptimizationmobileedgeoffloadingratiobeamformingblockcoordinatedescent
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 argues that a single drone can serve as an aerial base station, a mobile edge-computing host, and a radar jammer at the same time, and that these roles should be planned jointly rather than separately. Its central claim is that by jointly optimizing the fraction of each user's task that is offloaded, which users are served in each time slot, the radar beamforming weights, and the drone's flight trajectory, the total energy consumed by the ground users is minimized while secure offloading, sensing, and drone-energy constraints are respected. Because the variables are tightly coupled and the problem is non-convex, the paper decomposes it into four subproblems and solves them alternately, using successive convex approximation to turn the non-convex parts into convex ones. Numerical results show the iterative algorithm converging in about ten iterations and producing lower user energy consumption than three fixed-strategy baselines. The contribution, if it holds, is a design principle for secure aerial computing: trajectory, scheduling, offloading, and jamming are one coupled resource-allocation problem.

What carries the argument

Three named mechanisms carry the argument. Block coordinate descent (BCD) cycles through four subproblems so each variable block is updated while the others are frozen, which makes the objective non-increasing at each step. Successive convex approximation (SCA) replaces non-convex constraints with first-order Taylor expansions around the current iterate; this is applied to the offloading-rate expression, the inverse-distance terms in the secure-rate and sensing constraints, and the propulsion-power function, producing convex subproblems P1.1, P2.1, P3.2, and P4.2. The third mechanism is the sensing beampattern gain $P[n] = a_{\mathrm{T}}^H[n]\,W[n]\,a_{\mathrm{T}}[n]$, a quadratic form of the beamforming matrix that appears in both the sensing-threshold constraint and the eavesdropper-suppression constraint, thereby coupling beamforming and trajectory design.

What would settle it

Compute the Hessian of $f(x,y)=1/((x-x_k)^2+(y-y_k)^2+H^2)$ at a point horizontally offset from the user, say $(x,y)=(1,0)$ with $H=1$; its eigenvalues are $0.5$ and $-0.5$, which is indefinite, showing the claimed convexity fails. The same second-order test applied directly to constraint (39) of the paper settles whether the trajectory subproblem is convex as claimed.

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Extended reading notes

Core claim

The paper's own claim is that the non-convex central problem, which minimizes total user energy consumption under secure-communication, sensing, and drone-energy constraints, can be solved near-optimally by alternating among four convex subproblems. The offloading proportion is a linear program; user scheduling is a linear program after relaxing binary variables; beamforming is a semidefinite program after dropping the rank-one constraint and recovering it later; and the trajectory subproblem is built from successive convex approximations of rate expressions, inverse-distance terms, and the rotary-wing propulsion model. The paper asserts the resulting objective sequence is non-increasing and bounded below, so the alternating procedure converges, and the simulations display optimized trajectories that stay close to the users while keeping distance from the estimated eavesdropper region. The sensing beam plays a dual role: it provides the radar return for locating the eavesdropper and it suppresses the eavesdropper's signal-to-interference-plus-noise ratio, which is why the secure offloading rate can stay above its threshold.

Load-bearing premise

The load-bearing premise is that the inverse-squared-distance functions used in the secure-rate and sensing constraints are convex functions of the drone's horizontal position, so that the first-order approximations in the trajectory subproblem are valid; the function $1/(\|q_s-q_k\|^2+H^2)$ is not actually convex, so the trajectory subproblem's convexity is not established.

Editorial extensions

If this is right

  • Users located close to the eavesdropper can still offload the bulk of their computation, because the radar beam suppresses the eavesdropper's rate instead of forcing local computation.
  • The optimized trajectory will approach users during their scheduled slots and keep distance from the estimated eavesdropper region, so flight path becomes a security resource as well as a communication resource.
  • Because the alternating algorithm converges in a handful of iterations, the joint design is computationally cheap enough to re-run as user positions or threat estimates change.
  • User energy consumption increases with user transmit power and with the number of users, but is nearly insensitive to the size of the eavesdropper-location uncertainty, suggesting the design is robust to imperfect localization.

