# Caching Contents with Varying Popularity using Restless Bandits

arXiv: https://arxiv.org/abs/2212.03291 · HTML: https://mlsanity.com/paper/2212.03291/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 32 references that are in the map.
- First posted on arXiv 31 Oct 2022.

## Builds on (most cited first, 5 of 5)

- [Whittle index based Q-learning for restless bandits with average reward](https://mlsanity.com/paper/2004.14427/) (2020)
- [Optimal and Scalable Caching for 5G Using Reinforcement Learning of Space-time Popularities](https://mlsanity.com/paper/1708.06698/) (2017)
- [Temporal Locality in Today's Content Caching: Why it Matters and How to Model it](https://mlsanity.com/paper/1305.7114/) (2013)
- [On the Whittle Index for Restless Multi-armed Hidden Markov Bandits](https://mlsanity.com/paper/1603.04739/) (2016)
- [The Design of Dynamic Probabilistic Caching with Time-Varying Content Popularity](https://mlsanity.com/paper/2002.06251/) (2020)

## Abstract (arXiv)

Mobile networks are experiencing prodigious increase in data volume and user density , which exerts a great burden on mobile core networks and backhaul links. An efficient technique to lessen this problem is to use caching i.e. to bring the data closer to the users by making use of the caches of edge network nodes, such as fixed or mobile access points and even user devices. The performance of a caching depends on contents that are cached. In this paper, we examine the problem of content caching at the wireless edge(i.e. base stations) to minimize the discounted cost incurred over infinite horizon. We formulate this problem as a restless bandit problem, which is hard to solve. We begin by showing an optimal policy is of threshold type. Using these structural results, we prove the indexability of the problem, and use Whittle index policy to minimize the discounted cost.