On learning mobility patterns in cellular networks
Document typeConference report
Rights accessRestricted access - publisher's policy
This paper considers the use of clustering techniques to learn the mobility patterns existing in a cellular network. These patterns are materialized in a database of prototype trajectories obtained after having observed multiple trajectories of mobile users. Both K-means and Self-Organizing Maps (SOM) techniques are assessed. Different applicability areas in the context of Self-Organizing Networks (SON) for 5G are discussed and, in particular, a methodology is proposed for predicting the trajectory of a mobile user.
CitationSanchez, J., Perez-Romero, J., Agusti, R., Sallent, J. On learning mobility patterns in cellular networks. A: International Conference on Artificial Intelligence Applications and Innovations. "Artificial Intelligence Applications and Innovations: 12th IFIP WG 12.5 International Conference and Workshops, AIAI 2016: Thessaloniki, Greece: September 16-18, 2016 proceedings". Thessaloniki: Springer, 2016, p. 686-696.
|Paper AIAI 5G-P ... ns in Cellular Netw....pdf||3,008Mb||Restricted access|