Context-Aware Proactive Content Caching with Service Differentiation in Wireless Networks

TL;DR

This paper introduces a novel context-aware proactive caching algorithm for wireless networks that learns user preferences online, adapts cache content dynamically, and supports service differentiation, significantly improving cache hit rates.

Contribution

The paper presents a new online learning algorithm for context-aware caching that converges to optimal content placement and enables service differentiation in wireless networks.

Findings

Achieves at least 14% increase in cache hits over existing algorithms.

Provides a sublinear regret bound demonstrating convergence to optimal caching.

Supports prioritization of different user groups in cache management.

Abstract

Content caching in small base stations or wireless infostations is considered to be a suitable approach to improve the efficiency in wireless content delivery. Placing the optimal content into local caches is crucial due to storage limitations, but it requires knowledge about the content popularity distribution, which is often not available in advance. Moreover, local content popularity is subject to fluctuations since mobile users with different interests connect to the caching entity over time. Which content a user prefers may depend on the user's context. In this paper, we propose a novel algorithm for context-aware proactive caching. The algorithm learns context-specific content popularity online by regularly observing context information of connected users, updating the cache content and observing cache hits subsequently. We derive a sublinear regret bound, which characterizes the learning speed and proves that our algorithm converges to the optimal cache content placement strategy in terms of maximizing the number of cache hits. Furthermore, our algorithm supports service differentiation by allowing operators of caching entities to prioritize customer groups. Our numerical results confirm that our algorithm outperforms state-of-the-art algorithms in a real world data set, with an increase in the number of cache hits of at least 14%.