A Reinforcement-Learning Approach to Proactive Caching in Wireless Networks

TL;DR

This paper develops a reinforcement learning-based proactive caching strategy for wireless networks that minimizes energy costs by dynamically managing cache contents based on channel conditions and user behavior.

Contribution

It introduces a threshold-based caching scheme optimized via reinforcement learning, addressing the intractability of exact threshold computation in dynamic environments.

Findings

Proposed schemes outperform reactive downloading significantly.

Near-optimal performance close to genie-aided lower bounds.

Dynamic caching adapts effectively to channel and user behavior.

Abstract

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.