In this paper we study a data-supported caching policy design for wireless D2D caching networks, which is based on a dataset collected from a campus Wi-Fi network. After a well-designed preprocessing for the dataset, for the first time, we conduct a real dataset based performance evaluation for the caching policies designed based on the homogeneous Poisson Point Process (PPP) model and a clustered PPP model. We proceed to propose a novel approach for the design of the D2D caching policy. It directly models the number of D2D neighbours, instead of characterizing the locations of users as the PPP models. We show that the number of D2D neighbours can be well modeled by a discrete Gamma distribution. Given the model, we develop an iterative algorithm to optimize the D2D caching policy, and also provide a method to optimize the cache update time in order to balance the caching gain and overhead. Simulation results based on the dataset show that the proposed caching policy can achieve good performance with low cost of cache updating.

中文翻译：

---

无线 D2D 缓存网络的数据支持缓存策略优化


在本文中，我们研究了无线 D2D 缓存网络的数据支持的缓存策略设计，该设计基于从校园 Wi-Fi 网络收集的数据集。经过精心设计的数据集预处理后，我们首次对基于同质泊松点过程（PPP）模型和集群PPP模型设计的缓存策略进行了基于真实数据集的性能评估。我们继续提出一种新的 D2D 缓存策略设计方法。它直接对 D2D 邻居的数量进行建模，而不是像 PPP 模型那样描述用户的位置。我们表明，D2D 邻居的数量可以通过离散 Gamma 分布很好地建模。给定该模型，我们开发了一种迭代算法来优化 D2D 缓存策略，并且还提供了一种优化缓存更新时间的方法，以平衡缓存增益和开销。基于数据集的仿真结果表明，所提出的缓存策略能够以较低的缓存更新成本获得良好的性能。