Multi-Agent DRL for Queue-Aware Task Offloading in Hierarchical MEC-Enabled Air-Ground Networks

• URL: http://arxiv.org/abs/2503.03391v1
• Date: Wed, 05 Mar 2025 11:12:40 GMT
• Title: Multi-Agent DRL for Queue-Aware Task Offloading in Hierarchical MEC-Enabled Air-Ground Networks
• Authors: Muhammet Hevesli, Abegaz Mohammed Seid, Aiman Erbad, Mohamed Abdallah,
• Abstract summary: Mobile edge computing (MEC)-enabled air-ground networks are a key component of 6G.<n>This paper tackles the overall energy problem in MEC-enabled air-ground integrated networks (MAGIN)<n>We propose a novel variant of multi-altitude policy optimization with a Beta distribution (MAPPO-BD) to solve it.
• Score: 4.0948483603286245
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Mobile edge computing (MEC)-enabled air-ground networks are a key component of 6G, employing aerial base stations (ABSs) such as unmanned aerial vehicles (UAVs) and high-altitude platform stations (HAPS) to provide dynamic services to ground IoT devices (IoTDs). These IoTDs support real-time applications (e.g., multimedia and Metaverse services) that demand high computational resources and strict quality of service (QoS) guarantees in terms of latency and task queue management. Given their limited energy and processing capabilities, IoTDs rely on UAVs and HAPS to offload tasks for distributed processing, forming a multi-tier MEC system. This paper tackles the overall energy minimization problem in MEC-enabled air-ground integrated networks (MAGIN) by jointly optimizing UAV trajectories, computing resource allocation, and queue-aware task offloading decisions. The optimization is challenging due to the nonconvex, nonlinear nature of this hierarchical system, which renders traditional methods ineffective. We reformulate the problem as a multi-agent Markov decision process (MDP) with continuous action spaces and heterogeneous agents, and propose a novel variant of multi-agent proximal policy optimization with a Beta distribution (MAPPO-BD) to solve it. Extensive simulations show that MAPPO-BD outperforms baseline schemes, achieving superior energy savings and efficient resource management in MAGIN while meeting queue delay and edge computing constraints.

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