Proactive Caching and Bandwidth Allocation in Heterogenous Networks by Learning From Historical Numbers of Requests

Abstract

Proactive caching at base stations (BSs) has been shown promising in offloading traffic, where most priori works consider full frequency reuse among cells and assume known file popularity. To facilitate proactive caching, recent works either adopt linear models or shallow neural networks to predict popularity, without explaining the rationale. In this paper, we consider cache-enabled multi-tier heterogenous networks. To show if full frequency reuse is superior, we consider underlay and overlay modes with random bandwidth allocation for interference management. After optimizing the caching policy and bandwidth allocation to maximize successful offloading probability, we show that the overlay mode outperforms the underlay mode. To confirm that complex non-linear prediction models are unnecessary for proactive caching when using the historical numbers of requests, we employ several linear and non-linear models to predict content popularity and user density, using MovieLens and Youku datasets. To interpret why the caching performance achieved by the optimized policies with the information predicted by the linear models is close to that using non-linear models, we prove that deterministic popularity with typical profiles can be predicted with linear models. Then, we show that most popular files are with these profiles in the real dataset.