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.
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