Social-aware cache information processing for 5G ultra-dense networks

摘要

The densification of small cells (SCs) and caching at the edge are two promising approaches to improve the network throughput, reduce end-to-end delay and backhaul cost in future 5G wireless networks. However, current analysis and design only focus on the system state at a certain time, neglecting the temporal-spatial fluctuation of traffic and the content popularity variations, which are often affected by users' behaviors. In this paper, we propose a novel social-aware cache information processing approach, where the social-tie factor (STF) is modelled on the basis of the data collected from practical cellular networks. Limited caching capacity is deployed in a few selected very important base stations (VIBSs), which have higher average STF values. Normal SCs are linked to VIBSs with limited microwave fronthaul. By adopting the stochastic geometry process, key performance indicators, e. g., throughput, delay, energy efficiency (EE), are all derived as functions of transmission power, cache ability, file popularity, density of SCs and users. Numerical results show that the throughput of the proposed social-aware caching method is nearly 250% larger than heterogeneous network (HetNet) with no cache and 48% larger than the homogeneous cache, by better utilizing STF. The number of selected VIBSs can be optimized to maximize the network throughput and EE under the constraint of cache and backhaul capacity. Our study provides insights into the efficient use of cache utilizing BS social ties in ultra-dense networks (UDNs).