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DNN Task Assignment in UAV Networks: A Generative AI Enhanced Multi-Agent Reinforcement Learning Approach

T0 review · 4 major / 4 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read The paper claims that replacing the MADDPG actor with a diffusion denoising network, coupled with a task-aware greedy path planner, improves DNN task assignment in UAV swarms.

desk verdict The paper's proposed GDM-MADDPG is not actually RL as written: the actor update uses only the DDPM denoising loss, so the reward signal never shapes the policy. read the letter →

arxiv 2411.08299 v3 pith:PRKV3T2X submitted 2024-11-13 cs.AI

Xin Tang , Qian Chen , Wenjie Weng , Binhan Liao , Jiacheng Wang , Xianbin Cao , Xiaohuan Li This is my paper · ORCID
classification cs.AI
keywords DNNtaskassignmentUAVswarmgenerativediffusionmodelmulti-agentreinforcementlearningMADDPGpathplanningAgeofInformationoffloading
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 proposes a two-stage scheme for assigning deep neural network inference tasks among a swarm of UAVs without ground-station support. The first stage plans the swarm's flight path using a randomized greedy algorithm whose fitness function balances flight distance against the time needed to process task data at each target. The second stage assigns DNN layers to UAVs with GDM-MADDPG, a multi-agent reinforcement learning algorithm in which the actor network is replaced by the reverse denoising process of a generative diffusion model conditioned on each agent's observations. The paper claims this design lowers total path cost by roughly 19.3% to 27.1% relative to a plain greedy algorithm as the number of target coordinates grows from 10 to 50, and that GDM-MADDPG converges to a total reward near 5000, outperforming MADDPG baselines on Age of Information, task completion rate, energy consumption, and load balancing. If true, the work would establish diffusion models as practical policy generators in multi-agent reinforcement learning for latency- and energy-constrained UAV task assignment.

What carries the argument

The load-bearing object is the reverse denoising process of a denoising diffusion probabilistic model, used in place of the deterministic actor network of MADDPG. The model approximates the reverse transition $p_\theta(x_{t-1}|x_t) = \mathcal{N}(x_{t-1};\mu_\theta(x_t,t),\Sigma_\theta(x_t,t))$ with the agent's observation as conditioning information $g$, predicts the noise added at each step, and runs the chain $x_T \to x_0$ to produce an assignment action; its parameters are trained by the standard diffusion loss $L_t = E[\|\epsilon - \epsilon_\theta(\sqrt{\alpha_t}x_0 + \sqrt{1-\alpha_t}\epsilon, t)\|^2]$. The other mechanism is the path planner's fitness function $F = \vartheta D_{total} + \rho \Delta t$, where $\Delta t = t_{next} - t_q$ is the time gap between flying to the next target and processing the current target's data; a randomized greedy rule chooses the next coordinate from a random subset of unvisited targets, which the paper argues avoids local optima while keeping computational cost low.

What would settle it

Read Algorithm 2 and write out the gradient used to update the actor (denoising) network; if the update consists only of $\nabla_\theta \|\epsilon - \epsilon_\theta(\ldots)\|^2$ with no term from the critic's Q-value, the algorithm is behavior cloning, not reinforcement learning, and training a denoiser purely on stored transitions should reproduce the reported reward of about 5000. A second check is to run the path planner against a deterministic nearest-target greedy baseline on the same 10 to 50 coordinate instances and verify the claimed 19.3% to 27.1% cost reduction.

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Extended reading notes

Core claim

The paper's central claim is that DNN task assignment in a UAV swarm is best solved as two coupled subproblems, a path-planning subproblem and a task-assignment subproblem, and that a generative diffusion model can replace the actor network in MADDPG for the second part. The path planner selects each next target coordinate from a random candidate subset using a fitness function $F = \vartheta D_{total} + \rho \Delta t$ that jointly penalizes long flight distance and the mismatch between flight time and task-processing time. The assignment algorithm, GDM-MADDPG, conditions a denoising diffusion model on the observation $o_t^n$ of each UAV, starts from Gaussian noise $x_T \sim \mathcal{N}(0,I)$, and iteratively denoises to an action $x_0$ that specifies how the DNN task is split and which UAVs execute the subtasks; the denoising network plays the role of the actor while critics still evaluate state-action values. The paper asserts this is the first integration of a generative diffusion model into the MADDPG framework for multi-UAV networks, and reports that the combined scheme outperforms greedy, MADDPG, and MADDPG-with-path-planning on Age of Information, task completion rate, system utility, and energy consumption.

