Resource Allocation for D2D Wireless Networks With Asymmetric Social Weighted Graph

Different from the existing symmetric social relation graph, we first utilize a new asymmetric social weighted graph to study the problem of resource allocation for Device-to-Device (D2D) wireless social networks. We propose a social-aware cluster-based game theoretical scheme for resource allocation in which clusters are formatted based on the new defined weighted social interference degree and the physical interference graph. In the proposed cluster-based game scheme, we shrink action spaces of players (clusters) through a determined channel allocation rule. Simulation results show that the proposed cluster action selection algorithm has a higher convergence speed and a better social network interference performance.