We consider content-level selective offloading of cellular downlink traffic
to a wireless infostation terminal which stores high data-rate content in its
cache memory. Cellular users in the vicinity of the infostation can directly
download the stored content from the infostation through a broadband connection
(e.g., WiFi), reducing the latency and load on the cellular network. The goal
of the infostation cache controller (CC) is to store the most popular content
in the cache memory such that the maximum amount of traffic is offloaded to the
infostation. In practice, the popularity profile of the files is not known by
the CC, which observes only the instantaneous demands for those contents stored
in the cache. Hence, the cache content placement is optimised based on the
demand history and on the cost associated to placing each content in the cache.
By refreshing the cache content at regular time intervals, the CC gradually
learns the popularity profile, while at the same time exploiting the limited
cache capacity in the best way possible. This is formulated as a multi-armed
bandit (MAB) problem with switching cost. Several algorithms are presented to
decide on the cache content over time. The performance is measured in terms of
cache efficiency, defined as the amount of net traffic that is offloaded to the
infostation. In addition to theoretical regret bounds, the proposed algorithms
are analysed through numerical simulations. In particular, the impact of system
parameters, such as the number of files, number of users, cache size, and
skewness of the popularity profile, on the performance is studied numerically.
It is shown that the proposed algorithms learn the popularity profile quickly
for a wide range of system parameters.

本文研究了在移动通信下行数据中进行内容级别选择性卸载的问题，将流行的内容存储在无线信息站终端的高速缓存存储器中，直接从信息站下载存储的内容，以减少蜂窝网络的延迟和负载，并利用多臂赌博问题及切换成本等方式进行最优的缓存内容放置来学习流行度模型，以提高缓存效率。

异构网络中的内容级别选择性卸载：多臂赌博优化和遗憾界限

Content-Level Selective Offloading in Heterogeneous Networks:  Multi-armed Bandit Optimization and Regret Bounds

Optimal cache content placement in a wireless small cell base station (sBS)
with limited backhaul capacity is studied. The sBS has a large cache memory and
provides content-level selective offloading by delivering high data rate
contents to users in its coverage area. The goal of the sBS content controller
(CC) is to store the most popular contents in the sBS cache memory such that
the maximum amount of data can be fetched directly form the sBS, not relying on
the limited backhaul resources during peak traffic periods. If the popularity
profile is known in advance, the problem reduces to a knapsack problem.
However, it is assumed in this work that, the popularity profile of the files
is not known by the CC, and it can only observe the instantaneous demand for
the cached content. Hence, the cache content placement is optimised based on
the demand history. By refreshing the cache content at regular time intervals,
the CC tries to learn the popularity profile, while exploiting the limited
cache capacity in the best way possible. Three algorithms are studied for this
cache content placement problem, leading to different exploitation-exploration
trade-offs. We provide extensive numerical simulations in order to study the
time-evolution of these algorithms, and the impact of the system parameters,
such as the number of files, the number of users, the cache size, and the
skewness of the popularity profile, on the performance. It is shown that the
proposed algorithms quickly learn the popularity profile for a wide range of
system parameters.

研究了无线小型基站中有限的回程容量中的最佳缓存内容放置，使用内容级选择性卸载并基于需求历史来优化缓存内容放置，并提供了三种算法进行研究。