Load-bearing premise

The load-bearing assumption is that the diffusion-model actor is genuinely trained by reinforcement: Algorithm 2 updates it with the standard diffusion noise-prediction loss, and if that is the only update, the critic's reward signal never shapes the policy, so the claimed gains over MADDPG would not follow from the described mechanism.

Editorial extensions

If this is right

  • If the claims hold, UAV swarms can perform DNN inference without ground-station support by pipelining DNN layers across UAVs and choosing pipeline splits according to current energy, cache, and latency constraints.
  • The task-aware greedy path planner would reduce total flight cost by about 19.3% to 27.1% compared with a plain greedy heuristic, with larger gains as the number of target coordinates grows.
  • GDM-MADDPG would converge to a total reward near 5000 and beat MADDPG, with or without path planning, on Age of Information, task completion rate, and utility across increasing task sizes.
  • The method would handle different DNN families, including YOLOv5, AlexNet, and VGG16, with convergence quality ordered by model complexity.

Reading between the lines

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

  • A direct test of the mechanism is to inspect the actor update in Algorithm 2: if the denoising network is trained only by the noise-prediction loss in Eq. (31), the critic's Q-value never enters the policy update and the described algorithm becomes behavior cloning of actions already in the replay buffer; an ablation that trains the same denoiser by cloning stored actions would settle whether the
  • The path-planning component is independent of the MARL component, so the claimed 19.3% to 27.1% cost reduction could be transferred to any task-assignment policy; a natural experiment is to run each component separately and measure its marginal contribution.
  • Because the reward is a weighted sum of individual utility, task-completion utility, and load-balance variance, the same diffusion-as-actor architecture could be applied to other constrained multi-agent resource allocation problems, such as cache placement, spectrum sharing, or federated learning client selection.
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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 / 4 minor

Summary. The paper proposes a two-stage scheme for DNN task assignment in a mother-child UAV swarm: first, a greedy path-planning algorithm with a fitness function that trades flight distance against task processing time; second, a MARL algorithm called GDM-MADDPG, in which the reverse denoising process of a diffusion model replaces the actor network of MADDPG. The problem is formulated as a constrained utility maximization with AoI, load balancing, and energy terms, and simulations compare the proposed method against greedy, MADDPG, and MADDPG with path planning on path cost, AoI, task completion rate, and utility.

Significance. If the proposed actor update were a correct diffusion-based policy-gradient update, the paper would present a useful combination of generative models and MARL for UAV task assignment. The path-planning part is concrete and falsifiable: Algorithm 1 and Fig. 5(c) report specific percentage cost reductions versus greedy for 10 to 50 target coordinates. However, the central algorithmic contribution is not specified as a working reinforcement-learning update, so the reported convergence and benchmark comparisons do not support the paper's main claim. The manuscript provides no code, no analytical derivations, and the performance metrics that improve (AoI, task completion, utility) are themselves components of the optimized reward, so the improvements are partly by construction.