Reading between the lines

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

  • The claimed convexity of the trajectory subproblem should be checked before relying on the numerical gains; a corrected surrogate for the inverse-distance terms, such as a second-order-cone reformulation, would put the successive-convex-approximation step on firmer ground.
  • The same joint-design logic suggests a testable extension to multiple eavesdroppers or to three-dimensional trajectories: the jamming-versus-service trade-off should become sharper because the drone has more spatial degrees of freedom.
  • A natural empirical check is to compare the proposed scheme against a greedy scheduling policy in a hardware-in-the-loop testbed; the predicted energy gap should grow as users move closer to the eavesdropper.
  • The result also implies that sensing quality and communication secrecy are not competing objectives in this setting: the radar signal is itself the jamming signal, so better sensing and better secrecy improve together.
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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

3 major / 5 minor

Summary. The paper studies a secure UAV-aided integrated sensing, communication, and computing (ISCC) system. A UAV equipped with a uniform planar array serves as an aerial edge server, transmits radar sensing signals to localize and jam a potential eavesdropper with uncertain location, and receives partial offloading from ground users. The total user energy consumption is minimized by jointly optimizing the offloading ratio, user scheduling, transmit beamforming, and UAV trajectory, subject to secrecy, sensing, propulsion-energy, and computational-delay constraints. The authors propose a block-coordinate-descent algorithm with successive convex approximation, decomposing the problem into four subproblems: an offloading-ratio LP, a user-scheduling LP, a beamforming SDP, and a trajectory SCA problem. Numerical results over three geometric scenarios show convergence and lower energy consumption than three benchmark schemes.

Significance. If the theoretical guarantees were valid, the paper would be a useful combination of ISCC, UAV trajectory design, and physical-layer security, with a tractable decomposition into convex subproblems. The system model is reasonably rich, including an uncertain eavesdropper region, rotary-wing propulsion energy, and partial computation offloading, and the numerical study covers three distinct user/eavesdropper geometries. However, the paper does not provide reproducible code or machine-checked proofs, and the convergence and optimality guarantees rest on a small number of hand-verified convexity claims. Those claims are incorrect in a load-bearing way: the trajectory subproblem P4.2 is not convex as stated, and the stochastic approximation in Eq. (12) is not a valid expectation approximation. The numerical results may well be reproducible, but they do not by themselves establish the algorithmic guarantees claimed in Sections III-D and III-E. The paper also does not discuss how the relaxed scheduling variables in P2.1 are mapped back to a feasible binary schedule.

major comments (3)
  1. [Section III-D, Eqs. (39)-(41)] The assertion above Eqs. (39)-(40) that d_sk^{-2}[n] and Z1[n]d_se^{-4}[n] are convex functions of the UAV position q_s[n] is false. For f(q)=1/(||q-q_k||^2+H^2), the Hessian is -2I/x^2 + 8(q-q_k)(q-q_k)^T/x^3 with x=||q-q_k||^2+H^2, so its eigenvalue in any direction orthogonal to q-q_k is -2/x^2<0; the function is not convex over its domain. The same indefiniteness holds for Z1/(||q-q_e||^2+H^2)^2. Consequently constraint (40) is not convex, and the first-order Taylor replacement in (41) is not a valid convex inner approximation of (39): for a nonconvex function the tangent is not a global underestimator, so the surrogate may admit points that violate the original constraint. Therefore P4.2 is not a convex problem as claimed, and the convergence step (53) in Section III-E, which depends on the convexity of P4.2 and on the validity of the SCA surrogates, is not established.
  2. [Section II-A, Eq. (12)] Equation (12) approximates the random SINR gamma_ek[n] by replacing only the numerator |hek|^2 with its expectation, while the denominator still contains the random variables |hei[n]|^2. This is not an approximation of E[gamma_ek[n]] in general (E[X/Y] is not E[X]/E[Y]), and, more importantly, the resulting expression remains random. Constraint (25i), which uses this expression, is therefore stochastic as written rather than a deterministic worst-case constraint over the eavesdropper location. Since (25i) is inherited by every subproblem that enforces secrecy, this issue affects the feasibility of P0 and the validity of the reported numerical feasibility checks. A consistent treatment would approximate the whole ratio, e.g., by replacing all random channel powers by their means or by using an outage or Jensen-based bound.
  3. [Section III-B, P2.1] The claim that P2.1 is a linear program after relaxing theta_k[n] to [0,1] is inconsistent with the problem as written. The constraints (25h) and (25i) are ratios of affine functions of theta, with theta_r[n]=sum_k theta_k[n] appearing in the denominators through P_ses[n] and hse[n]^H W[n] hse[n]; the objective also includes terms theta_k[n] E_offload_k[n] whose rate Rhat_sk[n] depends on theta_r[n]. These are nonlinear in the relaxed variable. Furthermore, the paper does not describe a rounding or penalty step that recovers a binary schedule from the continuous solution, so the monotonicity argument in (51) for Step 2 is not justified. This is a load-bearing issue because Step 2 is one of the four blocks on which the BCD convergence proof rests.
minor comments (5)
  1. [Abstract and throughout] The text alternates between 'uncrewed' and 'unmanned' aerial vehicle, and the symbol 'UAV' is frequently typeset as 'UA V'; these should be made consistent.
  2. [Section II-B, Eq. (22)] Constraint (22) multiplies by theta_k[n] a quantity T_offload_k[n] that already contains theta_k[n] by definition (18), producing a quadratic term under the relaxed scheduling variable. For binary theta this is harmless, but it is inconsistent with the LP claim for P2.1 and should be clarified.
  3. [Section III-E, Algorithm 1] The convergence condition in Algorithm 1 writes E(A^(m),...)-E(A^(m-1),...) ≻ epsilon; this should be a scalar inequality, and the loop should specify whether the absolute value or the difference itself is tested.
  4. [Table III] The rotor solidity entry 's 0.05 m3' appears to have an incorrect unit; solidity is dimensionless.
  5. [Footnote 1] The footnote describing the approximation of random power gains by their expectation is grammatically incomplete and does not state whether Eq. (12) approximates the expectation of the ratio or the ratio of expectations.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the optimization pipeline is self-contained and validated against independent benchmarks; the flagged convexity issue is a correctness concern, not a circularity.

