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.

中文翻译：

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具有无速率编码传输的节能毫微微缓存的非平稳 Bandit-Learning 方法

对媒体流不断增长的需求以及有限的回程容量使得开发高效的文件传输方法势在必行。一种这样的方法是毫微微缓存，尽管它具有巨大的潜力，但它带来了一些挑战，例如有效的资源管理。我们研究了小型蜂窝网络中联合缓存和传输的资源分配问题，其中系统在两个连续阶段运行：（i）缓存放置，以及（ii）联合文件和传输功率选择，然后是广播。我们根据每单位传输功率的成功重建次数来定义每个小型基站的效用。然后，我们将问题表述为从缓存中选择一个文件以及每个广播轮的传输功率级别，以便最大化地平线上的累积效用。前一个问题归结为一个随机背包问题，我们把后者看成一个多臂老虎机问题。我们为每个问题制定解决方案，并提供理论和数值评估。与最先进的研究相比，所提出的方法特别适用于具有时变统计特性的网络。此外，即使在没有关于诸如文件流行度和频道质量等随机参数的统计特征的初始信息可用时，它也适用并且运行良好。我们将后者视为多臂强盗问题。我们为每个问题制定解决方案，并提供理论和数值评估。与最先进的研究相比，所提出的方法特别适用于具有时变统计特性的网络。此外，即使在没有关于诸如文件流行度和频道质量等随机参数的统计特征的初始信息可用时，它也适用并且运行良好。我们将后者视为多臂强盗问题。我们为每个问题制定解决方案，并提供理论和数值评估。与最先进的研究相比，所提出的方法特别适用于具有时变统计特性的网络。此外，即使在没有关于诸如文件流行度和频道质量等随机参数的统计特征的初始信息可用时，它也适用并且运行良好。