major comments (4)
  1. [Section VI, Algorithm 2 lines 20-25 and Eq. (31)] This is the load-bearing issue. Algorithm 2 states that the Actor and Critic target network parameters are updated `according to Eq. (31)', but Eq. (31) is the standard DDPM denoising loss, which depends only on the noise, the noised action, and the condition; it contains no Q-value, reward, or policy-gradient term. Taken literally, the actor is trained to denoise actions sampled from the replay buffer conditioned on observations, which is behavior cloning of past actions, not reinforcement learning. The prose in Section VI.A says the output of the Critic network is used to update the Actor network to maximize expected reward, but no such Q-guided or reparameterized gradient is derived or referenced anywhere (for example, no Diffusion-QL-style loss). Consequently, the reported convergence to a total reward of about 5000 and the claimed superiority over MADDPG have no mechanism in the specified algorithm. The future-work remark in Section VIII about training with an expert dataset via supervised learning further confirms that the current update is not a policy-gradient method.
  2. [Section IV, Eq. (23f), constraint C6] Constraint C6 imposes sum_{q=1}^W phi_q = 1, while phi_q is the binary decision variable indicating whether target coordinate q is inspected. This means exactly one target coordinate is inspected, which directly contradicts the path-planning problem in Section V and Algorithm 1, where the UAV visits all W target coordinates (the text says the process is repeated until all target coordinates have been inspected once). The problem formulation is therefore internally inconsistent: the optimization model does not describe the scenario evaluated in the simulations.
  3. [Section IV, Eqs. (19)-(20)] The objective is to maximize U = delta*u1 + epsilon*u2 + theta*u3, and u1 is defined as e^comp_n + e^trans_n, i.e., the energy consumed by a UAV. Maximizing a positive multiple of energy consumption rewards higher energy use, which is the opposite of the paper's stated goal of reducing energy consumption and extending UAV survival time. Unless u1 is intended as a cost term that should enter with a negative sign, the objective function is mis-specified.
  4. [Section VII.B.3 and abstract] The abstract and Table I claim favorable performance in energy consumption, but the experimental section presents no energy-consumption comparison: Figs. 8(a)-(c) show AoI, task completion rate, and utility. Moreover, the reward function in Eqs. (33)-(35) includes AoI, task completion, and utility terms, so the reported improvements on exactly those metrics are partly due to optimizing the same quantities; an evaluation on held-out or independent metrics, or at least a sensitivity analysis, is needed to substantiate the claim of general superiority.
minor comments (4)
  1. [Section V, Algorithm 1] Algorithm 1 is not executable as written: the loop `for i = 1, 2, ..., q' uses an undefined variable q, and the body only contains an if-else that sets the candidate set without selecting the next coordinate inside the loop; the actual selection step described in the text (compute F and choose the minimum) is missing from the pseudocode.
  2. [Section VII.A.1] The notation is confusingly overloaded: the learning rate is denoted by gamma while gamma is also used as the discount factor earlier, and the reward weights sigma and vartheta in Eq. (35) reuse symbols that denote other quantities elsewhere in the paper; please use distinct symbols for distinct parameters.
  3. [Section VII.B] The performance comparisons in Fig. 8 are presented without error bars or confidence intervals, even though the training section reports ten independent runs; it would be useful to know whether the differences between GDM-MADDPG and MADDPG with path planning are statistically meaningful.
  4. [Throughout] There are numerous typographical and formatting issues, including inconsistent spacing in `UA V', the misspelling `latencys', and the broken phrase `product ef ly n' in the text after Eq. (16); a careful proofreading pass is needed.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper's central claims rest on benchmark comparisons under a shared objective, and no equation reduces a predicted quantity to a fitted input.

full rationale

Walking the derivation chain: the path-planning subproblem (P2, Eq. 26) minimizes the fitness function F (Eq. 25) and is compared against a greedy baseline in Fig. 5; this is an empirical comparison, and the fitness function is not derived from the baseline's output. The DNN-task-assignment objective P1 (Eq. 23) maximizes utility U (Eq. 19), and the reward (Eqs. 33-35) is explicitly constructed from the same u1-u3 components, so the reported AoI, task-completion, and utility improvements are aligned with the optimized objective by design; this is reward shaping, not a circular prediction, and all compared algorithms are evaluated under the same reward. No uniqueness theorem or prior work by the authors is invoked as load-bearing evidence; Eq. (31) is the standard DDPM loss attributed to Ho et al. The only passage that could appear circular is Algorithm 2 line 24, where the actor is updated 'according to Eq. (31)' and the promised Q-value-based actor gradient in Section VI.A is never specified; I flag this as an omitted or inconsistent derivation, but it is not a case where the paper's output reduces to its input by construction. The empirical results are externally benchmarked, so no circular step is established.

