Reinforcement learning for caching with space-time popularity dynamics

Alireza Sadeghi, Georgios B. Giannakis, Gang Wang, Fatemeh Sheikholeslami

Research output: Chapter in Book/Report/Conference proceedingChapter


With the tremendous growth of data traffic over wired and wireless networks along with the increasing number of rich-media applications, caching is envisioned to play a critical role in next-generation networks. To intelligently prefetch and store contents, a cache node should be able to learn what and when to cache. Considering the geographical and temporal content popularity dynamics, the limited available storage at cache nodes, as well as the interactive influence of caching decisions in networked caching settings, developing effective caching policies is practically challenging. In response to these challenges, this chapter presents a versatile reinforcement learning-based approach for near-optimal caching policy design, in both single-node and network caching settings under dynamic space-time popularities. The policies presented here are complemented using a set of numerical tests, which showcase the merits of the presented approach relative to several standard caching policies.

Original languageEnglish (US)
Title of host publicationEdge Caching for Mobile Networks
PublisherInstitution of Engineering and Technology
Number of pages27
ISBN (Electronic)9781839531224
ISBN (Print)9781839531231
StatePublished - Jan 1 2022

Bibliographical note

Publisher Copyright:
© The Institution of Engineering and Technology 2022.


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