full rationale

The paper's central claim is that an iterative BCD/SCA algorithm minimizes total user energy consumption under communication, sensing, and computing constraints. The derivation chain is a standard decomposition: P0 is split into LP, SDP, and trajectory subproblems, each solved with slack variables and Taylor surrogates, then compared against three benchmark schemes. No fitted parameter is renamed as a prediction, and no quantity is defined in terms of the target result. The self-citations ([31], [32], [33], [41], [42], [49]) are used only for modeling conventions (partial offloading, time-slot discretization, LoS approximation via expectation, rank-one recovery) and are not load-bearing for the claimed reduction of energy consumption. These are independent, standard modeling assumptions, not a self-citation chain that forces the paper's numerical conclusions. The potentially serious weakness is the assertion in Section III-D that d_sk^{-2}[n] and Z1[n] d_se^{-4}[n] are convex in q_s[n]; as stated, 1/(||q-q_k||^2+H^2) and 1/(||q-q_e||^2+H^2)^2 have indefinite Hessians over their domains, so the convexity of P4.2 and the convergence proof in Section III-E are not established. However, this is a mathematical correctness risk, not circularity: the claim does not reduce to the paper's inputs, and it is not an instance of fitting, renaming, or self-citation. The numerical results may still be reproducible, and the algorithm is benchmarked externally rather than guaranteed by construction. Therefore, the circularity score is 0.

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

No new physical entities are introduced. The system consists of standard components (UAV, ground users, eavesdropper, computing server) from prior literature. The optimization variables are not free fitted parameters but decision variables. The key assumptions are the LoS channel, the wiretap SINR approximation, and the (incorrect) convexity assertion for distance functions.

assumptions (3)
  • domain assumption The ground-to-air channel between the UAV and users/eavesdropper is modeled as a line-of-sight link.
    Invoked in Section II-A following Refs. [36]-[38]. The paper notes that multipath cases would make the problem stochastic, and it argues the LoS model serves as a benchmark, but the central rates and constraints are built on this assumption.
  • domain assumption The wiretap SINR in Eq. (12) is approximated by replacing the random numerator |hek|^2 with its expectation while retaining random variables in the denominator.
    This approximation, attributed to Refs. [40]-[42], is not derived and is generally invalid for ratios of random variables; it is used to define the worst-case eavesdropping rate constraint (25i).
  • ad hoc to paper The distance functions d_sk^{-2} and Z1 d_se^{-4} are convex in the UAV position.
    Stated in Section III-D to justify the SCA transformations (39)-(41). This assertion is false for 1/(||q_s-q_k||^2+H^2), so P4.2 may not be convex.

