Gibbsian On-Line Distributed Content Caching Strategy for Cellular Networks

Abstract

In this paper, we develop Gibbs sampling-based techniques for learning the optimal placement of contents in a cellular network. We consider the situation where a finite collection of base stations are scattered on the plane, each covering a cell (possibly overlapping with other cells). Mobile users request downloads from a finite set of contents according to some popularity distribution which may be known or unknown to the base stations. Each base station has a fixed memory space that can store only a strict subset of the contents at a time; hence, if a user requests content that is not stored at any of its serving base stations, the content has to be downloaded from the backhaul. Hence, we ..