Task Segmentation and Resource Allocation in MEC-Based Space-Air-Ground Integrated Networks

doi:10.19678/j.issn.1000-3428.0069181

Abstract

Abstract:

In the 6G era, a Space-Air-Ground Integrated Network (SAGIN) can provide ubiquitous coverage for Internet of Things (IoT) devices and can therefore effectively address the current inadequacies in network architecture coverage capabilities. Multi-access Edge Computing (MEC) is a crucial technology that further enhances the service capabilities of SAGIN, demonstrating significant abilities in reducing task execution latency and system energy consumption. This paper proposes an MEC-based SAGIN architecture in which satellites and multiple Unmanned Aerial Vehicles (UAVs) act as edge nodes that offers computational power in close proximity to IoT devices. Through the task segmentation of IoT devices and bandwidth allocation for UAVs and satellites, the proposed architecture intends to minimize the average network energy consumption. The problem of high network dynamics is reformulated as a Markov Decision Process (MDP), and a low-complexity adaptive decision algorithm based on Deep Deterministic Policy Gradient (DDPG) is introduced as its solution. Simulation results demonstrate that the algorithm performs well in minimizing network energy consumption and maximizing the cumulative rewards for the DDPG Agent.

Key words: Deep Deterministic Policy Gradient (DDPG) algorithm, Space-Air-Ground Integrated Network (SAGIN), edge computing, resource allocation, task segmentation

摘要：

在6G时代, 空天地一体化网络(SAGIN)可以为物联网(IoT)设备提供无处不在的覆盖, 能有效解决当前网络架构覆盖能力不足的问题。多接入边缘计算(MEC)是一种非常重要的技术, 可以进一步增强SAGIN的服务能力, 其中MEC在有效降低任务执行延迟和系统能耗方面表现出显著的能力。提出一种支持MEC的SAGIN架构, 其中卫星和多架无人机(UAV)作为边缘节点, 为IoT设备就近提供算力。通过IoT设备的任务分割以及UAV和卫星的带宽分配, 实现网络平均总能耗的最小化。将网络动态性问题表述为马尔可夫决策过程(MDP)问题, 提出一种基于深度确定性策略梯度(DDPG)的自适应决策算法对其进行求解。仿真结果表明, 该算法在最小化网络能耗和DDPG代理累计奖励最大化方面表现出良好的性能。

关键词: 深度确定性策略梯度算法, 空天地一体化网络, 边缘计算, 资源分配, 任务分割

DU Jianbo, DONG Weizhe, JIN Rong, WANG Junxuan, KANG Jiawen, LIU Lei, Celimuge Wu. Task Segmentation and Resource Allocation in MEC-Based Space-Air-Ground Integrated Networks[J]. Computer Engineering, 2025, 51(5): 43-51.

杜剑波, 董伟哲, 金蓉, 王军选, 康嘉文, 刘雷, 策力木格. 基于MEC的空天地一体化网络任务分割与资源分配[J]. 计算机工程, 2025, 51(5): 43-51.

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Figures/Tables 6

Fig.1 UAV-supported SAGIN architecture

Fig.2 Relationship between rewards and satellite propagation delay

Fig.3 Relationship between energy consumption and satellite propagation delay

Fig.4 Relationship between rewards and local device processing capability

Fig.5 Relationship between energy consumption and local device processing capability

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