This article presents a user spatio-temporal behavior aware cache selection framework to facilitate device-to-device multicast (D2MD) communication that minimizes the number of caches required while achieving a desired user load on the cellular network. Consequently, it alleviates the caching load on the cellular network. The optimization problem formulated to minimize the number of caches is combinatorial in nature with an exponential search space. Hence, a greedy algorithm for cache selection is proposed to reduce the search space. It has been shown that the greedy algorithm has a complexity $\mathcal {O}(K^2)$ where $K$ is the number of users. A real-world location-based inhomogeneous Markov chain is presented to model the joint spatio-temporal behavior of the users. The diurnal variation of observable user load on the core network as well as sum-rate of non-caching users has been demonstrated for real-world location data traces. It has been shown that the proposed framework not only achieves the desired user load but also helps in improving the sum-rate of non-caching users as compared to the mobility-unaware selection of caches.

中文翻译：

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动态D2D组播网络中使用非均匀马尔可夫模型的缓存选择

本文介绍了一种用户时空行为感知的缓存选择框架，以促进设备到设备多播（D2MD）通信，该通信可最大程度地减少所需的缓存数量，同时在蜂窝网络上实现所需的用户负载。因此，它减轻了蜂窝网络上的缓存负荷。为使高速缓存数量最小化而制定的优化问题实际上是与指数搜索空间结合在一起的。因此，提出了一种用于缓存选择的贪婪算法以减少搜索空间。结果表明，贪婪算法具有复杂性$ \数学{O}（K ^ 2）$ 哪里 $ K $是用户数。提出了一个基于实际位置的非均匀马尔可夫链，以模拟用户的联合时空行为。对于真实位置数据跟踪，已经证明了核心网络上可观察到的用户负载的日变化以及非缓存用户的总速率。已经显示出，与不知道移动性的高速缓存选择相比，所提出的框架不仅实现了期望的用户负载，而且还有助于提高非高速缓存用户的总速率。