Social Aware Edge Caching in D2D Enabled Communication

As a promising architecture, mobile edge networks can effectively mitigate the load on backhaul links and reduce the transmission delay simultaneously. With the development of artificial intelligence (AI) and machine learning, how to reasonably combine AI as well as machine learning with communication is a hot topic. In this paper, considering the content features and user preference jointly, the projective adaptive resonance theory neural network (PART NN) is used to design the community architecture. After that, we can obtain the status table of communities. In order to reduce the redundant caching, the popular contents will be cached in the user equipment (UE) of center user in advance. The cache scheme of center user is adjusted according to the status table. Two transmission links are considered, i.e., cellular link and device-to-device (D2D) link, to reduce the transmission delay. Since the content preference of UE is time-varying and the migration patterns are various, the community architecture will be updated dynamically to further improve the cache hit rate. The migration patterns of UEs are affected by social factors as well as geographical factors. The simulation results show that the community construction scheme and cache scheme effectively improve the cache hit rate and reduce the transmission delay simultaneously.