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

The paper introduces no new physical entities. The free parameters are mostly reward and utility weights that are set by hand or tuned, and the axioms are standard modeling assumptions for UAV communications and DNN partitioning, plus the DDPM framework borrowed from prior work.

free parameters (5)
  • Utility weights delta, epsilon, theta = not reported
    Weights in Eq. (19) combining individual contribution, task completion, and load balance; chosen without sensitivity analysis.
  • Reward weights sigma, vartheta = not reported
    Weights in Eqs. (33)-(35) balancing individual and group rewards; values not given in the paper.
  • Path planning weights vartheta, rho = 0.5, 0.5
    Weights in fitness function Eq. (25); set to 0.5 in Table II without ablation.
  • Number of random candidates k = 5
    Random candidate set size in the greedy path planning algorithm; set to 5 in Table II.
  • AoI utility coefficients alpha, beta, gamma = not reported
    Coefficients in task completion rate utility u2 in Eq. (21).
assumptions (5)
  • domain assumption UAV-to-UAV links are line-of-sight and follow the Close-In Free Space Reference Model
    Used in Eq. (3) for path loss; simplified model with fixed formation flight.
  • domain assumption UAV swarm maintains fixed formation flight during task execution
    Stated in Section III-C; distances between UAVs are assumed constant along the route.
  • domain assumption DNN tasks can be partitioned at arbitrary layer boundaries and executed serially across UAVs
    Assumed in DNN task model; each layer is the atomic unit.
  • domain assumption The leader UAV has full knowledge of all observations and acts as central decision maker
    Used in the MARL setup; agents are followers, leader assigns tasks.
  • standard math Standard DDPM training loss Eq. (31) is valid for the conditional denoising network
    Borrowed from Denoising Diffusion Probabilistic Models [43].

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Cite this review

Pith. "Pith review of DNN Task Assignment in UAV Networks: A Generative AI Enhanced Multi-Agent Reinforcement Learning Approach." pith.science (2026). https://pith.science/paper/PRKV3T2X

@misc{pith2026241108299,
  author       = {Pith},
  title        = {Pith review of: DNN Task Assignment in UAV Networks: A Generative AI Enhanced Multi-Agent Reinforcement Learning Approach},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PRKV3T2X}},
  note         = {Machine review of arXiv:2411.08299}
}
read the original abstract

Unmanned Aerial Vehicles (UAVs) possess high mobility and flexible deployment capabilities, prompting the development of UAVs for various application scenarios within the Internet of Things (IoT). The unique capabilities of UAVs give rise to increasingly critical and complex tasks in uncertain and potentially harsh environments. The substantial amount of data generated from these applications necessitates processing and analysis through deep neural networks (DNNs). However, UAVs encounter challenges due to their limited computing resources when managing DNN models. This paper presents a joint approach that combines multiple-agent reinforcement learning (MARL) and generative diffusion models (GDM) for assigning DNN tasks to a UAV swarm, aimed at reducing latency from task capture to result output. To address these challenges, we first consider the task size of the target area to be inspected and the shortest flying path as optimization constraints, employing a greedy algorithm to resolve the subproblem with a focus on minimizing the UAV's flying path and the overall system cost. In the second stage, we introduce a novel DNN task assignment algorithm, termed GDM-MADDPG, which utilizes the reverse denoising process of GDM to replace the actor network in multi-agent deep deterministic policy gradient (MADDPG). This approach generates specific DNN task assignment actions based on agents' observations in a dynamic environment. Simulation results indicate that our algorithm performs favorably compared to benchmarks in terms of path planning, Age of Information (AoI), energy consumption, and task load balancing.

Figures

Figures reproduced from arXiv: 2411.08299 by the authors.

Figure 1
Figure 1. Four task assignment models. I. INTRODUCTION Advancements in Unmanned Aerial Vehicles (UAVs) technology have led to their increasing use in various civil and military domains, including aerial object detection, rapid rescue operations in disaster-stricken areas [1], [2], emergency scenarios [3], and extensive agriculture [4]. Unlike traditional Internet of Things (IoT) devices [5], UAVs offer not only lower costs, b… view at source ↗
Figure 2
Figure 2. The computational complexity, processing latency, and output data size of each layer of different models. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Network architecture. The DNN model typically comprises l layers, denoted as l = {1, 2, ..., L}. Each layer has a cache capacity mi,l and a computing requirement ci,l, represented by a tuple (mi,l, ci,l). lκ = {1, 2, ..., Lκ} represents a set of layers associated with a specific type of DNN task, including convolutional and pooling layers for feature extraction, as well as fully connected layers for classification [… view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: GDM-MADDPG for strategy generation. process, specifically denoising Gaussian noise to yield opti￾mal action decisions. This approach transforms the challenge of limited datasets into an opportunity for dynamic online learning and agent self-optimization. GDM-MADDPG tak…
Figure 5
Figure 5. Figure 5: UAV path planning considering task size and flight distance. [PITH_FULL_IMAGE:figures/full_fig_p011_5.png]
Figure 6
Figure 6. Figure 6: The training of different DNN models and benchmark algorithms. [PITH_FULL_IMAGE:figures/full_fig_p011_6.png]
Figure 8
Figure 8. Figure 8: Comparison of the AoI, task completion rate, and utility across varying task sizes. [PITH_FULL_IMAGE:figures/full_fig_p012_8.png]

