Social-aware collaborative caching for D2D content sharing

Can Zhang, Dan Wu, Liang Ao, Yueming Cai
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Abstract

With the rapid growth of wireless content demands, Device-to-Device (D2D) content sharing technology is proposed to effectively alleviate the pressure of base stations and improve the quality of service of users. However, due to the limited storage capacity of devices, the various content demands are difficult to be satisfied. Hence, caching schemes are needed. In particular, the collaborative caching, which can increase the utilization ratio of the storage capacity, attracts much attention. Moreover, we introduce the social popularity to improve the availability of preset contents, and then, we propose the social-related download rate by combining the physical and social information. Guided by this, we model the social-aware collaborative caching problem in a D2D content sharing scenario by maximizing the sum of social-related download rate over the constraint of limited storage capacity. Due to its intractability, it is computationally reduced to the maximization of a monotone submodular function, subject to a partition matroid constraint. Subsequently, the social-aware collaborative caching algorithm based on greedy algorithm is designed to achieve a suboptimal solution within a factor 1/2 approximation guarantee and polynomial-complexity.
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用于D2D内容共享的社会感知协作缓存
随着无线内容需求的快速增长,为了有效缓解基站压力,提高用户服务质量,提出了设备到设备(Device-to-Device, D2D)内容共享技术。然而,由于设备存储容量有限,各种内容需求很难得到满足。因此,需要缓存方案。特别是协同缓存技术,由于其能够提高存储空间的利用率而备受关注。通过引入社会人气来提高预设内容的可用性,并结合物理信息和社会信息,提出与社会相关的下载率。在此指导下,我们通过在有限存储容量的约束下最大化社交相关下载速率的总和,对D2D内容共享场景中的社交感知协作缓存问题进行建模。由于它的难解性,它在计算上被简化为一个单调的子模函数的最大化,并受到划分矩阵的约束。在此基础上,设计了基于贪心算法的社会感知协同缓存算法,实现了1/2因子近似保证和多项式复杂度的次优解。
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