TopRank-Based Delivery Rate Optimization for Coded Caching under Non-Uniform Demands

TL;DR

This paper introduces a ranking-based coded caching method that adapts to unknown, non-uniform file popularities, outperforming previous algorithms especially in small or noisy data scenarios.

Contribution

It proposes a novel ranking and grouping approach inspired by multi-armed bandits, improving coded caching performance without explicit popularity estimation.

Findings

Outperforms previous algorithms in small networks

Achieves sublinear regret in learning popularity

Better handles noisy or synthetic request data

Abstract

We study the problem of coded caching with nonuniform file popularity under the setting where the popularity distribution is initially unknown. By reframing the problem, we propose a method inspired by an algorithm from the recommender-systems literature and multi-armed bandits. Unlike prior approaches, which focus on accurately estimating file popularities, our method ranks files relative to one another and partitions them into groups. This perspective is more consistent with the structure of prior approaches as well, since earlier methods also divided files into popular and non-popular groups after estimating their popularities. The proposed approach relies on differences in request counts between files as the basis for ranking, and under many conditions it outperforms the previous algorithm. In particular, we obtain significantly improved performance in scenarios where the number of users in the network is small, the cache storage capacity is limited, or the learning process of the true popularity of files based on observations is contaminated by exploratory or synthetic requests that do not match the true popularity distribution. In these cases, our policy achieves markedly better performance and attains sublinear regret.