Proactive caching in cloud and D2D assisted multi-tier cellular networks

Abstract

Proactive caching in multi-tier cellular networks (MTCNs) is an efficient technique for alleviating heavy data traffic on backhaul links, thereby reducing content latency and increasing the overall cache hit ratio (CHR) and user satisfaction ratio (USR). The CHR and USR can be further enhanced by incorporating cache-enabled device-to-device (D2D) and cloud cache into MTCNs, forming cloud and D2D-assisted multi-tier cellular networks (CD2DMTCNs). In this context, we formulate two problems: the first is the joint optimization of the overall CHR and USR of the system, while the second focuses on minimizing the average content latency. The popularity prediction of newly published content using machine learning algorithms has become increasingly attractive, with support vector machines (SVMs) showing promising results in this regard. However, for improved performance, an uncertainty assessment is necessary. To address this, bootstrapping is applied to SVM to obtain confidence and prediction intervals. Additionally, K-means clustering is used to form virtual clusters within both D2D networks and MTCNs, facilitating content popularity prediction. This enables the maximization of CHR and USR through a clustered-bootstrapped support vector machine (clustered-BSVM). In addition, establishing criteria for content delivery is essential. Since multiple cache-enabled transmitters may store the content requested by users, only one transmitter can deliver the requested content at a time. This constraint is addressed using the branch-and-bound method of linear programming, which minimizes the average latency of the system. Numerical simulations demonstrate that proactive caching based on clustered-BSVM outperforms existing methods in terms of CHR and USR, while the branch-and-bound method effectively resolves the content delivery problem, minimizing system latency.