A self-attention based dynamic resource management for satellite-terrestrial networks

Tianhao Lin, Zhiyong Luo
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Abstract

The satellite-terrestrial networks possess the ability to transcend geographical constraints inherent in traditional communication networks, enabling global coverage and offering users ubiquitous computing power support, which is an important development direction of future communications. In this paper, we take into account a multi-scenario network model under the coverage of low earth orbit (LEO) satellite, which can provide computing resources to users in faraway areas to improve task processing efficiency. However, LEO satellites experience limitations in computing and communication resources and the channels are time-varying and complex, which makes the extraction of state information a daunting task. Therefore, we explore the dynamic resource management issue pertaining to joint computing, communication resource allocation and power control for multi-access edge computing (MEC). In order to tackle this formidable issue, we undertake the task of transforming the issue into a Markov decision process (MDP) problem and propose the self-attention based dynamic resource management (SABDRM) algorithm, which effectively extracts state information features to enhance the training process. Simulation results show that the proposed algorithm is capable of effectively reducing the long-term average delay and energy consumption of the tasks.
基于自我关注的卫星-地面网络动态资源管理
卫星-地面网络能够超越传统通信网络固有的地理限制,实现全球覆盖,为用户提供无处不在的计算能力支持,是未来通信的重要发展方向。本文考虑了低地轨道(LEO)卫星覆盖下的多场景网络模型,该模型可以为远距离地区的用户提供计算资源,提高任务处理效率。然而,低地轨道卫星的计算和通信资源有限,信道时变且复杂,这使得状态信息的提取成为一项艰巨的任务。因此,我们探讨了与多接入边缘计算(MEC)的联合计算、通信资源分配和功率控制有关的动态资源管理问题。为了解决这一难题,我们将该问题转化为马尔可夫决策过程(MDP)问题,并提出了基于自我关注的动态资源管理(SABDRM)算法,该算法可有效提取状态信息特征,从而增强训练过程。仿真结果表明,所提出的算法能够有效降低任务的长期平均延迟和能耗。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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