# A Reinforcement-Learning Approach to Proactive Caching in Wireless Networks

arXiv: https://arxiv.org/abs/1712.07084 · HTML: https://mlsanity.com/paper/1712.07084/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Dec 2017.

## Built on by (most cited first, 14 of 14)

- [Holistic Network Virtualization and Pervasive Network Intelligence for 6G](https://mlsanity.com/paper/2301.00519/) (2023)
- [Optimistic No-regret Algorithms for Discrete Caching](https://mlsanity.com/paper/2208.06414/) (2022)
- [Whittle Index based Q-Learning for Wireless Edge Caching with Linear Function Approximation](https://mlsanity.com/paper/2202.13187/) (2022)
- [Online Caching with Optimistic Learning](https://mlsanity.com/paper/2202.10590/) (2022)
- [Cocktail Edge Caching: Ride Dynamic Trends of Content Popularity with Ensemble Learning](https://mlsanity.com/paper/2101.05885/) (2021)
- [Online Caching with no Regret: Optimistic Learning via Recommendations](https://mlsanity.com/paper/2204.09345/) (2022)
- [Resource-Aware Hierarchical Federated Learning in Wireless Video Caching Networks](https://mlsanity.com/paper/2402.04216/) (2024)
- [Predictive Edge Caching through Deep Mining of Sequential Patterns in User Content Retrievals](https://mlsanity.com/paper/2210.02657/) (2022)
- [AoI-based Temporal Attention Graph Neural Network for Popularity Prediction and Content Caching](https://mlsanity.com/paper/2208.08606/) (2022)
- [Sustainable Edge Intelligence Through Energy-Aware Early Exiting](https://mlsanity.com/paper/2305.14094/) (2023)
- [Fundamental Limits of Reinforcement Learning in Environment with Endogeneous and Exogeneous Uncertainty](https://mlsanity.com/paper/2106.08477/) (2021)
- [From Traditional Adaptive Data Caching to Adaptive Context Caching: A Survey](https://mlsanity.com/paper/2211.11259/) (2022)
- [Reinforcement Learning Based Approaches to Adaptive Context Caching in Distributed Context Management Systems](https://mlsanity.com/paper/2212.11709/) (2022)
- [Graph Federated Learning Based Proactive Content Caching in Edge Computing](https://mlsanity.com/paper/2502.04760/) (2025)

## Abstract (arXiv)

We consider a mobile user accessing contents in a dynamic environment, where new contents are generated over time (by the user's contacts), and remain relevant to the user for random lifetimes. The user, equipped with a finite-capacity cache memory, randomly accesses the system, and requests all the relevant contents at the time of access. The system incurs an energy cost associated with the number of contents downloaded and the channel quality at that time. Assuming causal knowledge of the channel quality, the content profile, and the user-access behavior, we model the proactive caching problem as a Markov decision process with the goal of minimizing the long-term average energy cost. We first prove the optimality of a threshold-based proactive caching scheme, which dynamically caches or removes appropriate contents from the memory, prior to being requested by the user, depending on the channel state. The optimal threshold values depend on the system state, and hence, are computationally intractable. Therefore, we propose parametric representations for the threshold values, and use reinforcement-learning algorithms to find near-optimal parametrizations. We demonstrate through simulations that the proposed schemes significantly outperform classical reactive downloading, and perform very close to a genie-aided lower bound.