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Reference graph

Works this paper leans on

44 extracted references · 37 canonical work pages · cited by 1 Pith paper

  1. [8]

    Dnn partitioning, task offloading, and resource allocation in dynamic vehicular networks: A lyapunov-guided diffusion-based reinforcement learning approach,

    Z. Liu, H. Du, J. Lin, Z. Gao, L. Huang, S. Hosseinalipour, and D. Niyato, “Dnn partitioning, task offloading, and resource allocation in dynamic vehicular networks: A lyapunov-guided diffusion-based reinforcement learning approach,” arXiv preprint arXiv:2406.06986 , 2024

    arXiv 2024

  2. [1]

    Ugen: Uav and gan-aided ensemble network for post-disaster survivor detection through oran,

    G. Raja, A. Manoharan, and H. Siljak, “Ugen: Uav and gan-aided ensemble network for post-disaster survivor detection through oran,” IEEE Transactions on Vehicular Technology, 2024

    work page 2024

  3. [2]

    Uav swarm- assisted two-tier hierarchical federated learning,

    T. Wang, X. Huang, Y . Wu, L. Qian, B. Lin, and Z. Su, “Uav swarm- assisted two-tier hierarchical federated learning,” IEEE Transactions on Network Science and Engineering , 2023

    work page 2023

  4. [3]

    Uav- assisted wireless cooperative communication and coded caching: a multiagent two-timescale drl approach,

    B. Tian, L. Wang, L. Xu, W. Pan, H. Wu, L. Li, and Z. Han, “Uav- assisted wireless cooperative communication and coded caching: a multiagent two-timescale drl approach,” IEEE Transactions on Mobile Computing, 2023

    work page 2023

  5. [4]

    Detecting and mapping individual fruit trees in complex natural envi- ronments via uav remote sensing and optimized yolov5,

    Y . Xiong, X. Zeng, W. Lai, J. Liao, Y . Chen, M. Zhu, and K. Huang, “Detecting and mapping individual fruit trees in complex natural envi- ronments via uav remote sensing and optimized yolov5,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2024

    work page 2024

  6. [5]

    Dynamic social-aware peer selection for cooperative relay management with d2d communi- cations,

    Y . Gao, Y . Xiao, M. Wu, M. Xiao, and J. Shao, “Dynamic social-aware peer selection for cooperative relay management with d2d communi- cations,” IEEE Transactions on Communications , vol. 67, no. 5, pp. 3124–3139, 2019

    work page 2019

  7. [6]

    Split learning with differential privacy for integrated terrestrial and non- terrestrial networks,

    M. Wu, G. Cheng, P. Li, R. Yu, Y . Wu, M. Pan, and R. Lu, “Split learning with differential privacy for integrated terrestrial and non- terrestrial networks,” IEEE Wireless Communications, 2023

    work page 2023

  8. [7]

    Energy-efficient uav assisted secure relay transmission via cooperative computation offloading,

    T. Wang, Y . Li, and Y . Wu, “Energy-efficient uav assisted secure relay transmission via cooperative computation offloading,” IEEE Transac- tions on Green Communications and Networking , vol. 5, no. 4, pp. 1669–1683, 2021

    work page 2021

Show all 44 references
  1. [9]

    All-sky autonomous computing in uav swarm,

    H. Sun, Y . Qu, C. Dong, H. Dai, Z. Li, L. Zhang, Q. Wu, and S. Guo, “All-sky autonomous computing in uav swarm,” IEEE Transactions on Mobile Computing, 2024

    2024

  2. [10]

    Integrated sensing, communication, and computation with adaptive dnn splitting in multi-uav networks,

    C. Deng, X. Fang, and X. Wang, “Integrated sensing, communication, and computation with adaptive dnn splitting in multi-uav networks,” IEEE Transactions on Wireless Communications , 2024

