User Preference Learning-Aided Collaborative Edge Caching for Small Cell Networks

TL;DR

This paper introduces an LSTM-based model for predicting user content preferences in edge caching, enabling dynamic and collaborative caching strategies to reduce sharing costs in small cell networks.

Contribution

It proposes a novel LSTM-based preference prediction model combined with a collaborative caching optimization approach for dynamic edge networks.

Findings

The LSTM model accurately captures temporal user preferences.

Collaborative caching reduces content sharing costs.

The proposed algorithm outperforms existing schemes in simulations.

Abstract

While next-generation wireless communication networks intend leveraging edge caching for enhanced spectral efficiency, quality of service, end-to-end latency, content sharing cost, etc., several aspects of it are yet to be addressed to make it a reality. One of the fundamental mysteries in a cache-enabled network is predicting what content to cache and where to cache so that high caching content availability is accomplished. For simplicity, most of the legacy systems utilize a static estimation - based on Zipf distribution, which, in reality, may not be adequate to capture the dynamic behaviors of the contents popularities. Forecasting user's preferences can proactively allocate caching resources and cache the needed contents, which is especially important in a dynamic environment with real-time service needs. Motivated by this, we propose a long short-term memory (LSTM) based sequential model that is capable of capturing the temporal dynamics of the users' preferences for the available contents in the content library. Besides, for a more efficient edge caching solution, different nodes in proximity can collaborate to help each other. Based on the forecast, a non-convex optimization problem is formulated to minimize content sharing costs among these nodes. Moreover, a greedy algorithm is used to achieve a sub-optimal solution. By using mathematical analysis and simulation results, we validate that the proposed algorithm performs better than other existing schemes.