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On learning mobility patterns in cellular networks

Author
Sanchez, J.; Perez-Romero, J.; Agusti, R.; Sallent, J.
Type of activity
Presentation of work at congresses
Name of edition
12th IFIP WG 12.5 International Conference and Workshops on Artificial Intelligence Applications and Innovations
Date of publication
2016
Presentation's date
2016-09
Book of congress proceedings
Artificial Intelligence Applications and Innovations: 12th IFIP WG 12.5 International Conference and Workshops, AIAI 2016: Thessaloniki, Greece: September 16-18, 2016 proceedings
First page
686
Last page
696
Publisher
Springer
DOI
https://doi.org/10.1007/978-3-319-44944-9_61 Open in new window
Repository
http://hdl.handle.net/2117/99420 Open in new window
Abstract
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 traject...
Citation
Sanchez, 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.
Keywords
Artificial intelligence, Cellular network, Clustering, Clustering techniques, Conformal mapping, K-means, Mobile telecommunication systems, Mobile users, Mobility pattern, Self Organizing Network (SON), Trajectories, Wireless networks
Group of research
CCABA - Advanced Broadband Communications Center
GRCM - Mobile Communication Reserach Group

Participants