Implicit Coordination of Caches in Small Cell Networks under Unknown Popularity Profiles
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Abstract (arXiv)
We focus on a dense cellular network, in which a limited-size cache is available at every Base Station (BS). In order to optimize the overall performance of the system in such scenario, where a significant fraction of the users is covered by several BSs, a tight coordination among nearby caches is needed. To this end, this pape introduces a class of simple and fully distributed caching policies, which require neither direct communication among BSs, nor a priori knowledge of content popularity. Furthermore, we propose a novel approximate analytical methodology to assess the performance of interacting caches under such policies. Our approach builds upon the well known characteristic time approximation and provides predictions that are surprisingly accurate (hardly distinguishable from the simulations) in most of the scenarios. Both synthetic and trace-driven results show that the our caching policies achieve excellent performance (in some cases provably optimal). They outperform state-of-the-art dynamic policies for interacting caches, and, in some cases, also the greedy content placement, which is known to be the best performing polynomial algorithm under static and perfectly-known content popularity profiles.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 5 Apr 2018.
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Citing papers per year: 2021: 1; 2022: 3; 2023: 0; 2024: 0; 2025: 1.
5 papers.
- 2021No-Regret Caching via Online Mirror Descentcited by 12
- 2022Optimistic No-regret Algorithms for Discrete Cachingcited by 10
- 2022Online Caching with Optimistic Learningcited by 7
- 2022Online Caching with no Regret: Optimistic Learning via Recommendationscited by 4
- 2025Efficient and Optimal No-Regret Caching under Partial Observationcited by 1
- 2025Efficient and Optimal No-Regret Caching under Partial Observation4 Mar 2025
- 2022Optimistic No-regret Algorithms for Discrete Caching15 Aug 2022
- 2022Online Caching with no Regret: Optimistic Learning via Recommendations20 Apr 2022
- 2022Online Caching with Optimistic Learning22 Feb 2022
- 2021No-Regret Caching via Online Mirror Descent29 Jan 2021