Bandwidth and Storage Efficient Caching Based on Dynamic Programming and Reinforcement Learning

被引：4
|
作者：
Lin, Zhiyuan [1 ,2 ]
Huang, Wei [1 ,2 ,3 ]
Chen, Wei [1 ,2 ]
机构：
[1] Tsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R China
[2] Tsinghua Univ, Beijing Natl Res Ctr Informat Sci & Technol, Beijing 100084, Peoples R China
[3] PLA, Unit 96946, Beijing, Peoples R China
来源：
IEEE WIRELESS COMMUNICATIONS LETTERS | 2020年 / 9卷 / 02期
基金：
中国国家自然科学基金; 北京市自然科学基金;
关键词：
Delays; Dynamic programming; Reinforcement learning; Buffer storage; Bandwidth; Next generation networking; Base stations; Proactive caching; profit maximization; dynamic programming; reinforcement learning;
D O I：
10.1109/LWC.2019.2948337
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Proactive caching holds the promise of coping with the explosively increasing traffic demands of next generation mobile networks. However, it may also incur extra pushing and storage costs for base stations (BSs). By taking these costs into account, we are interested in maximizing the average profit for the BS. A joint pushing and caching (JPC) approach is investigated to determine whether and when to push and how long a file should be cached at the user's buffer. More specifically, we present a dynamic programming based JPC for the BS that knows the distributions of user's request times for content files. When user's request time distributions are unknown in priori, we adopt a reinforcement learning algorithm to achieve high bandwidth and storage efficiency.
引用
收藏
页码：206 / 209
页数：4
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