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

2026-03-07

AI Summary

This work addresses the limitations of conventional caching strategies that rely on accurate file popularity estimation, which often fail under practical constraints such as unknown and non-uniform popularity distributions, small user populations, limited cache capacity, or exploratory requests introducing noise. To overcome these challenges, the paper reframes coded caching as a file ranking problem and introduces a relative-frequency-based grouped caching mechanism. Inspired by the TopRank algorithm from recommendation systems and multi-armed bandits, the proposed approach eschews absolute popularity estimation in favor of online relative ranking based on observed request frequency differences. This design aligns more closely with the intrinsic characteristics of coded caching, effectively mitigating the impact of observation noise and resource constraints. The method achieves sublinear regret and demonstrates significantly improved caching efficiency and content delivery performance over existing schemes in typical constrained scenarios.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Search and Optimization: Distributed Search
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• User Modeling, Personalization and Recommendation: Personalized content ranking and presentation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• coded caching
• non-uniform demands
• delivery rate optimization
• file popularity
• unknown popularity distribution

Innovation

• coded caching
• non-uniform demands
• TopRank
• multi-armed bandits
• popularity ranking