Recommending Access Points To Individual Mobile Users Via Automatic Group Learning

We consider user to cell association in a heterogeneous network with a mix of LTE/3G and WiFi cells. Individual user preferences are often neglected when a user to cell association decision is made. In this paper we propose use of a recommender system to inform the mapping of users to cells. We demonstrate the effectiveness of the proposed grouped-based user to cell associations for a set of synthetically generated user/cell ratings.