Content Caching and Allocation in Spatially Correlated Small Cells

Optimal content caching has been an important topic in dense small cell networks. Due to spatial and temporal variation in the popularity of data, most content requests cannot be directly served by the lower tiers of the network, increasing the chances of congestion at the core network. This raises the issues of what to cache and where to cache, especially for content with different popularity patterns in a given region. In this work, we focus on the issue of redundant caching of popular files in a cluster when designing a content allocation scheme. We formulate the considered problem as a stable matching theory problem, where the preferences of each cache entity are sent to the Macro Base Station (MBS) for stable matching. The caches share their request lists with the MBS, which subsequently uses Irving One-Sided matching algorithm to generate a unique preference list for each caching entity such that every preference list is a representative of the popular data in that region. The algorithm achieves the desired goal of efficient caching with few but smartly planned repetitions of the popular files. Results show that our proposed scheme provides better performance in terms of cache hit ratio with increasing number of requests as compared to a popularity based scheme.