    2024

  3. [11]

    Elastic collaborative edge intelligence for uav swarm: Architecture, challenges, and opportunities,

    Y . Qu, H. Sun, C. Dong, J. Kang, H. Dai, Q. Wu, and S. Guo, “Elastic collaborative edge intelligence for uav swarm: Architecture, challenges, and opportunities,” IEEE Communications Magazine , 2023. 13

    2023

  4. [12]

    Distributed cnn inference on resource- constrained uavs for surveillance systems: Design and optimization,

    M. Jouhari, A. K. Al-Ali, E. Baccour, A. Mohamed, A. Erbad, M. Guizani, and M. Hamdi, “Distributed cnn inference on resource- constrained uavs for surveillance systems: Design and optimization,” IEEE Internet of Things Journal , vol. 9, no. 2, pp. 1227–1242, 2021

    2021

  5. [13]

    Object recognition offloading in augmented reality assisted uav-ugv systems,

    C. Wang, B. Carlson, and Q. Han, “Object recognition offloading in augmented reality assisted uav-ugv systems,” inProceedings of the Ninth Workshop on Micro Aerial Vehicle Networks, Systems, and Applications, 2023, pp. 33–38

    2023

  6. [14]

    Reliable dnn partitioning for uav swarm,

    M. Zhao, X. Zhang, Z. Meng, and X. Hou, “Reliable dnn partitioning for uav swarm,” in 2022 International Wireless Communications and Mobile Computing (IWCMC) . IEEE, 2022, pp. 265–270

    2022

  7. [15]

    Multi-uav maritime search and rescue with dnn inference acceleration,

    T. Yang, H. He, and L. Kong, “Multi-uav maritime search and rescue with dnn inference acceleration,” in 2023 International Conference on Ubiquitous Communication (Ucom) . IEEE, 2023, pp. 248–253

    2023

  8. [16]

    Aoi- aware sensing scheduling and trajectory optimization for multi-uav- assisted wireless backscatter networks,

    Y . Long, S. Zhao, S. Gong, B. Gu, D. Niyato, and X. Shen, “Aoi- aware sensing scheduling and trajectory optimization for multi-uav- assisted wireless backscatter networks,” IEEE Transactions on Vehicular Technology, 2024

    2024

  9. [17]

    Efficient pipeline collaborative dnn inference in resource-constrained uav swarm,

    W. Ren, Y . Qu, Z. Qin, C. Dong, F. Zhou, L. Zhang, and Q. Wu, “Efficient pipeline collaborative dnn inference in resource-constrained uav swarm,” in 2024 IEEE Wireless Communications and Networking Conference (WCNC). IEEE, 2024, pp. 1–6

    2024

  10. [18]

    Artificial intelligence for enhanced mobility and 5g connectivity in uav-based critical missions,

    S. Lins, K. V . Cardoso, C. B. Both, L. Mendes, J. F. De Rezende, A. Silveira, N. Linder, and A. Klautau, “Artificial intelligence for enhanced mobility and 5g connectivity in uav-based critical missions,” IEEE Access, vol. 9, pp. 111 792–111 801, 2021

    2021

  11. [19]

    On uav serving nodes trajectory planning for fast localization in forest environment: A multi-agent drl approach,

    X. Wang, L. Wang, Z. Liu, L. Xu, and A. Fei, “On uav serving nodes trajectory planning for fast localization in forest environment: A multi-agent drl approach,” in 2023 IEEE Wireless Communications and Networking Conference (WCNC) . IEEE, 2023, pp. 1–6

    2023

  12. [20]

    Game theory-based anti- jamming strategies for frequency hopping wireless communications,

    Y . Gao, Y . Xiao, M. Wu, M. Xiao, and J. Shao, “Game theory-based anti- jamming strategies for frequency hopping wireless communications,” IEEE Transactions on Wireless Communications , vol. 17, no. 8, pp. 5314–5326, 2018

    2018

  13. [21]

    Comparing yolov3, yolov4 and yolov5 for autonomous landing spot detection in faulty uavs,

    U. Nepal and H. Eslamiat, “Comparing yolov3, yolov4 and yolov5 for autonomous landing spot detection in faulty uavs,” Sensors, vol. 22, no. 2, p. 464, 2022

