Context-Aware Small Cell Networks: How Social Metrics Improve Wireless Resource Allocation

In this paper, a novel approach for optimizing resource allocation in wireless small cell networks (SCNs) with device-to-device (D2D) communication is proposed. The proposed approach allows jointly exploiting the wireless and social context of wireless users for optimizing the overall allocation of resources and improving the traffic offload in SCNs. This context-aware resource allocation problem is formulated as a matching game, in which user equipments (UEs) and resource blocks (RBs) rank one another, based on utility functions that capture both wireless and social metrics. Due to social interrelations, this game is shown to belong to a class of matching games with peer effects. To solve this game, a novel self-organizing algorithm is proposed, using which UEs and RBs can interact to decide on their desired allocation. The proposed algorithm is then proven to converge to a two-sided stable matching between UEs and RBs. The properties of the resulting stable outcome are then studied and assessed. Simulation results using real social data show that clustering of socially connected users allows offloading a substantially larger amount of traffic than the conventional context-unaware approach. These results show that exploiting social context has high practical relevance in saving resources on wireless links and in the backhaul.