Context-Aware Data Caching for 5G Heterogeneous Small Cells Networks

In this work, we investigate the problem of context-aware data caching in the heterogeneous small cell networks (HSCNs) to provide satisfactory to the end-users in reducing the service latency. In particular, we explore the storage capability of base stations (BSs) in HSCNs and propose a data caching model consists of edge caching elements (CAEs), small cell base stations (SBSs), and macro cell BS (MBS). Then, we concentrate on how to efficiently match the data contents to the different cache entities in order to minimize the overall system service latency. We model it as a distributed college admission (CA) stable matching problem and tackle this issue by utilizing contextual information to generate the preference lists of cache entities and contents, respectively. In the CA model, we leverage the resident-oriented Gale-Shapley (RGS) algorithm to find a stable matching between the contents and the cache entities. Through numerical results, we illustrate the advantages of of our proposed methods.