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Social Community-Aware Content Placement in Wireless Device-to-Device Communication Networks

机译:无线设备到设备通信网络中的社交社区感知内容放置

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In this paper, a novel framework for optimizing the caching of popular user content at the level of wireless user equipments (UEs) is proposed. The goal is to improve content offloading over wireless device-to-device (D2D) communication links. In the considered network, users belong to different social communities while their UEs form a single multi-hop D2D network. The proposed framework allows us to exploit the multi-community social context of users for improving the local offloading of cached content in a multi-hop D2D network. To model the collaborative effect of a set of UEs on content offloading, a cooperative game between the UEs is formulated. For this game, it is shown that the Shapley value (SV) of each UE effectively captures the impact of this UE on the overall content offloading process. To capture the presence of multiple social communities that connect the UEs, a hypergraph model is proposed. Two line graphs, an influence-weighted graph, and a connectivity-weighted graph, are developed for analyzing the proposed hypergaph model. Using the developed line graphs along with the SV of the cooperative game, a precise offloading power metric is derived for each UE within a multi-community, multi-hop D2D network. Then, UEs with high offloading power are chosen as the optimal locations for caching the popular content. Simulation results show that, on the average, the proposed cache placement framework achieves 12, 19, and 21 percent improvements in terms of the number of UEs that received offloaded popular content compared to the schemes based on betweenness, degree, and closeness centrality, respectively.
机译:在本文中,提出了一种在无线用户设备(UE)级别优化流行用户内容缓存的新颖框架。目标是改善无线设备到设备(D2D)通信链路上的内容卸载。在所考虑的网络中,用户属于不同的社交社区,而其用户设备则形成单个多跳D2D网络。所提出的框架使我们能够利用用户的多社区社交环境,以改善多跳D2D网络中缓存内容的本地卸载。为了建模一组UE对内容卸载的协同作用,制定了UE之间的协同游戏。对于该游戏,示出了每个UE的Shapley值(SV)有效地捕获了该UE对整个内容卸载过程的影响。为了捕获连接UE的多个社交社区的存在,提出了超图模型。开发了两个折线图,一个影响加权图和一个连通性加权图,用于分析所提出的超视距模型。使用开发的线图以及合作游戏的SV,可以为多社区,多跳D2D网络中的每个UE导出精确的卸载功率度量。然后,将具有高卸载能力的UE选择为用于缓存流行内容的最佳位置。仿真结果表明,与基于中间性,程度和紧密性中心的方案相比,平均而言,所建议的缓存放置框架在接收卸载的流行内容的UE数量方面分别实现了12%,19%和21%的改进。

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