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In this paper, we investigate channel allocation for ultradense device-to-device (D2D) communications. Different from both binary graph and undirected hypergraph interference model, an improved directed hypergraph is applied to simultaneously represent cumulative and asymmetric interference aspects in the context of ultradense communications. We formulate the channel access problem in cloud D2D communication networks as a directed-hypergraph-based local altruistic game, which is proved to be an exact potential game. Then, a multiagent concurrent learning scheme in centralized-distributed fashion is proposed to search the optimal pure Nash equilibrium, which can also maximize the normalized network capacity. Finally, simulation results are presented to validate the proposed learning scheme. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the content delivery in edge caching networks where requesters can get the interested contents from surrounding helpers by device-to-device (D2D) communications. Taking the finite service capacity and cache space of helpers into account, the problem is to maximize the number of requesters obtaining all desired contents by optimizing the selection of content sources. The hypergraph theory is first applied to analyze the cumulative content effect caused by multiple content sources, and the problem is formulated as a hypergraph based cooperation cache game with at least one pure Nash equilibrium (NE). The log-linear based cooperation caching algorithm (LBCC) is proposed to find the corresponding NE points. Simulation results verify the superiority of the proposed LBCC. [ABSTRACT FROM AUTHOR]