基于深度學習的低軌衛(wèi)星互聯(lián)網(wǎng)建設研究

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摘要：為提升衛(wèi)星通信的效率和質(zhì)量，該文聚焦低軌衛(wèi)星互聯(lián)網(wǎng)，運用深度學習理論，創(chuàng)新性地提出一種低軌衛(wèi)星網(wǎng)絡算法。通過明確系統(tǒng)運行問題，構(gòu)建多智能體算法系統(tǒng)，以優(yōu)化低軌衛(wèi)星互聯(lián)網(wǎng)建設，滿足高質(zhì)量通信需求，為衛(wèi)星通信行業(yè)發(fā)展提供新思路與經(jīng)驗借鑒。

關鍵詞：深度學習；衛(wèi)星通信；低軌衛(wèi)星；互聯(lián)網(wǎng)建設

doi：10.3969/J.ISSN.1672-7274.2024.12.007

中圖分類號：TN 927+.2；TP 393.4          文獻標志碼：A            文章編碼：1672-7274（2024）12-00-03

Application Research on Low-Orbit Satellite Internet Construction Based on Deep Learning

GUO Xiangliang WEI Chuanqi ZHANG Shi

（1. China Academy of Information and Communications Technology， Beijing 100191， China;

2. Landspace Technology Corporation， Beijing 100176， China）

Abstract： To enhance the efficiency and quality of satellite communications， this paper focuses on low-orbit satellite internet and innovatively proposes a low-orbit satellite network algorithm using deep learning theory. By identifying system operational issues and constructing a multi-agent algorithm system， we aim to optimize the construction of low-orbit satellite internet to meet high-quality communication demands， providing new insights and experiences for the development of the satellite communication industry.

Keywords： deep learning; satellite communication; low-orbit satellite; Internet construction

0   引言

隨著物聯(lián)網(wǎng)技術的蓬勃發(fā)展，衛(wèi)星通信網(wǎng)絡中的用戶終端節(jié)點數(shù)量急劇增加，這一趨勢正有力推動著衛(wèi)星通信行業(yè)的快速進步。（剩余4182字）