Collaborative resource allocation in UAV-assisted MEC networks: A heterogeneous MAPPO scheme

Abstract

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).