    2022

  14. [22]

    Multilevel recognition of uav-to-ground targets based on micro-doppler signatures and transfer learning of deep convolutional neural networks,

    L. Zhu, S. Zhang, X. Wang, S. Chen, H. Zhao, and D. Wei, “Multilevel recognition of uav-to-ground targets based on micro-doppler signatures and transfer learning of deep convolutional neural networks,” IEEE Transactions on Instrumentation and Measurement , vol. 70, pp. 1–11, 2020

    2020

  15. [23]

    A novel enhanced vgg16 model to tackle grapevine leaves diseases with automatic method,

    S. Mousavi and G. Farahani, “A novel enhanced vgg16 model to tackle grapevine leaves diseases with automatic method,” IEEE Access, vol. 10, pp. 111 564–111 578, 2022

    2022

  16. [24]

    Task partitioning and offloading in dnn-task enabled mobile edge computing networks,

    M. Gao, R. Shen, L. Shi, W. Qi, J. Li, and Y . Li, “Task partitioning and offloading in dnn-task enabled mobile edge computing networks,” IEEE Transactions on Mobile Computing, vol. 22, no. 4, pp. 2435–2445, 2021

    2021

  17. [25]

    Distributed dnn inference with fine-grained model partitioning in mobile edge computing networks,

    H. Li, X. Li, Q. Fan, Q. He, X. Wang, and V . C. Leung, “Distributed dnn inference with fine-grained model partitioning in mobile edge computing networks,” IEEE Transactions on Mobile Computing , 2024

    2024

  18. [26]

    Toward reliable dnn-based task partitioning and of- floading in vehicular edge computing,

    C. Liu and K. Liu, “Toward reliable dnn-based task partitioning and of- floading in vehicular edge computing,” IEEE Transactions on Consumer Electronics, vol. 70, no. 1, pp. 3349–3360, 2023

    2023

  19. [27]

    Multi- agent reinforcement learning based distributed transmission in collabo- rative cloud-edge systems,

    C. Xu, S. Liu, C. Zhang, Y . Huang, Z. Lu, and L. Yang, “Multi- agent reinforcement learning based distributed transmission in collabo- rative cloud-edge systems,” IEEE Transactions on Vehicular Technology, vol. 70, no. 2, pp. 1658–1672, 2021

    2021

  20. [28]

    Ga- drl: Graph neural network-augmented deep reinforcement learning for dag task scheduling over dynamic vehicular clouds,

    Z. Liu, L. Huang, Z. Gao, M. Luo, S. Hosseinalipour, and H. Dai, “Ga- drl: Graph neural network-augmented deep reinforcement learning for dag task scheduling over dynamic vehicular clouds,” IEEE Transactions on Network and Service Management , 2024

    2024

  21. [29]

    Trajectory design and power control for multi-uav assisted wireless networks: A machine learning approach,

    X. Liu, Y . Liu, Y . Chen, and L. Hanzo, “Trajectory design and power control for multi-uav assisted wireless networks: A machine learning approach,” IEEE Transactions on Vehicular Technology , vol. 68, no. 8, pp. 7957–7969, 2019

    2019

  22. [30]

    Energy efficient computation offloading in aerial edge networks with multi-agent cooperation,

    W. Liu, B. Li, W. Xie, Y . Dai, and Z. Fei, “Energy efficient computation offloading in aerial edge networks with multi-agent cooperation,” IEEE Transactions on Wireless Communications , vol. 22, no. 9, pp. 5725– 5739, 2023

    2023

  23. [31]

    Cooperative multi- agent deep reinforcement learning methods for uav-aided mobile edge computing networks,

    M. Kim, H. Lee, S. Hwang, M. Debbah, and I. Lee, “Cooperative multi- agent deep reinforcement learning methods for uav-aided mobile edge computing networks,” IEEE Internet of Things Journal , 2024

    2024

  24. [32]

    Joint secure offloading and resource allocation for vehicular edge computing network: A multi-agent deep reinforcement learning approach,

    Y . Ju, Y . Chen, Z. Cao, L. Liu, Q. Pei, M. Xiao, K. Ota, M. Dong, and V . C. Leung, “Joint secure offloading and resource allocation for vehicular edge computing network: A multi-agent deep reinforcement learning approach,” IEEE Transactions on Intelligent Transportation Sys...

