论文标题

一种非平稳的匪徒学习方法,用于使用无重量编码的传播的节能FEMTOCACHING

A Non-Stationary Bandit-Learning Approach to Energy-Efficient Femto-Caching with Rateless-Coded Transmission

论文作者

Maghsudi, Setareh, van der Schaar, Mihaela

论文摘要

对媒体流媒体流的需求不断增加,而有限的回程容量渲染器必须开发有效的文件传递方法。一种这样的方法就是FEMTO-CACHING,尽管它具有很大的潜力,但仍面临着一些挑战,例如有效的资源管理。我们研究了小型细胞网络中关节缓存和传输的资源分配问题,该系统分为两个连续的阶段:(i)缓存放置,以及(ii)关节文件和传输功率选择,然后是广播。我们根据单位传输功率成功重建的数量来定义每个小基站的实用性。然后,我们制定问题,以便在每个广播回合中选择一个从缓存中的文件以及传输功率级别,以便最大化地平线上的累积实用程序。前者的问题归结为随机背包问题,我们将后者视为多军匪徒问题。我们为每个问题开发一个解决方案,并提供理论和数值评估。与最先进的研究相反,所提出的方法特别适合具有时间变化的统计属性的网络。此外,即使没有有关随机参数的统计特征(例如文件受欢迎程度和频道质量)的统计特征,它也适用并且运行良好。

The ever-increasing demand for media streaming together with limited backhaul capacity renders developing efficient file-delivery methods imperative. One such method is femto-caching, which, despite its great potential, imposes several challenges such as efficient resource management. We study a resource allocation problem for joint caching and transmission in small cell networks, where the system operates in two consecutive phases: (i) cache placement, and (ii) joint file- and transmit power selection followed by broadcasting. We define the utility of every small base station in terms of the number of successful reconstructions per unit of transmission power. We then formulate the problem as to select a file from the cache together with a transmission power level for every broadcast round so that the accumulated utility over the horizon is maximized. The former problem boils down to a stochastic knapsack problem, and we cast the latter as a multi-armed bandit problem. We develop a solution to each problem and provide theoretical and numerical evaluations. In contrast to the state-of-the-art research, the proposed approach is especially suitable for networks with time-variant statistical properties. Moreover, it is applicable and operates well even when no initial information about the statistical characteristics of the random parameters such as file popularity and channel quality is available.

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