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Caching at mobile devices and leveraging device- to-device (D2D)communication are two promising approaches to support massive content deliveryover wireless networks. The analysis of cache-enabled wireless networks isusually carried out by assuming that devices are uniformly distributed,however, in social networks, mobile devices are intrinsically grouped intodisjoint clusters. In this regards, this paper proposes a spatiotemporalmathematical model that tracks the service requests arrivals and account forthe clustered devices geometry. Two kinds of devices are assumed, particularly,content clients and content providers. Content providers are assumed to have asurplus memory which is exploited to proactively cache contents from a knownlibrary, following a random probabilistic caching scheme. Content clients canretrieve a requested content from the nearest content provider in theirproximity (cluster), or, as a last resort, the base station (BS). The developedspatiotemporal model is leveraged to formulate a joint optimization problem ofthe content caching and spectrum partitioning in order to minimize the averageservice delay. Due to the high complexity of the optimization problem, thecaching and spectrum partitioning problems are decoupled and solved iterativelyusing the block coordinate descent (BCD) optimization technique. To this end,an optimal and suboptimal solutions are obtained for the bandwidth partitioningand probabilistic caching subproblems, respectively. Numerical resultshighlight the superiority of the proposed scheme over conventional cachingschemes under equal and optimized bandwidth allocations. Particularly, it isshown that the average service delay is reduced by nearly 100% and 350%,compared to the Zipf and uniform caching schemes under equal bandwidthallocations, respectively.

Optimized Caching and Spectrum Partitioning for D2D enabled Cellular Systems with Clustered Devices