Dynamic Access Task Scheduling of LEO Constellation Based on Space-Based Distributed Computing

IF 1.9 3区 计算机科学 Q3 AUTOMATION & CONTROL SYSTEMS Journal of Systems Engineering and Electronics Pub Date : 2024-08-21 DOI:10.23919/jsee.2024.000071
Wei Liu, Yifeng Jin, Lei Zhang, Zihe Gao, Ying Tao
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Abstract

A dynamic multi-beam resource allocation algorithm for large low Earth orbit (LEO) constellation based on on-board distributed computing is proposed in this paper. The allocation is a combinatorial optimization process under a series of complex constraints, which is important for enhancing the matching between resources and requirements. A complex algorithm is not available because that the LEO on-board resources is limited. The proposed genetic algorithm (GA) based on two-dimensional individual model and uncorrelated single paternal inheritance method is designed to support distributed computation to enhance the feasibility of on-board application. A distributed system composed of eight embedded devices is built to verify the algorithm. A typical scenario is built in the system to evaluate the resource allocation process, algorithm mathematical model, trigger strategy, and distributed computation architecture. According to the simulation and measurement results, the proposed algorithm can provide an allocation result for more than 1 500 tasks in 14 s and the success rate is more than 91% in a typical scene. The response time is decreased by 40% compared with the conditional GA.
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基于天基分布式计算的低地轨道星座动态访问任务调度
本文提出了一种基于星载分布式计算的大型低地球轨道(LEO)星座动态多波束资源分配算法。资源分配是在一系列复杂约束条件下的组合优化过程,对于提高资源与需求的匹配度非常重要。由于低地轨道星载资源有限,因此无法采用复杂的算法。本文提出的遗传算法(GA)基于二维个体模型和非相关单父系遗传方法,旨在支持分布式计算,提高星载应用的可行性。为验证该算法,构建了一个由八个嵌入式设备组成的分布式系统。在系统中构建了一个典型场景,以评估资源分配过程、算法数学模型、触发策略和分布式计算架构。根据仿真和测量结果,在典型场景中,所提出的算法能在 14 秒内提供超过 1 500 个任务的分配结果,成功率超过 91%。与条件遗传算法相比,响应时间缩短了 40%。
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来源期刊
Journal of Systems Engineering and Electronics
Journal of Systems Engineering and Electronics 工程技术-工程:电子与电气
CiteScore
4.10
自引率
14.30%
发文量
131
审稿时长
7.5 months
期刊介绍: Information not localized
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