Optimizing The Spatial Content Caching Distribution for Device-to-Device Communications

TL;DR

This paper investigates optimal spatial content caching strategies in D2D networks with spatially correlated models, demonstrating that MHC placement improves hit probability over independent placement for small caches.

Contribution

It introduces two novel spatial correlation models for content placement in D2D networks and analyzes their impact on cache hit probability, highlighting the advantages of MHC placement.

Findings

MHC placement outperforms independent placement for small cache sizes.

Exchangeable content model performs worse than independent placement.

Spatial correlation models significantly affect cache hit probability.

Abstract

We study the optimal geographic content placement problem for device-to-device (D2D) networks in which the content popularity follows the Zipf law. We consider a D2D caching model where the locations of the D2D users (caches) are modeled by a Poisson point process (PPP) and have limited communication range and finite storage. Unlike most related work which assumes independent placement of content, and does not capture the locations of the users, we model the spatial properties of the network including spatial correlation in terms of the cached content. We propose two novel spatial correlation models, the exchangeable content model and a Mat\'{e}rn (MHC) content placement model, and analyze and optimize the \emph{hit probability}, which is the probability of a given D2D node finding a desired file at another node within its communication range. We contrast these results to the independent placement model, and show that exchangeable placement performs worse. On the other hand, MHC placement yields a higher cache hit probability than independent placement for small cache sizes.