In future cellular networks, the ability to predict network parameters such as cell load will be a key enabler of several proposed adaptation and resource allocation techniques. In this study, we consider a joint exploitation of spatio-temporal data to improve the prediction accuracy of standard regression methods. We test several such methods from the literature on a publicly available dataset and document the advantages of the proposed approach.

Cell traffic prediction using joint spatio-temporal information

LOVISOTTO, ENRICO
;
VIANELLO, ENRICO
;
CAZZARO, DAVIDE
;
Polese, Michele
;
Chiariotti, Federico
;
Zucchetto, Daniel
;
Zanella, Andrea
;
Zorzi, Michele
2017

Abstract

In future cellular networks, the ability to predict network parameters such as cell load will be a key enabler of several proposed adaptation and resource allocation techniques. In this study, we consider a joint exploitation of spatio-temporal data to improve the prediction accuracy of standard regression methods. We test several such methods from the literature on a publicly available dataset and document the advantages of the proposed approach.
2017 6th International Conference on Modern Circuits and Systems Technologies, MOCAST 2017
9781509043866
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11577/3249021
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