JIPS (한국정보처리학회)
Current Result Document :
| 한글제목(Korean Title) |
Deep Reinforcement Learning-Based Edge Caching in Heterogeneous Networks |
| 영문제목(English Title) |
Deep Reinforcement Learning-Based Edge Caching in Heterogeneous Networks |
| 저자(Author) |
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| 원문수록처(Citation) |
VOL 18 NO. 06 PP. 0803 ~ 0812 (2022. 12) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
With the increasing number of mobile device users worldwide, utilizing mobile edge computing (MEC) devices close to users for content caching can reduce transmission latency than receiving content from a server or cloud. However, because MEC has limited storage capacity, it is necessary to determine the content types and sizes to be cached. In this study, we investigate a caching strategy that increases the hit ratio from small base stations (SBSs) for mobile users in a heterogeneous network consisting of one macro base station (MBS) and multiple SBSs. If there are several SBSs that users can access, the hit ratio can be improved by reducing duplicate content and increasing the diversity of content in SBSs. We propose a Deep Q-Network (DQN)-based caching strategy that considers time-varying content popularity and content redundancy in multiple SBSs. Content is stored in the SBS in a divided form using maximum distance separable (MDS) codes to enhance the diversity of the content. Experiments in various environments show that the proposed caching strategy outperforms the other methods in terms of hit ratio. |
| 키워드(Keyword) |
Edge Caching
Heterogeneous Networks
Reinforcement Learning
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| 파일첨부 |
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