Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) is a key enabler for meeting the stringent low-latency and energy-efficiency requirements of emerging low-altitude economy applications. However, achieving these objectives remains challenging due to dynamic environments, limited communication and computation resources, and the heterogeneity of network entities. This paper investigates the long-term joint optimization framework that minimizes system-wide latency and energy consumption simultaneously by coordinating UAV association, subchannel selection, uplink/downlink power allocation, and computational resource distribution. This sequential decision-making process is formulated into a partially observable Markov decision process (POMDP) to account for localized observations and dynamic channel states. To solve it, we propose a heterogeneous multi-agent proximal policy optimization (MAPPO)-based framework where both user devices (UDs) and UAVs act as heterogeneous agents. This architecture utilizes a centralized training and decentralized execution (CTDE) paradigm to enable collaborative strategies between computing requesters and providers. Numerical results demonstrate that the proposed scheme effectively navigates the high-dimensional action space and achieves superior convergence and cost reduction compared to benchmarks, including PPO, independent PPO (iPPO), Q-learning multi-agent extension (QMIX), value decomposition networks (VDN), independent deep Q-network (iDQN), and genetic algorithm (GA).
Ming Cheng, Canlin Zhu, Jiang-Hang Tang et al.· Journal of King Saud Univers...· 0 citations
Low-altitude embodied intelligence (LAEI) has emerged as a promising solution for operational efficiency and sustainability of the emerging low-altitude economy via perception–reasoning–action loops. The ground base stations with limited service coverages fail to achieve ubiquitous connectivity in widespread environments. The aerial agents embedded in flying bodies ensure pervasive intelligence across dynamic three-dimensional spaces. However, the joint optimization of flight trajectories and resource allocation for hierarchical UAV networks introduces large state and action spaces, posing significant challenges for real-time mission execution. In this paper, we propose an agentic Generative Artificial Intelligence (GenAI)-based LAEI framework. In the framework, a joint optimization problem is formulated to minimize long-term average energy consumption while ensuring task queue stability and satisfying spatial kinematic constraints. The Lyapunov optimization technique decomposes the long-term energy minimization problem into deterministic per-slot sub-problems with low computational complexity. A diffusion-based GenAI algorithm synthesizes optimal trajectories through an iterative denoising process, where the model-based resource allocation problem serves as guidance to accelerate convergence. Finally, extensive simulation experiments indicate that the proposed GenAI-enabled algorithm outperforms other baseline schemes, delivering minimized energy consumption and enhanced resource utilization in dynamic low-altitude embodied intelligence environments.
Dong-Hai Wu, Jiangtian Nie, Yang Zhang et al.· IEEE Transactions on Cogniti...· 0 citations