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Pith. "Pith review of On secure UAV-aided ISCC systems." pith.science (2026). https://pith.science/paper/WTAX7AXP

@misc{pith2026250613137,
  author       = {Pith},
  title        = {Pith review of: On secure UAV-aided ISCC systems},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WTAX7AXP}},
  note         = {Machine review of arXiv:2506.13137}
}
read the original abstract

Integrated communication and sensing, which can make full use of the limited spectrum resources to perform communication and sensing tasks simultaneously, is an up-and-coming technology in wireless communication networks. In this work, we investigate the secrecy performance of an uncrewed aerial vehicle (UAV)-assisted secure integrated communication, sensing, and computing system, where the UAV sends radar signals to locate and disrupt potential eavesdroppers while providing offload services to ground users (GUs). Considering the constraints of UAV maximum speed, transmit power, and propulsion energy, as well as secure offloading, data transmission, and computation time, the total energy consumption of GUs is minimized by jointly optimizing user offloading ratio, user scheduling strategy, transmit beamforming, and UAV trajectory. An efficient iterative optimization algorithm is proposed to solve the non-convex optimization problem caused by tightly coupled dependent variables. In particular, the original optimization problem is decomposed into four sub-optimization problems, and the non-convex sub-problems are transformed into approximately convex forms via successive convex approximation. Then, all sub-problems are solved successively by using the block coordinate descent technique. Numerical results demonstrate the convergence and validate the effectiveness of the proposed algorithm.

Figures

Figures reproduced from arXiv: 2506.13137 by the authors.

Figure 1
Figure 1. System model. practicability. Similarly, solving optimization problems is also more challenging. C. Organization The rest of this paper is organized as follows. The system model and problem formulation are provided in Section II. Section III presents the joint proportion of user offloading, user scheduling, transmit beamforming, and UAV trajectory optimization algorithm. Simulation results are demonstrated in Sectio… view at source ↗
Figure 2
Figure 2. The user energy consumption versus the number of iterations with varying (a) [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗
Figure 3
Figure 3. Scenario 1 wherein K = 4 users are distributed on one side of E. (a) The optimal trajectory of S. (b) The offloading ratio of users. (c) The energy consumption of users. 2) Benchmark 2: The user offloading ratio and user scheduling are fixed and the transmit beamforming and S trajectory are optimized, similar to [50]. 3) Benchmark 3: There is no radar signal to interfere with E and the user offloading ratio, user sc… view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Scenario 2 wherein K = 4 users are in a trapezoidal distribution and E is located near the center. (a) The optimal trajectory of S. (b) The offloading ratio of users. (c) The energy consumption of users. 0 20 40 60 80 100 120 140 160 180 200 0 50 100 150 200 250 (a) 1 …
Figure 5
Figure 5. Figure 5: Scenario 3 wherein K = 4 users are in a rectangle and E is located near a corner. (a) The optimal trajectory of S. (b) The offloading ratio of users. (c) The energy consumption of users. transmission powers in different scenarios, respectively. In scenario 1, as shown …
Figure 6
Figure 6. Figure 6: The user scheduling of the proposed. (a) Scenario 1. (b) Scenario 2. (c) Scenario 3. [PITH_FULL_IMAGE:figures/full_fig_p011_6.png]
Figure 7
Figure 7. Figure 7: Rsk and Rek (a) Scenario 1. (b) Scenario 2. (c) Scenario 3. [10] X. Pang, S. Guo, J. Tang, N. Zhao, and N. Al-Dhahir, “Dynamic ISAC beamforming design for UAV-enabled vehicular networks,” IEEE Trans. Wireless Commun., vol. 23, no. 11, pp. 16852-16864, Nov. 2024. [11] X…

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Works this paper leans on

49 extracted references · 46 canonical work pages

  1. [1]

    Integrated sensing and communications: Toward dual-functional wireless networks for 6G and beyond,

    F. Liu, Y . Cui, C. Masouros, J. Xu, T. X. Han, Y . C. Eldar, and S. Buzzi, “Integrated sensing and communications: Toward dual-functional wireless networks for 6G and beyond,” IEEE J. Sel. Areas Commun. , vol. 40, no. 6, pp. 1728-1767, Jun. 2022

    work page 2022

  2. [2]

    Network- level analysis of integrated sensing and communication using stochastic geometry,

    R. Wang, B. E. Y . Belmekki, X. Zhang, and M.-S. Alouini, “Network- level analysis of integrated sensing and communication using stochastic geometry,” IEEE Internet of Things Mag. , vol. 7, no. 4, pp. 84-90, Jun. 2024

    work page 2024

  3. [3]

    Deep cooperation in ISAC system: Resource, node and infrastructure perspectives,

    Z. Wei, H. Liu, Z. Feng, H. Wu, F. Liu, Q. Zhang, and Y . Du, “Deep cooperation in ISAC system: Resource, node and infrastructure perspectives,” IEEE Internet of Things Mag. , vol. 7, no. 6, pp. 118-125, Nov. 2024

    work page 2024

  4. [4]

