Intelligent Caching Algorithms in Heterogeneous Wireless Networks with Uncertainty

A burgeoning number of wireless devices connecting to the Internet tend to impose a heavy traffic load on the network backbone. Caching the most popular content at the heterogeneous wireless network edge is a promising way to alleviate the network overload. However, to cache the diverse content effectively, a file popularity profile that may not be known in advance to network operators has to be utilized. To tackle the challenge caused by this uncertainty, online learning techniques can be considered. Additionally, in practice, dense small-cell networks are often deployed to maximize spectral efficiency, which will naturally bring overlapping coverage areas among individual small cells. In this paper, we propose to address the content caching problem in a scenario of overlapping coverage areas among small cells while further allowing users distributed in the overlapping area to stochastically choose to connect to the small-cell base station they can reach. We propose two effective and efficient online learning algorithms to address the aforementioned problem and also provide theoretical guarantees. Finally, experiments are conducted to verify the performance of the proposed algorithms practically.