Learning distributed caching strategies in small cell networks
Learning distributed caching strategies in small cell networks
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DOI：
10.1109/iswcs.2014.6933484
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发表时间：
2014-10
期刊：
影响因子：
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通讯作者：
A. Sengupta;Saidhiraj Amuru;R. Tandon;R. Buehrer;T. Clancy
中科院分区：
文献类型：
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作者：
A. Sengupta;Saidhiraj Amuru;R. Tandon;R. Buehrer;T. Clancy
高速缓存已经成为现代通信系统中的重要工具，用于通过允许流行文件被预取并本地存储在最终用户的设备上来降低峰值数据速率。随着从同构蜂窝网络到异构蜂窝网络的范式转变，数据卸载到小小区基站（sBS）的概念已经引起了极大的关注。最近已经提出了在这些小小区基站处的高速缓存，其中流行的文件被预取并本地存储，以便避免到核心网络的有限容量回程连接链路中的瓶颈。在本文中，我们研究了分布式缓存策略在这样一个异构的小小区无线网络从强化学习的角度。使用现有技术的结果，可以表明，即使当sBS知道要被缓存的文件的流行度简档时，sBS中的最佳联合缓存内容放置也是NP难问题。为了解决这个问题，我们提出了一个编码的缓存框架，其中的sBS学习的流行档案的文件（基于他们的需求历史）通过组合多臂强盗框架。然后，sBS定期预取流行文件的喷泉编码版本的片段，以满足用户的请求。我们表明，建议的编码缓存框架可以建模为一个线性规划，考虑到网络的连通性，从而共同设计的缓存策略。数值结果表明，联合编码缓存技术的好处幼稚分散的缓存放置策略。
Caching has emerged as a vital tool in modern communication systems for reducing peak data rates by allowing popular files to be pre-fetched and stored locally at end users' devices. With the shift in paradigm from homogeneous cellular networks to the heterogeneous ones, the concept of data offloading to small cell base stations (sBS) has garnered significant attention. Caching at these small cell base stations has recently been proposed, where popular files are pre-fetched and stored locally in order to avoid bottlenecks in the limited capacity backhaul connection link to the core network. In this paper, we study distributed caching strategies in such a heterogeneous small cell wireless network from a reinforcement learning perspective. Using state of the art results, it can be shown that the optimal joint cache content placement in the sBSs turns out to be a NP-hard problem even when the sBS's are aware of the popularity profile of the files that are to be cached. To address this problem, we propose a coded caching framework, where the sBSs learn the popularity profile of the files (based on their demand history) via a combinatorial multi-armed bandit framework. The sBSs then pre-fetch segments of the Fountain-encoded versions of the popular files at regular intervals to serve users' requests. We show that the proposed coded caching framework can be modeled as a linear program that takes into account the network connectivity and thereby jointly designs the caching strategies. Numerical results are presented to show the benefits of the joint coded caching technique over naive decentralized cache placement strategies.