    Integrated sensing and communications: Recent advances and ten open challenges,

    S. Lu, F. Liu, Y . Li, K. Zhang, H. Huang, J. Zou, X. Li, Y . Dong, F. Dong, J. Zhu, Y . Xiong, W. Yuan, Y . Cui, and L. Hanzo, “Integrated sensing and communications: Recent advances and ten open challenges,” IEEE Internet Things J. , vol. 11, no. 11, pp. 19094-19120, Jun. 2024

    work page 2024

  5. [5]

    UA V meets integrated sensing and communication: Challenges and future directions,

    J. Mu, R. Zhang, Y . Cui, N. Gao, and X. Jing, “UA V meets integrated sensing and communication: Challenges and future directions,” IEEE Commun. Mag., vol. 61, no. 5, pp. 62-67, May 2023

    work page 2023

  6. [6]

    UA V-enabled integrated sensing and communication: Opportunities and challenges,

    K. Meng, Q. Wu, J. Xu, W. Chen, Z. Feng, R. Schober, and A. L. Swindlehurst, “UA V-enabled integrated sensing and communication: Opportunities and challenges,” IEEE Wireless Commun. , vol. 31, no. 2, pp. 97-104, Apr. 2024

    work page 2024

  7. [7]

    Beamforming design and trajectory optimization for UA V-empowered adaptable integrated sensing and com- munication,

    C. Deng, X. Fang, and X. Wang, “Beamforming design and trajectory optimization for UA V-empowered adaptable integrated sensing and com- munication,” IEEE Trans. Wireless Commun. , vol. 22, no. 11, pp. 8512- 8526, Nov. 2023

    work page 2023

  8. [8]

    Precoding and trajectory design for UA V-assisted integrated communication and sensing systems,

    R. Chai, X. Cui, R. Sun, D. Zhao, and Q. Chen, “Precoding and trajectory design for UA V-assisted integrated communication and sensing systems,” IEEE Trans. Veh. Technol., vol. 73, no. 9, pp. 13151-13163, Sep. 2024

    work page 2024

Show all 49 references
  1. [9]

    Throughput maximization for UA V-enabled integrated periodic sensing and commu- nication,

    K. Meng, Q. Wu, S. Ma, W. Chen, K. Wang, and J. Li, “Throughput maximization for UA V-enabled integrated periodic sensing and commu- nication,” IEEE Trans. Wireless Commun. , vol. 22, no. 1, pp. 671-687, Jan. 2023. 12 0 5 10 15 20 25 30 35 40 0 1 2 3 4 5 6Rate (bit/s/Hz) (a) 0...

    2023

  2. [10]

    Dynamic ISAC beamforming design for UA V-enabled vehicular networks,

    X. Pang, S. Guo, J. Tang, N. Zhao, and N. Al-Dhahir, “Dynamic ISAC beamforming design for UA V-enabled vehicular networks,” IEEE Trans. Wireless Commun., vol. 23, no. 11, pp. 16852-16864, Nov. 2024

    2024

  3. [11]

    Fair integrated sensing and communication for multi-UA V enabled internet of things: Joint 3D trajectory and resource optimization,

    X. Liu, Y . Liu, Z. Liu, and T. S. Durrani, “Fair integrated sensing and communication for multi-UA V enabled internet of things: Joint 3D trajectory and resource optimization,” IEEE Internet Things J. , vol. 11, no. 18, pp. 29546-29556, Oct. 2024

    2024

  4. [12]

    A joint UA V trajectory, user association, and beamforming design strategy for multi-UA V-assisted ISAC systems,

    R. Zhang, Y . Zhang, R. Tang, H. Zhao, Q. Xiao, and C. Wang, “A joint UA V trajectory, user association, and beamforming design strategy for multi-UA V-assisted ISAC systems,”IEEE Internet Things J. , vol. 11, no. 18, pp. 29360-29374, Sep. 2024

    2024

  5. [13]

    Integration of sensing, communication, and computing for metaverse: A survey,

    X. Wang, Q. Guo, Z. Ning, L. Guo, G. Wang, X. Gao, and Y . Zhang, “Integration of sensing, communication, and computing for metaverse: A survey,” Acm Comput. Surv. , vol. 56, no. 10, pp. 1-38, May 2024

    2024

  6. [14]

    Toward intelligent and efficient 6G networks: JCSC enabled on-purpose machine communications,

    P. Zhang, H. Yang, Z. Feng, Y . Cui, J. Dai, X. Qin, J. Li, and Q. Zhang, “Toward intelligent and efficient 6G networks: JCSC enabled on-purpose machine communications,” IEEE Wireless Commun. , vol. 30, no. 1, pp. 150-157, Jul. 2023

    2023

  7. [15]

