首页> 外文会议>IEEE Vehicular Technology Conference >Content Caching for Heterogeneous Small-Cell Networks with Intelligent Content Access
【24h】

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

机译:具有智能内容访问功能的异构小型蜂窝网络的内容缓存

获取原文

摘要

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:为了在基站(BS)上实现内容缓存,缓存系统预先将内容提取到适当的基站,然后在服务阶段使用提取的内容为最终用户提供服务。本文研究了具有QoS意识和自适应BS关联的异构小蜂窝网络的缓存问题,其中最终用户可以与小蜂窝BS或宏蜂窝BS关联。为此,我们导出了通用缓存策略的缓存未命中率,在此基础上,我们提出了旨在使缓存未命中率最小化的缓存问题。为了解决这个问题,我们提出了两种算法,即稀疏网络缓存(SNC)和两阶段缓存(TSC)算法。我们证明了SNC算法可以获得最佳的缓存解决方案,因为对每个BS的请求速率远小于其服务能力。数值结果表明,在稀疏网络场景中,SNC算法表现良好,而在所有研究场景中,TSC算法均能高效运行。此外,提出的算法明显优于随机缓存(RDC)和最流行的缓存(MPC)算法。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号