2026· IEEE Transactions on Communications· Vol 74, pp. 11819-11831· 0 citations· 45 references
Abstract
Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) systems provide flexible computing services for resource-constrained devices, but malicious jamming attacks introduce dynamic channel conditions and resource competition, making joint trajectory and resource optimization challenging. This paper investigates this problem in multi-UAV MEC systems under jamming, aiming to minimize delay and energy consumption while ensuring anti-jamming robustness. The problem is formulated as a decentralized partially observable Markov decision process (Dec-POMDP). However, traditional multi-agent reinforcement learning (MARL) approaches struggle with high exploration costs and low sampling efficiency in high-dimensional hybrid action spaces. To overcome these limitations, we propose an LLM-guided MARL framework instantiated with the multi-agent deep deterministic policy gradient (MADDPG), which leverages LLM-generated semantic trajectory prompts to dynamically constrain exploration within the continuous action space, effectively compressing the policy search space and accelerating convergence. Simulation results demonstrate that the proposed method achieves $3.4\times $ to $5\times $ faster convergence over hierarchical MADDPG, MADDPG, and independent soft actor-critic (ISAC) baselines, significantly reducing training costs while maintaining superior performance and anti-jamming robustness.
Deploying Uncrewed Aerial Vehicles (UAVs) for dynamic target search in disaster response scenarios can reduce losses. This paper investigates multi-UAV cooperative trajectory planning for dynamic target search in a three-dimensional environment with static obstacles, aiming to maximize the number of searched targets and minimize the average uncertainty of the search area, while ensuring collision avoidance between UAVs and obstacles. Existing Multi-Agent Reinforcement Learning (MARL) based methods face the sparse reward problem in dynamic target search, which hinders planning feasible multi-UAV trajectories. Notably, Large Language Models (LLMs), with extensive pre-trained knowledge and powerful semantic reasoning capabilities, exhibit potential for designing high-quality reward functions to alleviate the sparse reward problem. Therefore, we propose an LLM-guided Multi-Agent Proximal Policy Optimization (LLM-MAPPO) algorithm, which leverages LLMs’ reasoning capabilities to guide MARL policy learning and plans multi-UAV trajectories for efficient dynamic target search. Specifically, we design an offline LLM reward shaping scheme that generates dense reward signals to mitigate the sparse reward problem. Moreover, we propose a dual-mode pheromone-based search mechanism to guide UAVs to respond promptly to changes in target positions. Experimental results demonstrate that LLM-MAPPO significantly outperforms compared algorithms in terms of the number of searched targets and average area uncertainty, while successfully avoiding collisions. In particular, LLM-MAPPO reduces the target search time by 71.4%.
Yifei Liu, Xiaoshuai Li, Xia Jiang et al.· IEEE Transactions on Cogniti...· 0 citations
A Prioritized Adaptive Weighting based on Deep Deterministic Policy Gradient (PAW-DDPG) as an enhanced Deep Deterministic Policy Gradient (DDPG) algorithm to minimize both processing delay and energy consumption by jointly optimizing user scheduling, partial-task offloading, and UAV trajectory is proposed.
W. Saber, Hanan Algamil, Fifi Farouk et al.· Future Internet· 0 citations
A predictive multi-agent Reinforcement Learning (RL) framework that proactively maintains SLA stability in UAV-enabled MEC through coordinated trajectory control and computation resource allocation and designs an SLA-aware reward function that explicitly penalizes both violation probability and duration across slices.
M. Farhoudi, Zeinab Sasan, Masoud Shokrnezhad et al.· 0 citations
Unmanned aerial vehicle (UAV) communications are a promising enabler for 6G networks, offering flexible deployment and strong line-of-sight channel conditions. Effective UAV operation requires jointly optimizing trajectory and user scheduling to balance throughput and information freshness. This paper proposes a proximal policy optimization (PPO)-based deep reinforcement learning (DRL) framework that controls UAV movement and user scheduling together via a joint MultiDiscrete action space. We formulate a Markov decision process for a 8-user, $1000 \times 1000 \mathrm{~m}^{2}$ service area with a 3GPP TR 36.777-compliant channel model, where the agent selects both its next position and which user to serve at each time slot. The proposed PPO policy achieves 85.75 Mbps mean throughput, a 24.4% improvement over the AoI-greedy baseline, while reducing mean AoI by 87.7% compared to the throughput-greedy baseline, reaching a Pareto-optimal trade-off between the two competing objectives. An ablation study over the AoI penalty weight confirms a clear throughput-AoI trade-off, validating the joint design.
Quang Tuan Do, Tung Son Do, Thanh Phung Truong et al.· International Conference on...· 0 citations
This paper investigates the problem of cooperative multiple unmanned aerial vehicles (UAVs) data collection for Internet of Things (IoT) networks in dense urban environments. Unlike existing studies that predominantly rely on idealized spatial models and average-based probabilistic channel models, this work explicitly accounts for realistic 3-D building distributions and deterministically models ground-to-air (G2A) channel blockages. We formulate a joint optimization problem to minimize the total task completion time, subject to stringent system throughput, flight dynamics, and energy constraints. To tackle the highly coupled challenges of node scheduling and trajectory planning, we propose a lightweight two-stage heuristic strategy for dynamic access control, along with a multi-agent reinforcement learning for trajectory planning. Crucially, to overcome the severe sparse-reward bottleneck inherent in complex 3-D obstacle avoidance, we introduce a Pheromone-based Reward Shaping (PRS) mechanism. By mathematically integrating the UAV’s kinematic state with deterministic environmental feedback, PRS effectively transforms the sparse-reward navigation challenge into a dense and smooth gradient, thereby profoundly accelerating policy convergence. Extensive simulations demonstrate that the proposed MATD3-PRS framework significantly outperforms representative baselines, achieving superior performance in task completion time, flight trajectory efficiency, and overall energy saving.
Haitao Chen, Xinfeng Deng, Zhe Wang et al.· IEEE Transactions on Cogniti...· 0 citations
Unmanned aerial vehicles (UAVs) have emerged as a key enabler of next-generation Internet of Things (IoT) ecosystems, offering flexible aerial relaying to extend connectivity across dynamic vehicular ad hoc networks (VANETs) in smart city environments. However, conventional centralized approaches for UAV trajectory planning require continuous global network state aggregation, making them impractical under bandwidth and energy constraints typical of dense urban deployments. In this article, we present TRUAV, a distributed multi-agent reinforcement learning framework based on independent tabular Q-learning for joint UAV trajectory planning and routing enhancement in UAV-aided VANETs. Each UAV is equipped with a local Q-learning agent that operates purely on locally observable information, including vehicle density, packet queue states, and neighbor UAV positions, thereby eliminating the need for global state exchange. A potential-game-inspired reward design encourages spatial diversity and routing-aware UAV positioning among interacting agents while accounting for energy consumption. Numerical simulations over a large urban area with 200 mobile vehicles show that the proposed TRUAV framework achieves network coverage and packet delivery ratios comparable to centralized deep reinforcement learning methods, while also improving relay delay and energy efficiency. Finally, we discuss emerging challenges and future research directions for distributed multi-agent UAV-assisted IoT systems.
Muhammad Umar Farooq Qaisar, Lin Zhang, Zhen Chen et al.· 0 citations