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Intelligent Caching in Dense Small-Cell Networks with Limited External Resources

A promising solution to alleviate the mobile traffic burden on the Internet is to cache the most popular content at the heterogeneous wireless network edge. However, due to the vast content stored at the remote server, and to cache effectively, it concerns the file popularity profile that may not be known by the network operators in advance. Therefore, online learning techniques are used to tackle the challenges brought by the unknown knowledge. We present an effective and efficient algorithm based on the stochastic combinatorial multi-armed bandits with locked-up slots to address the content caching problem. Our work particularly addresses the scenario where dense small cells with diverse user populations are deployed. Additionally, this network is only given limited external resources such as computational resource to learn the caching policies and wireless backhaul resource to refresh the caches. Our algorithm learns the caching policies online which is to decide which files to be cached sequentially. Despite sharing the limited external resources, the proposed algorithm guarantees the performance of each small cell to approach the optimum. Experiments are conducted to cross-validate the theorem presented in this work.