TopRank-Based Delivery Rate Optimization for Coded Caching under Non-Uniform Demands
This work provides an incremental improvement in coded caching performance for network operators and content providers, particularly in challenging network conditions.
This paper addresses coded caching with non-uniform and initially unknown file popularity by proposing a method that ranks files relatively and partitions them into groups. This approach outperforms previous algorithms, especially in scenarios with a small number of users, limited cache storage, or contaminated popularity learning, achieving sublinear regret.
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.