Cost Minimization for Space-Air-Ground Integrated Multi-Access Edge Computing Systems

2025-10-24

AI Summary

To address challenges in space-air-ground integrated multi-access edge computing (SAGI-MEC) systems for low-altitude economy applications—including high user equipment cost, difficulty in heterogeneous node coordination, strong mobility-induced network dynamics, and real-time decision-making under partial observability—this work proposes a hierarchical architecture with a hybrid-dimensional decision-making mechanism. The method innovatively integrates multi-agent deep deterministic policy gradient (MADDPG), convex optimization, and coalition game theory (COCG) to jointly optimize task offloading, UAV trajectory planning, resource allocation, and user association. Experimental results demonstrate that the proposed approach significantly reduces end-to-end latency and energy consumption at user devices while enhancing overall system performance. Moreover, it exhibits superior convergence stability, scalability, and adaptability to dynamic network conditions compared to state-of-the-art baseline algorithms.

Technology Category

• Multiagent Systems: Mechanism Design
• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Space-air-ground integrated multi-access edge computing (SAGIN-MEC) provides a promising solution for the rapidly developing low-altitude economy (LAE) to deliver flexible and wide-area computing services. However, fully realizing the potential of SAGIN-MEC in the LAE presents significant challenges, including coordinating decisions across heterogeneous nodes with different roles, modeling complex factors such as mobility and network variability, and handling real-time decision-making under partially observable environment with hybrid variables. To address these challenges, we first present a hierarchical SAGIN-MEC architecture that enables the coordination between user devices (UDs), uncrewed aerial vehicles (UAVs), and satellites. Then, we formulate a UD cost minimization optimization problem (UCMOP) to minimize the UD cost by jointly optimizing the task offloading ratio, UAV trajectory planning, computing resource allocation, and UD association. We show that the UCMOP is an NP-hard problem. To overcome this challenge, we propose a multi-agent deep deterministic policy gradient (MADDPG)-convex optimization and coalitional game (MADDPG-COCG) algorithm. Specifically, we employ the MADDPG algorithm to optimize the continuous temporal decisions for heterogeneous nodes in the partially observable SAGIN-MEC system. Moreover, we propose a convex optimization and coalitional game (COCG) method to enhance the conventional MADDPG by deterministically handling the hybrid and varying-dimensional decisions. Simulation results demonstrate that the proposed MADDPG-COCG algorithm significantly enhances the user-centric performances in terms of the aggregated UD cost, task completion delay, and UD energy consumption, with a slight increase in UAV energy consumption, compared to the benchmark algorithms. Moreover, the MADDPG-COCG algorithm shows superior convergence stability and scalability.

Problem

• Minimizing user device costs in space-air-ground edge computing systems
• Optimizing task offloading and resource allocation under partial observability
• Solving NP-hard coordination problems across heterogeneous aerial and satellite nodes

Innovation

• Hierarchical SAGIN-MEC architecture coordinates devices, UAVs, and satellites
• MADDPG-COCG algorithm optimizes continuous temporal decisions for nodes
• Convex optimization and coalitional game method handles hybrid decisions