    Reconfigurable intelligent surface for sensing, communication, and computation: Perspectives, challenges, and opportunities,

    B. Li, W. Xie, and Z. Fei, “Reconfigurable intelligent surface for sensing, communication, and computation: Perspectives, challenges, and opportunities,” IEEE Internet of Things Mag. , vol. 7, no. 4, pp. 36-42, Jun. 2024

    2024

  8. [16]

    A survey on integrated sensing, communication, and computation,

    D. Wen, Y . Zhou, X. Li, Y . Shi, K. Huang, and K. B. Letaief, “A survey on integrated sensing, communication, and computation,” IEEE Commun. Surveys Tuts., doi: 10.1109/comst.2024.3521498, Dec. 2024

    2024

  9. [17]

    Energy-efficient integrated sensing and communication: A multi-access edge computing design,

    N. Huang, C. Dou, Y . Wu, L. Qian, and R. Lu, “Energy-efficient integrated sensing and communication: A multi-access edge computing design,” IEEE Wireless Commun. Lett. , vol. 12, no. 12, pp. 2053-2057, Dec. 2023

    2023

  10. [18]

    NOMA-aided joint communication, sensing, and multi-tier computing systems,

    Z. Wang, X. Mu, Y . Liu, X. Xu, and P. Zhang, “NOMA-aided joint communication, sensing, and multi-tier computing systems,” IEEE J. Sel. Areas Commun., vol. 41, no. 3, pp. 574-588, Mar. 2023

    2023

  11. [19]

    Resource management for integrated communications, computing, and sensing (ICCS) networks,

    G. Sun, X. Wu, W. Hao, Z. Zhu, J. Li, Z. Chu, and P. Xiao, “Resource management for integrated communications, computing, and sensing (ICCS) networks,” IEEE Trans. Veh. Technol., vol. 73, no. 12, pp. 18719- 18731, Dec. 2024

    2024

  12. [20]

    Joint MIMO precoding and computation resource allocation for dual-function radar and communication systems with mobile edge computing,

    C. Ding, J.-B. Wang, H. Zhang, M. Lin, and G. Y . Li, “Joint MIMO precoding and computation resource allocation for dual-function radar and communication systems with mobile edge computing,” IEEE J. Sel. Areas Commun., vol. 40, no. 7, pp. 2085-2102, Jul. 2022

    2022

  13. [21]

    UA V-enabled integrated sensing, computing, and communication: A fundamental trade-off,

    Y . Xu, T. Zhang, Y . Liu, and D. Yang, “UA V-enabled integrated sensing, computing, and communication: A fundamental trade-off,” IEEE Wireless Commun. Lett., vol. 12, no. 5, pp. 843-847, May 2023

    2023

  14. [22]

    Joint precoding design and location optimization in joint communication, sensing and computing of UA V systems,

    Y . Liu, B. Zhang, D. Guo, H. Wang, and G. Ding, “Joint precoding design and location optimization in joint communication, sensing and computing of UA V systems,” IEEE Trans. Cogn. Commun. Netw. , vol. 10, no. 2, pp. 541-552, Apr. 2024

    2024

  15. [23]

    Trajectory design and resource allocation for multi-UA V-assisted sensing, communication, and edge computing integration,

    S. Peng, B. Li, L. Liu, Z. Fei, and D. Niyato, “Trajectory design and resource allocation for multi-UA V-assisted sensing, communication, and edge computing integration,” IEEE Trans. Commun. , vol. 73, no. 4, pp. 2847-2861, Apr. 2025

    2025

  16. [24]

    UA V-enabled integrated sensing, computing, and communication for internet of things: Joint resource allocation and trajectory design,

    Y . Zhou, X. Liu, X. Zhai, Q. Zhu, and T. S. Durrani, “UA V-enabled integrated sensing, computing, and communication for internet of things: Joint resource allocation and trajectory design,” IEEE Internet Things J. , vol. 11, no. 7, pp. 12717-12727, Apr. 2024

    2024

  17. [25]

    Edge information hub: Orchestrating satellites, UA Vs, MEC, sensing and communications for 6G closed-loop controls,

    C. Lei, W. Feng, P. Wei, Y . Chen, N. Ge, and S. Mao, “Edge information hub: Orchestrating satellites, UA Vs, MEC, sensing and communications for 6G closed-loop controls,” IEEE J. Sel. Areas Commun. , vol. 43, no. 1, pp. 5-20, Jan. 2025

    2025

  18. [26]

    Toward multi- functional 6G wireless networks: Integrating sensing, communication, and security,

