Fundamentals of Cluster-Centric Content Placement in Cache-Enabled Device-to-Device Networks

TL;DR

This paper presents an analytical framework for cluster-centric content placement in cache-enabled D2D networks, demonstrating performance improvements when content and requests are biased towards cluster centers, using stochastic geometry.

Contribution

It introduces a stochastic geometry-based model for cluster-centric content placement, deriving performance metrics and analyzing the impact of bias towards cluster centers in D2D networks.

Findings

Significant performance gains when content placement favors cluster centers.

Derived explicit expressions for coverage probability and area spectral efficiency.

New generative model for intra-cluster interference considering bias towards cluster centers.

Abstract

This paper develops a comprehensive analytical framework with foundations in stochastic geometry to characterize the performance of cluster-centric content placement in a cache-enabled device-to-device (D2D) network. Different from device-centric content placement, cluster-centric placement focuses on placing content in each cluster such that the collective performance of all the devices in each cluster is optimized. Modeling the locations of the devices by a Poisson cluster process, we define and analyze the performance for three general cases: (i)$k$-Tx case: receiver of interest is chosen uniformly at random in a cluster and its content of interest is available at the $k^{th}$ closest device to the cluster center, (ii) $\ell$-Rx case: receiver of interest is the $\ell^{th}$ closest device to the cluster center and its content of interest is available at a device chosen uniformly at random from the same cluster, and (iii) baseline case: the receiver of interest is chosen uniformly at random in a cluster and its content of interest is available at a device chosen independently and uniformly at random from the same cluster. Easy-to-use expressions for the key performance metrics, such as coverage probability and area spectral efficiency (ASE) of the whole network, are derived for all three cases. Our analysis concretely demonstrates significant improvement in the network performance when the device on which content is cached or device requesting content from cache is biased to lie closer to the cluster center compared to baseline case. Based on this insight, we develop and analyze a new generative model for cluster-centric D2D networks that allows to study the effect of intra-cluster interfering devices that are more likely to lie closer to the cluster center.