Exploring Content Clustering for User Association in Small Cell Networks

Abstract

User association has redrawn much attention lately, due to the introduction of content caching in small base stations (SBSs). To reduce traffic burden on backhaul links, users are associated with different SBSs when requesting different contents. However, user-perceived delay increases if the serving SBSs that have the desired contents are overloaded. Moreover, the user association problem becomes complex due to the vast number of contents. In this paper, to reduce user-perceived delay as well as backhaul loads, we propose a cluster-level user association scheme where content clustering is leveraged to simplify user association and reduce its complexity. Particularly, similar contents are clustered together according to the content preferences of users and cached contents in SBSs. Thus, the dimensionality of the user problem becomes smaller. On this basis, we propose a distributed cluster-level user association scheme, where each user selects SBSs based on their traffic loads and cached contents. Simulation results show that our scheme based on clustered contents outperforms the traditional schemes.