    2023

  25. [33]

    Mobile-aware service offloading for uav-assisted iovs: A multi-agent tiny distributed learning approach,

    Y . Liu, P. Lin, M. Zhang, Z. Zhang, and F. R. Yu, “Mobile-aware service offloading for uav-assisted iovs: A multi-agent tiny distributed learning approach,” IEEE Internet of Things Journal , 2024

    2024

  26. [34]

    Multi-agent learning-based optimal task offloading and uav trajectory planning for agin-power iot,

    P. Qin, Y . Fu, Y . Xie, K. Wu, X. Zhang, and X. Zhao, “Multi-agent learning-based optimal task offloading and uav trajectory planning for agin-power iot,” IEEE Transactions on Communications , vol. 71, no. 7, pp. 4005–4017, 2023

    2023

  27. [35]

    Generative ai for advanced uav networking,

    G. Sun, W. Xie, D. Niyato, H. Du, J. Kang, J. Wu, S. Sun, and P. Zhang, “Generative ai for advanced uav networking,” arXiv preprint arXiv:2404.10556, 2024

    2024 arXiv

  28. [36]

    Guiding iot-based healthcare alert systems with large language models,

    Y . Gao, Z. Ye, M. Xiao, Y . Xiao, and D. I. Kim, “Guiding iot-based healthcare alert systems with large language models,” arXiv preprint arXiv:2408.13071, 2024

    2024 arXiv

  29. [37]

    Rme-gan: A learning framework for radio map estimation based on conditional generative adversarial network,

    S. Zhang, A. Wijesinghe, and Z. Ding, “Rme-gan: A learning framework for radio map estimation based on conditional generative adversarial network,” IEEE Internet of Things Journal , vol. 10, no. 20, pp. 18 016– 18 027, 2023

    2023

  30. [38]

    Generative ai for physical layer communications: A survey,

    N. Van Huynh, J. Wang, H. Du, D. T. Hoang, D. Niyato, D. N. Nguyen, D. I. Kim, and K. B. Letaief, “Generative ai for physical layer communications: A survey,” IEEE Transactions on Cognitive Communications and Networking , 2024

    2024

  31. [39]

    Joint task offloading and resource allocation in heterogeneous edge environments,

    Y . Liu, Y . Mao, Z. Liu, F. Ye, and Y . Yang, “Joint task offloading and resource allocation in heterogeneous edge environments,” IEEE Transactions on Mobile Computing , 2023

    2023

  32. [40]

    Cooperative uav resource allocation and task offloading in hierarchical aerial computing systems: A mappo-based approach,

    H. Kang, X. Chang, J. Mi ˇsi´c, V . B. Mi ˇsi´c, J. Fan, and Y . Liu, “Cooperative uav resource allocation and task offloading in hierarchical aerial computing systems: A mappo-based approach,” IEEE Internet of Things Journal, vol. 10, no. 12, pp. 10 497–10 509, 2023

    2023

  33. [41]

    Digital-twin- assisted task assignment in multi-uav systems: A deep reinforcement learning approach,

    X. Tang, X. Li, R. Yu, Y . Wu, J. Ye, F. Tang, and Q. Chen, “Digital-twin- assisted task assignment in multi-uav systems: A deep reinforcement learning approach,” IEEE Internet of Things Journal , vol. 10, no. 17, pp. 15 362–15 375, 2023

    2023

  34. [42]

    Gan-powered heterogeneous multi- agent reinforcement learning for uav-assisted task offloading,

    Y . Li, L. Feng, Y . Yang, and W. Li, “Gan-powered heterogeneous multi- agent reinforcement learning for uav-assisted task offloading,” Ad Hoc Networks, vol. 153, p. 103341, 2024

    2024

  35. [43]

    Denoising diffusion probabilistic models,

    J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” Advances in neural information processing systems , vol. 33, pp. 6840– 6851, 2020

    2020

  36. [44]

    Computation offloading via multi-agent deep reinforcement learning in aerial hierarchical edge computing systems,

    Y . Wang, C. Zhang, T. Ge, and M. Pan, “Computation offloading via multi-agent deep reinforcement learning in aerial hierarchical edge computing systems,” IEEE Transactions on Network Science and Engi- neering, 2024

    2024

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.