Optimizing Joint Probabilistic Caching and Communication for Clustered D2D Networks

Caching at mobile devices and leveraging device-to-device (D2D) communication are two promising approaches to support massive content delivery over wireless networks. The analysis of such D2D caching networks based on a physical interference model is usually carried out by assuming that devices are uniformly distributed. However, this approach does not fully consider and characterize the fact that devices are usually grouped into clusters. Motivated by this fact, this paper presents a comprehensive performance analysis and joint communication and caching optimization for a clustered D2D network. Devices are distributed according to a Thomas cluster process (TCP) and are assumed to have a surplus memory which is exploited to proactively cache files from a known library, following a random probabilistic caching scheme. Devices can retrieve the requested files from their caches, from neighbouring devices in their proximity (cluster), or from the base station as a last resort. Three key performance metrics are optimized in this paper, namely, the offloading gain, energy consumption, and latency

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

Caching at mobile devices and leveraging device- to-device (D2D) communication are two promising approaches to support massive content delivery over wireless networks. The analysis of cache-enabled wireless networks is usually carried out by assuming that devices are uniformly distributed, however, in social networks, mobile devices are intrinsically grouped into disjoint clusters. In this regards, this paper proposes a spatiotemporal mathematical model that tracks the service requests arrivals and account for the clustered devices geometry. Two kinds of devices are assumed, particularly, content clients and content providers. Content providers are assumed to have a surplus memory which is exploited to proactively cache contents from a known library, following a random probabilistic caching scheme. Content clients can retrieve a requested content from the nearest content provider in their proximity (cluster), or, as a last resort, the base station (BS). The developed spatiotemporal model is leveraged to formulate a joint optimization problem of the content caching and spectrum partitioning in order to minimize the average service delay. Due to the high complexity of the optimization problem, the caching and spectrum partitioning problems are decoupled and solved iteratively using the block coordinate descent (BCD) optimization technique. To this end, an optimal and suboptimal solutions are obtained for the bandwidth partitioning and probabilistic caching subproblems, respectively. Numerical results highlight the superiority of the proposed scheme over conventional caching schemes under equal and optimized bandwidth allocations. Particularly, it is shown that the average service delay is reduced by nearly 100% and 350%, compared to the Zipf and uniform caching schemes under equal bandwidth allocations, respectively