LTE traffic analysis and application behavior characterization

Foddis, Gianluca; Garroppo, Rosario G.; Giordano, Stefano; Procissi, Gregorio; Roma, Simone; Topazzi, Simone

doi:10.1109/eucnc.2014.6882660

Cited by 7 publications

(3 citation statements)

References 7 publications

(6 reference statements)

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“…to obtain CV > 1. Previous analyses, such as [6], show that LTE traffic is generated by a set of applications/services that have different behaviors, both at session level and at packet level. Then, we assume that the resulting IRR timeseries is obtained from a sum of behaviors, each one associated with the different applications/services used by the average user.…”

Section: Modelling Inter-rach Timesmentioning

confidence: 99%

Modelling RACH Arrivals and Collisions for Human Type Communication

et al. 2016

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The paper proposes an analytical model to evaluate the collision probability on the Random-Access CHannel (RACH) in LTE systems as a function of the number of User Equipments (UEs), the number of available preambles and the Inter-arrival times of the RACH Requests (IRR) of the average user. The model for the IRR of the average user is obtained from real traffic data captured at the eNodeB of a mobile operator, and is derived by emulating the RRC state machine for different Radio Resource Control Inactivity Timer (RRC IT ) settings. The results of the presented study suggest that when RRC IT is set to a few seconds a mixture model is more accurate than the Poisson hypothesis both in modelling the IRR and in estimating the RACH performance.

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Section: Modelling Inter-rach Timesmentioning

confidence: 99%

Modelling RACH Arrivals and Collisions for Human Type Communication

et al. 2016

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“…Huang et al did an experimental setup for testing web applications' traffic and associated energy usage over LTE [7]. The analyses were extended by Foddis et al focusing on application behaviour [8]. These studies have the problems of either being controlled experiments or limited to a few base stations, and may not reflect the average conditions in the real world.…”

Section: Previous Measurement Studiesmentioning

confidence: 99%

Energy Usage Forecasting for LTE: A Network-Wide Traffic Measurements Study

Kure

Engelstad

Maharjan

et al. 2018

2018 IEEE Globecom Workshops (GC Wkshps)

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Energy usage in LTE base stations are driven by spectral efficiency and traffic. To predict the energy usage these parameters must be forecasted. In this work we analyse hourly measurements collected from more than 12000 base station cells spread across more than 3700 base stations over the course of one month. We show that the two parameters are very weakly correlated, and therefore we investigated them separately. Further, we evaluated the possible gains for advanced prediction methods using a large scale search for individually fitted time series (SARIMA models) for each base station. In total, we examined and evaluated approximately 31000 time series models from an identified group of 4 million potential models. We found that the spectral efficiency measurements can be represented fairly well with time series models, with only an average 6.5% relative error. The time series models have to be individually adapted for each base station as the unsupervised clustering showed that each cluster's members have a wide variety of best fitted models. However, for traffic the time series models have relative high prediction errors, and we believe there is a potential for new methods to improve the forecasts.

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“…We are interested in studying the arrivals to the eNodeB requesting for any kind of service either directly or indirectly. This work is based on a real world live network similar to [19] which is based on real traffic.…”

Section: Comparison Of Data Setsmentioning

confidence: 99%