Social-aware cache information processing for 5G ultra-dense networks
Social-aware cache information processing for 5G ultra-dense networks
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DOI：
10.1109/wcsp.2016.7752643
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发表时间：
2016-10
期刊：
影响因子：
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通讯作者：
Jiaxin Zhang;Xing Zhang;Zhi Yan;Yongjing Li;Wenbo Wang;Yan Zhang
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文献类型：
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作者：
Jiaxin Zhang;Xing Zhang;Zhi Yan;Yongjing Li;Wenbo Wang;Yan Zhang
小型小区（SC）的密集化和边缘缓存是未来5G无线网络中提高网络吞吐量、降低端到端延迟和回程成本的两种有前途的方法。然而，目前的分析和设计只关注系统在某一时刻的状态，忽略了流量的时空波动和内容流行度的变化，而这些往往受到用户行为的影响。在本文中，我们提出了一种新的社会意识的缓存信息处理方法，其中的社会联系因素（STF）的基础上，从实际的蜂窝网络收集的数据建模。有限的缓存容量部署在几个选定的非常重要的基站（VIBSs），具有较高的平均STF值。正常SC与具有有限微波前传的VIBSs相关联。通过采用随机几何过程，对关键性能指标，如：例如，在一个实施例中，吞吐量、延迟、能量效率（EE）都被导出为传输功率、高速缓存能力、文件流行度、SC和用户的密度的函数。数值结果表明，通过更好地利用STF，所提出的社会感知缓存方法的吞吐量比没有缓存的异构网络（HetNet）大近250%，比同构缓存大48%.在缓存和回程容量的约束下，可以优化所选择的VIBS的数量以最大化网络吞吐量和EE。我们的研究提供了深入了解超密集网络（UDN）中利用BS社会关系的高速缓存的有效使用。
The densification of small cells (SCs) and caching at the edge are two promising approaches to improve the network throughput, reduce end-to-end delay and backhaul cost in future 5G wireless networks. However, current analysis and design only focus on the system state at a certain time, neglecting the temporal-spatial fluctuation of traffic and the content popularity variations, which are often affected by users' behaviors. In this paper, we propose a novel social-aware cache information processing approach, where the social-tie factor (STF) is modelled on the basis of the data collected from practical cellular networks. Limited caching capacity is deployed in a few selected very important base stations (VIBSs), which have higher average STF values. Normal SCs are linked to VIBSs with limited microwave fronthaul. By adopting the stochastic geometry process, key performance indicators, e. g., throughput, delay, energy efficiency (EE), are all derived as functions of transmission power, cache ability, file popularity, density of SCs and users. Numerical results show that the throughput of the proposed social-aware caching method is nearly 250% larger than heterogeneous network (HetNet) with no cache and 48% larger than the homogeneous cache, by better utilizing STF. The number of selected VIBSs can be optimized to maximize the network throughput and EE under the constraint of cache and backhaul capacity. Our study provides insights into the efficient use of cache utilizing BS social ties in ultra-dense networks (UDNs).