一种优化空天地一体化网络吞吐量算法

杜丹冰

doi:10.15892/j.cnki.djzdxb.2023.05.017

弹箭与制导学报 >

2023 , Vol. 43 >Issue 5: 109 - 114

DOI: https://doi.org/10.15892/j.cnki.djzdxb.2023.05.017

一种优化空天地一体化网络吞吐量算法

杜丹冰

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长春师范大学教育学院,吉林长春 130032

杜丹冰(1980—),女,副教授,硕士,研究方向:人工智能。

收稿日期: 2022-06-22

网络出版日期: 2024-12-30

基金资助

2021年吉林省社会科学基金项目(2021C94)

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A Throughput Optimization in Space-air-ground Integrated Networks

DU Danbing

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College of Education, Changchun Normal University, Changchun 130032, Jilin, China

Received date: 2022-06-22

Online published: 2024-12-30

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摘要

为提高空天地一体化网络的吞吐量,提出基于强化学习的链路优化算法(reinforcement learning-based link optimization, RLLO)。RLLO算法以近地轨道卫星为基站提供回程链路。同时,RLLO算法通过管理无人机和微基站的资源以及优化无人机(unmanned aerial vehicles, UAV)的轨迹,提升吞吐量。先建立优化回程链路和接入链路的目标问题,再利用多臂老虎机的强化学习工具求解目标问题。仿真结果表明,相比于同类的基准算法,RLLO算法提高了吞吐量和用户端的可达速率。

关键词： 空天地一体化网络; 近地轨道卫星; 强化学习; 接入链路; 多臂老虎机

本文引用格式

杜丹冰 . 一种优化空天地一体化网络吞吐量算法[J]. 弹箭与制导学报, 2023 , 43(5) : 109 -114 . DOI: 10.15892/j.cnki.djzdxb.2023.05.017

Abstract

In order to improve throughput of Space-Air-Ground Integrated Networks, Reinforcement Learning-based Link Optimization (RLLO) algorithm is proposed in this paper. In RLLO algorithm, we consider low Earth orbit satellites as an effective backhaul solution. For access links, we manage the radio resource among UAVs and small cell base stations and optimize the trajectories of unmanned Aerial Vehicles in order to improve the throughput. The objective problem of backhaul and access link is constructed. Then, we utilize the tools of reinforcement, and proposed approach based on the multi-armed bandit algorithm. Simulation results show that the proposed RLLO algorithm improve the throughput and rate of user.

Key words： space-air-ground integrated networks; low earth orbit; reinforcement learning; access link; multi-armed bandit

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参考文献

原文顺序 | 文献年度倒序 | 文中引用次数倒序

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