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Content Caching for Heterogeneous Small-Cell Networks with Intelligent Content Access

机译:具有智能内容访问的异构小型单元网络的内容缓存

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To realize content caching at base stations (BSs), a caching system fetches contents to the appropriate base stations in advance then it uses the fetched contents to serve end users in the serving phase. This paper studies a caching problem for heterogeneous small-cell networks with QoS-aware and adaptive BS association where end users can be associated with either small-cell or macro-cell BSs. Toward this end, we derive the cache miss ratio for general caching strategy based on which we formulate a caching problem which aims at minimizing the cache miss ratio. To solve this problem, we propose two algorithms, namely Sparse Network Caching (SNC) and Two-Stage Caching (TSC) algorithms. We prove that the SNC algorithm can obtain the optimal caching solution as the request rate to each BS is much smaller than its serving capability. Numerical results demonstrate that the SNC algorithm performs well in the sparse network scenario while the TSC algorithm operates efficiently in all studied scenarios. Moreover, the proposed algorithms significantly outperform the random caching (RDC) and most popular caching (MPC) algorithms.
机译:为了实现基站(BSS)的内容高速缓存,预计预先将内容提出到适当的基站,然后它使用获取的内容来服务于服务阶段的最终用户。本文研究了具有QoS感知和自适应BS关联的异构小型电池网络的缓存问题,其中最终用户可以与小小区或宏小区BS相关联。朝向此结束,我们基于哪个高速缓存策略的高速缓存未命中比率,我们制定了一个缓存问题,旨在最大限度地减少高速缓存未命中比率。为了解决这个问题,我们提出了两个算法,即稀疏网络缓存(SNC)和两级缓存(TSC)算法。我们证明SNC算法可以获得最佳缓存解决方案,因为每个BS的请求率远小于其服务能力。数值结果表明,SNC算法在稀疏网络场景中执行良好,而TSC算法在所有学习的场景中有效运行。此外,所提出的算法显着优于随机缓存(RDC)和最流行的缓存(MPC)算法。

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