Code, Cache and Deliver on the Move: A Novel Caching Paradigm in Hyper-Dense Small-Cell Networks

Caching popular content files at small-cell base stations (SBSs) has emerged as a promising technique to meet the overwhelming growth in mobile data demand. Despite the plethora of work in this field, a specific aspect has been overlooked. It is assumed that all users remain stationary during data transfer and therefore a complete copy of the requested file can always be downloaded by the associated SBSs. In this work, we revisit the caching problem in realistic environments where moving users intermittently connect to multiple SBSs encountered at different times. Due to connection duration limits, users may download only parts of the requested files. Requests for files that failed to be delivered on time by the SBSs are redirected to the coexisting macro-cell. We introduce an optimization framework that models user movements via random walks on a Markov chain aimed at minimizing the load of the macro-cell. As the main contribution, we put forward a distributed caching paradigm that leverages user mobility predictions and innovative information-mixing methods based on the principle of network coding. Systematic experiments based on measured traces of human mobility patterns demonstrate that our approach can offload $65 \text{percent}$ more macro-cell traffic than existing caching schemes in realistic settings.

1 Introduction

We are witnessing an unprecedented worldwide growth of mobile data traffic that is expected to continue at an annual rate of \$45\text{percent}\$ over the next years, surpassing \$30\$ exabytes per month by 2020 [2]. To manage this “data-tsunami”, operators deploy small-cell base stations (SBSs), such as pico-cells and femto-cells, that work in conjunction with conventional macro-cell base stations (MBSs), forming the so-called Heterogeneous Cellular Networks (HCNs)  [3]. SBSs increase area spectral efficiency and serve the users via short-range energy-prudent communication links.