AI技术在卫星通信/互联网领域的应用综述

doi:10.11959/j.issn.1000-0801.2023030

电信科学 ›› 2023, Vol. 39 ›› Issue (2): 10-24.doi: 10.11959/j.issn.1000-0801.2023030

• 专题：6G空间互联网 • 上一篇下一篇

AI技术在卫星通信/互联网领域的应用综述

刘雅琼¹^,²^,³, 吕哲¹^,²^,³, 赵亚飞³, 寿国础¹^,²^,³

¹ 先进信息网络北京实验室，北京 100876
² 网络体系构建与融合北京市重点实验室，北京 100876
³ 北京邮电大学信息与通信工程学院，北京 100876

修回日期:2023-02-04 出版日期:2023-02-20 发布日期:2023-02-01
作者简介:刘雅琼（1988- ），女，博士，北京邮电大学信息与通信工程学院副教授，主要研究方向为边缘智能、车联网、卫星通信
吕哲（2000- ），女，北京邮电大学硕士生，主要研究方向为知识图谱、网络运维和边缘智能
赵亚飞（1987- ），男，博士，北京邮电大学信息与通信工程学院特聘副研究员，主要研究方向为低轨卫星通信导航一体化
寿国础（1965- ），男，博士，北京邮电大学信息与通信工程学院教授，主要研究方向为接入网络与边缘计算、光纤与无线网络虚拟化、网络构建与路由、移动互联网与应用等
基金资助:
北京市科技计划项目(Z221100007722012)

A survey on AI techniques applied in the satellite communication/satellite Internet field

Yaqiong LIU¹^,²^,³, Zhe LYU¹^,²^,³, Yafei ZHAO³, Guochu SHOU¹^,²^,³

¹ Beijing Laboratory of Advanced Information Networks, Beijing 100876, China
² Beijing Key Laboratory of Network System Architecture and Convergence, Beijing 100876, China
³ School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China

Revised:2023-02-04 Online:2023-02-20 Published:2023-02-01
Supported by:
Beijing Municipal Science and Technology Project(Z221100007722012)

摘要/Abstract

摘要：

卫星互联网的诞生带来新的发展机遇，同时也具备诸多挑战。主要调研了在空天地一体化的背景下，人工智能（AI）技术作为重要的辅助工具在卫星通信/卫星互联网领域的应用，涉及通信抗干扰、通信路由、星地网络系统架构、星座运营与管理等场景，其中涉及的AI算法包括传统机器学习、深度学习、强化学习等；最后，针对 AI 技术在卫星通信/互联网领域的发展趋势，提出了未来的研究方向，为我国卫星领域的智能化发展提供了思路和技术方案。

关键词: 卫星通信, 空天地一体化, 卫星互联网, AI算法, 机器学习

Abstract:

The birth of satellite Internet brings new development opportunities, but also many challenges.How artificial intelligence, as an important auxiliary tool, was widely used in the field of satellite communication/satellite Internet in the context of the development of space-air-ground integration, was investigated, which involved communication anti-jamming, communication routing, satellite-terrestrial network system architecture, constellation operation and management and other scenarios.The AI algorithms included traditional machine learning, deep learning, reinforcement learning and so on.Finally, by taking the development trend of the AI applied in the satellite field into consideration, several future research directions were put forward, which provided new ideas and technical solutions for the intelligent development of satellite field in our country.

Key words: satellite communication, space-air-ground integration, satellite Internet, AI algorithm, machine learning

中图分类号:

TN927

刘雅琼, 吕哲, 赵亚飞, 寿国础. AI技术在卫星通信/互联网领域的应用综述[J]. 电信科学, 2023, 39(2): 10-24.

Yaqiong LIU, Zhe LYU, Yafei ZHAO, Guochu SHOU. A survey on AI techniques applied in the satellite communication/satellite Internet field[J]. Telecommunications Science, 2023, 39(2): 10-24.

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智能化内容/目标	使用的AI方法	代表性文献
实现对卫星通信干扰的自主感知	基于DL与干扰感知的AI技术	文献[15]
	LSTM
针对音频干扰、窄带干扰、扫频干扰及其两两叠加的复合	RL	文献[16]
干扰的智能抗干扰决策
负载均衡最优路径	基于集中式SDN的TRPO	文献[17]
自适应多路径路由发现	PIMTS	文献[18]
	DDPG
信道资源调度	智能水滴算法	文献[19]
识别通信信号调制方式	ResNet50	文献[20]
优化自适应编码和调制	在线随机回归森林ORRF	文献[21]
卫星信道中信号失真问题	RL	文献[22]
	多目标强化和自适应神经网络
多波束卫星信道分配、功率分配和跳波束算法	DRL	文献[23]
6G卫星通信网络	AI赋能技术	文献[24]
多层卫星通信	快速天线指向算法	文献[25]
	智能改进的DRL算法
低轨卫星物理层认证	ResNet18	文献[26]
计算卸载	深度确定性策略梯度方法	文献[27]
卫星表面充电通信	ML	文献[28]
多波束高吞吐量卫星的动态功率分配	遗传算法、模拟退火算法、粒子群优化	文献[29]
	DRL
降低带宽下行链路成本	DNN	文献[30]

场景	AI方法	优势
资源分配等需要考虑用户空间分布的问题	CNN	感知二维空间数据
预测问题，如用户流量预测、信道质量预测、异常预测	RNN	感知时序数据
学习卫星间拓扑关系，用户间、卫星与用户间的图关系	图神经网络	感知图关系
对计算效率要求高的场景，如核心网数据流异常检测	SVM	模型复杂度低
网络运维数据的高维特征降维；网络运行状态异常检测	自编码器	高维数据降维
解决数据样本不足问题或用鉴别器检测异常	生成式神经网络	生成模型，可应对小样本数据问题
动态网络环境中序列决策问题，如资源分配、流量调度	Q学习、深度Q学习	解决无模型环境中的序列决策问题
大规模网络中节点分布式控制资源分配、流量调度等	多智能体强化学习	各智能体独立决策
网络中标签训练数据不足的问题	迁移学习	所需训练样本少

AI技术在卫星通信/互联网领域的应用综述

A survey on AI techniques applied in the satellite communication/satellite Internet field

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