A Learning-Based Approach to Caching in Heterogenous Small Cell Networks

TL;DR

This paper proposes a learning-based caching strategy for heterogeneous small cell networks, optimizing content placement based on demand estimation, with transfer learning to reduce training time and improve efficiency.

Contribution

It introduces a novel distributed caching approach that estimates content popularity and employs transfer learning to enhance caching efficiency in small cell networks.

Findings

Training time scales as N^2 without transfer learning.

Transfer learning reduces training time significantly.

Optimal caching minimizes offloading loss effectively.

Abstract

A heterogenous network with base stations (BSs), small base stations (SBSs) and users distributed according to independent Poisson point processes is considered. SBS nodes are assumed to possess high storage capacity and to form a distributed caching network. Popular files are stored in local caches of SBSs, so that a user can download the desired files from one of the SBSs in its vicinity. The offloading-loss is captured via a cost function that depends on the random caching strategy proposed here. The popularity profile of cached content is unknown and estimated using instantaneous demands from users within a specified time interval. An estimate of the cost function is obtained from which an optimal random caching strategy is devised. The training time to achieve an $\epsilon>0$ difference between the achieved and optimal costs is finite provided the user density is greater than a predefined threshold, and scales as $N^2$, where $N$ is the support of the popularity profile. A transfer learning-based approach to improve this estimate is proposed. The training time is reduced when the popularity profile is modeled using a parametric family of distributions; the delay is independent of $N$ and scales linearly with the dimension of the distribution parameter.