Deep Q-Learning Aided Networking, Caching, and Computing Resources Allocation in Software-Defined Satellite-Terrestrial Networks

被引：207

作者：

Qiu, Chao ^{[1
]}

Yao, Haipeng ^{[2
]}

Yu, F. Richard ^{[3
]}

Xu, Fangmin ^{[1
]}

Zhao, Chenglin ^{[1
]}

机构：

[1] Beijing Univ Posts & Telecommun, Key Lab Univ Commun, Beijing 100876, Peoples R China

[2] Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing 100876, Peoples R China

[3] Carleton Univ, Ottawa, ON K1S 5B6, Canada

来源：

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY | 2019年 / 68卷 / 06期

基金：

中国国家自然科学基金;

关键词：

Satellite-terrestrial networks; software-defined networking (SDN); network virtualization; caching; edge computing; resources allocation; deep Q-learning; GROUND INTEGRATED NETWORK; BROAD-BAND; ARCHITECTURE; MANAGEMENT; STRATEGY; SERVICE;

D O I：

10.1109/TVT.2019.2907682

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

With the development of satellite networks, there is an emerging trend to integrate satellite networks with terrestrial networks, called satellite-terrestrial networks (STNs). The improvements of STNs need innovative information and communication technologies, such as networking, caching, and computing. In this paper, we propose a software-defined STN to manage and orchestrate networking, caching, and computing resources jointly. We formulate the joint resources allocation problem as a joint optimization problem, and use a deep Q-learning approach to solve it. Simulation results show the effectiveness of our proposed scheme.

引用

页码：5871 / 5883

页数：13

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