Deep Reinforcement Learning-Based Edge Caching in Heterogeneous Networks


Yoonjeong Choi, Yujin Lim, Journal of Information Processing Systems Vol. 18, No. 6, pp. 803-812, Dec. 2022  

https://doi.org/10.3745/JIPS.03.0180
Keywords: edge caching, Heterogeneous networks, Reinforcement Learning
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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.


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Cite this article
[APA Style]
Choi, Y. & Lim, Y. (2022). Deep Reinforcement Learning-Based Edge Caching in Heterogeneous Networks. Journal of Information Processing Systems, 18(6), 803-812. DOI: 10.3745/JIPS.03.0180.

[IEEE Style]
Y. Choi and Y. Lim, "Deep Reinforcement Learning-Based Edge Caching in Heterogeneous Networks," Journal of Information Processing Systems, vol. 18, no. 6, pp. 803-812, 2022. DOI: 10.3745/JIPS.03.0180.

[ACM Style]
Yoonjeong Choi and Yujin Lim. 2022. Deep Reinforcement Learning-Based Edge Caching in Heterogeneous Networks. Journal of Information Processing Systems, 18, 6, (2022), 803-812. DOI: 10.3745/JIPS.03.0180.