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

摘要

This paper develops a comprehensive analytical framework with foundations instochastic geometry to characterize the performance of cluster-centric contentplacement in a cache-enabled device-to-device (D2D) network. Different fromdevice-centric content placement, cluster-centric placement focuses on placingcontent in each cluster such that the collective performance of all the devicesin each cluster is optimized. Modeling the locations of the devices by aPoisson cluster process, we define and analyze the performance for threegeneral cases: (i)$k$-Tx case: receiver of interest is chosen uniformly atrandom 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 interestis the $\ell^{th}$ closest device to the cluster center and its content ofinterest is available at a device chosen uniformly at random from the samecluster, and (iii) baseline case: the receiver of interest is chosen uniformlyat random in a cluster and its content of interest is available at a devicechosen independently and uniformly at random from the same cluster. Easy-to-useexpressions for the key performance metrics, such as coverage probability andarea spectral efficiency (ASE) of the whole network, are derived for all threecases. Our analysis concretely demonstrates significant improvement in thenetwork performance when the device on which content is cached or devicerequesting content from cache is biased to lie closer to the cluster centercompared to baseline case. Based on this insight, we develop and analyze a newgenerative model for cluster-centric D2D networks that allows to study theeffect of intra-cluster interfering devices that are more likely to lie closerto the cluster center.