    Z. Wei, F. Liu, C. Masouros, N. Su, and A. P. Petropulu, “Toward multi- functional 6G wireless networks: Integrating sensing, communication, and security,” IEEE Commun. Mag. , vol. 60, no. 4, pp. 65-71, Apr. 2022

    2022

  19. [27]

    Secure rate maximization for ISAC-UA V assisted communication amidst multiple eavesdroppers,

    Y . Liu, X. Liu, Z. Liu, Y . Yu, M. Jia, Z. Na, and T. S. Durrani, “Secure rate maximization for ISAC-UA V assisted communication amidst multiple eavesdroppers,” IEEE Trans. Veh. Technol. , vol. 73, no. 10, pp. 15843- 15847, Oct. 2024

    2024

  20. [28]

    Secure transmission for IRS-aided UA V-ISAC networks,

    J. Zhang, J. Xu, W. Lu, N. Zhao, X. Wang, and D. Niyato, “Secure transmission for IRS-aided UA V-ISAC networks,” IEEE Trans. Wireless Commun., vol. 23, no. 9, pp. 12256-12269, Sep. 2024

    2024

  21. [29]

    When UA Vs meet ISAC: Real-time trajectory design for secure communications,

    J. Wu, W. Yuan, and L. Hanzo, “When UA Vs meet ISAC: Real-time trajectory design for secure communications,” IEEE Trans. Veh. Technol., vol. 72, no. 12, pp. 16766-16771, Dec. 2023

    2023

  22. [30]

    Integrated sensing, navigation, and communication for secure UA V networks with a mobile eavesdropper,

    Z. Wei, F. Liu, C. Liu, Z. Yang, D. W. K. Ng, and R. Schober, “Integrated sensing, navigation, and communication for secure UA V networks with a mobile eavesdropper,” IEEE Trans. Wireless Commun. , vol. 23, no. 7, pp. 7060-7078, Jul. 2024

    2024

  23. [31]

    Secure offloading in NOMA-aided aerial MEC systems based on deep reinforcement learning,

    H. Lei, M. Yang, J. Jiang, K.-H. Park, and G. Pan, “Secure offloading in NOMA-aided aerial MEC systems based on deep reinforcement learning,” IEEE J. Miniat. Air Space Syst. , vol. 6, no. 2, pp. 113-124, Jun. 2025

    2025

  24. [32]

    3D trajectory design for energy- constrained aerial CRNs under probabilistic LoS channel,

    H. Lei, X. Wu, K.-H. Park, and G. Pan, “3D trajectory design for energy- constrained aerial CRNs under probabilistic LoS channel,” IEEE Trans. Cogn. Commun. Netw., doi: 10.1109/TCCN.2024.3472298, 2024

    2024

  25. [33]

    Multi- UA V trajectory design for fair and secure communication,

    H. Lei, D. Meng, H. Ran, K.-H. Park, G. Pan, and M.-S. Alouini, “Multi- UA V trajectory design for fair and secure communication,” IEEE Trans. Cogn. Commun. Netw., doi: 10.1109/TCCN.2024.3487142, 2024

    2024

  26. [34]

    Secure intelligent reflecting surface-aided integrated sensing and communica- tion,

    M. Hua, Q. Wu, W. Chen, O. A. Dobre, and A. L. Swindlehurst, “Secure intelligent reflecting surface-aided integrated sensing and communica- tion,” IEEE Trans. Wireless Commun. , vol. 23, no. 1, pp. 575-591, Jan. 2024

    2024

  27. [35]

    Securing multi-user uplink communications against mobile aerial eavesdropper via sensing,

    P. Liu, Z. Fei, X. Wang, J. A. Zhang, Z. Zheng, and Q. Zhang, “Securing multi-user uplink communications against mobile aerial eavesdropper via sensing,” IEEE Trans. Veh. Technol., vol. 72, no. 7, pp. 9608-9613, Jul. 2023

    2023

  28. [36]

    Joint maneuver and beamforming design for UA V-enabled integrated sensing and communication,

    Z. Lyu, G. Zhu, and J. Xu, “Joint maneuver and beamforming design for UA V-enabled integrated sensing and communication,” IEEE Trans. Wireless Commun., vol. 22, no. 4, pp. 2424-2440, Apr. 2023

    2023

  29. [37]

    Sensing-assisted secure uplink communications with full-duplex base station,

    X. Wang, Z. Fei, J. A. Zhang, and J. Huang, “Sensing-assisted secure uplink communications with full-duplex base station,” IEEE Commun. Lett., vol. 26, no. 2, pp. 249-253, Feb. 2022

