The analysis of real mobile traffic traces is helpful to understand usage patterns of cellular networks. In particular, mobile data may be used for network optimization and management in terms of radio resources, network planning, energy saving, for instance. However, real network data from the operators is often difficult to be accessed, due to legal and privacy issues. In this paper, we overcome the lack of network information using a LTE sniffer capable of decoding the unencrypted LTE control channel and we present a temporal and spatial analysis of the recorded traces. Moreover, we present a methodology to derive a stochastic characterization for the daily variation of the LTE traffic. We compare our results to the traffic model proposed in the FP7 EARTH project [1] and we show that, with a limited number of states, our model presents a high level of accuracy in terms of first and second order statistics.

Grant numbers : The research leading to this work has received funding from the Spanish Ministry of Economy and Competitiveness under grant TEC2014-60491-R (5GNORM).© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.