  This paper studies content caching in cloud-aided wireless networks where
small cell base stations with limited storage are connected to the cloud via
limited capacity fronthaul links. By formulating a utility (inverse of service
delay) maximization problem, we propose a cache update algorithm based on
spatio-temporal traffic demands. To account for the large number of contents,
we propose a content clustering algorithm to group similar contents.
Subsequently, with the aid of regret learning at small cell base stations and
the cloud, each base station caches contents based on the learned content
popularity subject to its storage constraints. The performance of the proposed
caching algorithm is evaluated for sparse and dense environments while
investigating the tradeoff between global and local class popularity.
Simulation results show 15% and 40% gains in the proposed method compared to
various baselines.
