  In this paper, a novel approach for optimizing and managing resource
allocation in wireless small cell networks (SCNs) with device-to-device (D2D)
communication is proposed. The proposed approach allows to jointly exploit both
the wireless and social context of wireless users for optimizing the overall
allocation of resources and improving 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, selforganizing 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 to offload 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 the wireless links and on the backhaul.