    2022

  30. [38]

    Joint trajectory and beamforming optimization for UA V-relayed inte- grated sensing and communication with mobile edge computing,

    S. Xu, Z. Liu, L. Zhao, Z. Liu, X. Wang, Z. Fei, and A. Nallanathan, “Joint trajectory and beamforming optimization for UA V-relayed inte- grated sensing and communication with mobile edge computing,” IEEE Trans. Mobile Comput., doi: 10.1109/tmc.2025.3573702, pp. 1-13, 2025

  31. [40]

    3D UA V trajectory and communication design for simultaneous uplink and downlink trans- mission,

    M. Hua, L. Yang, Q. Wu, and A. L. Swindlehurst, “3D UA V trajectory and communication design for simultaneous uplink and downlink trans- mission,” IEEE Trans. Commun. , vol. 68, no. 9, pp. 5908-5923, Sep. 2020. 13

    2020

  32. [41]

    Trajectory and power design for aerial CRNs with colluding eavesdroppers,

    H. Lei, J. Jiang, H. Yang, K.-H. Park, I. S. Ansari, G. Pan, and M.-S. Alouini, “Trajectory and power design for aerial CRNs with colluding eavesdroppers,” IEEE Trans. Veh. Technol. , vol. 73, no. 12, pp. 18824- 18833, Dec. 2024

    2024

  33. [42]

    Beamforming for secure RSMA-aided ISAC systems,

    Q. Dan, H. Lei, K.-H. Park, and G. Pan, “Beamforming for secure RSMA-aided ISAC systems,” arXiv:2502.18118, Feb. 2025, [Online]: https://arxiv.org/abs/2502.18118

    2025 arXiv

  34. [43]

    Deep reinforcement learning for online computation offloading in wireless powered mobile-edge computing networks,

    L. Huang, S. Bi, and Y .-J. A. Zhang, “Deep reinforcement learning for online computation offloading in wireless powered mobile-edge computing networks,” IEEE Trans. Mobile Comput. , vol. 19, no. 11, pp. 2581-2593, Nov. 2020

    2020

  35. [44]

    UA V trajectory and beamforming optimization for integrated periodic sensing and communication,

    K. Meng, Q. Wu, S. Ma, W. Chen, and T. Q. S. Quek, “UA V trajectory and beamforming optimization for integrated periodic sensing and communication,” IEEE Wireless Commun. Lett. , vol. 11, no. 6, pp. 1211-1215, Jun. 2022

    2022

  36. [45]

    On probing signal design for MIMO radar,

    P. Stoica, L. Jian, and X. Yao, “On probing signal design for MIMO radar,” IEEE Trans. Signal Process. , vol. 55, no. 8, pp. 4151-4161, Aug. 2007

    2007

  37. [46]

    Beamforming and computing capacity allocation for ISAC-assisted secure mobile edge computing,

    C. Chen, J. Yao, M. Jin, and Q. Guo, “Beamforming and computing capacity allocation for ISAC-assisted secure mobile edge computing,” IEEE Wireless Commun. Lett., vol. 13, no. 12, pp. 3360-3364, Sep. 2024

    2024

  38. [47]

    Computation rate maximization for wireless powered mobile-edge computing with binary computation offloading,

    S. Bi and Y . J. Zhang, “Computation rate maximization for wireless powered mobile-edge computing with binary computation offloading,” IEEE Trans. Wireless Commun., vol. 17, no. 6, pp. 4177-4190, Jun. 2018

    2018

  39. [48]

    Energy minimization for wireless communication with rotary-wing UA V ,

    Y . Zeng, J. Xu, and R. Zhang, “Energy minimization for wireless communication with rotary-wing UA V ,”IEEE Trans. Wireless Commun., vol. 18, no. 4, pp. 2329-2345, Apr. 2019

    2019

  40. [49]

    Beam- forming design for joint target sensing and proactive eavesdropping,

    Q. Dan, H. Lei, K.-H. Park, G. Pan, and M.-S. Alouini, “Beam- forming design for joint target sensing and proactive eavesdropping,” arXiv:2407.06521, 2025, [Online]: https://arxiv.org/abs/2407.06521

    2025

  41. [50]

    Joint task offloading and resource allocation in UA V-enabled mobile edge computing,

    Z. Yu, Y . Gong, S. Gong, and Y . Guo, “Joint task offloading and resource allocation in UA V-enabled mobile edge computing,”IEEE Internet Things J., vol. 7, no. 4, pp. 3147-3159, Apr. 2020

    2020

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.