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arXiv 1404.6563cs.ITmath.IT
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多级别流行度和访问的编码缓存

Coded Caching for Multi-level Popularity and Access

Jad Hachem, Nikhil Karamchandani, Suhas Diggavi

摘要:

为了满足对无线内容呈指数级增长的需求,使用缓存正在成为一种潜在的解决方案。最近已经确定,内容交付和存储(编码缓存)的联合设计可以显着提高传统缓存的性能。编码缓存非常适合新兴的异构无线架构,该架构由具有高数据率的局部覆盖无线接入点(AP)的密集部署以及稀疏分布的大覆盖宏小区基站(BS)组成。这使得能够设计编码缓存和交付方案,该方案为AP配备存储,并以创建编码多播机会的方式将内容放置在其中,以与宏小区广播相结合,从而满足用户甚至不同的需求。对于具有单级内容的系统,即,一个所有内容都是受欢迎的地方。在这项工作中,我们考虑了一个系统与非统一的流行度的内容被分为多个级别,根据不同程度的流行。这项工作的主要贡献是推导出一个顺序最优的计划,明智地共享不同流行的文件之间的缓存内存。为了显示顺序最优性,我们推导出新的信息理论下界,它使用滑动窗口熵不等式,有效地创建一个非割集界。我们还扩展的想法,当用户可以访问多个缓存随着广播。最后,我们考虑两个极端的情况下,用户分布在缓存的多级流行度模型:一个单一的用户每个缓存(单用户设置)与大量的用户每个缓存(多用户设置),并证明了二分法在这两个极端情况下的顺序最优策略。

英文摘要:

To address the exponentially rising demand for wireless content, use of caching is emerging as a potential solution. It has been recently established that joint design of content delivery and storage (coded caching) can significantly improve performance over conventional caching. Coded caching is well suited to emerging heterogeneous wireless architectures which consist of a dense deployment of local-coverage wireless access points (APs) with high data rates, along with sparsely-distributed, large-coverage macro-cell base stations (BS). This enables design of coded caching-and-delivery schemes that equip APs with storage, and place content in them in a way that creates coded-multicast opportunities for combining with macro-cell broadcast to satisfy users even with different demands. Such coded-caching schemes have been shown to be order-optimal with respect to the BS transmission rate, for a system with single-level content, i.e., one where all content is uniformly popular. In this work, we consider a system with non-uniform popularity content which is divided into multiple levels, based on varying degrees of popularity. The main contribution of this work is the derivation of an order-optimal scheme which judiciously shares cache memory among files with different popularities. To show order-optimality we derive new information-theoretic lower bounds, which use a sliding-window entropy inequality, effectively creating a non-cutset bound. We also extend the ideas to when users can access multiple caches along with the broadcast. Finally we consider two extreme cases of user distribution across caches for the multi-level popularity model: a single user per cache (single-user setup) versus a large number of users per cache (multi-user setup), and demonstrate a dichotomy in the order-optimal strategies for these two extreme cases.

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