Caching Policy for Cache-enabled D2D Communications by Learning User Preference

TL;DR

This paper proposes a caching policy for cache-enabled D2D communications that leverages individual user preferences learned through probabilistic models, significantly improving offloading probability over traditional content popularity-based policies.

Contribution

It introduces a novel approach to model and learn user preferences for caching, optimizing caching policies accordingly, and demonstrates substantial performance gains through simulations.

Findings

User preferences are diverse and change slowly over time.

The proposed learning algorithm reduces training time.

Caching with user preference outperforms content popularity-based caching.

Abstract

Prior works in designing caching policy do not distinguish content popularity with user preference. In this paper, we illustrate the caching gain by exploiting individual user behavior in sending requests. After showing the connection between the two concepts, we provide a model for synthesizing user preference from content popularity. We then optimize the caching policy with the knowledge of user preference and active level to maximize the offloading probability for cache-enabled device-to-device communications, and develop a low-complexity algorithm to find the solution. In order to learn user preference, we model the user request behavior resorting to probabilistic latent semantic analysis, and learn the model parameters by expectation maximization algorithm. By analyzing a Movielens dataset, we find that the user preferences are less similar, and the active level and topic preference of each user change slowly over time. Based on this observation, we introduce a prior knowledge based learning algorithm for user preference, which can shorten the learning time. Simulation results show remarkable performance gain of the caching policy with user preference over existing policy with content popularity, both with realistic dataset and synthetic data validated by the real dataset.