Self-similarity And Modeling Of LTE / LTE-A Data Traffic

Abstract

The mobile wireless communication system has evolved into its 4th generation with ubiquitous availability assuring higher data rates and reliable communication. To achieve a robust and reliable network, it is necessary to understand the traffic characteristics of the network. In this work, network traffic characteristics like Self-Similarity property and modeling of user arrivals are estimated for real-world LTE & LTE-Advanced (LTE-A) traffic. Lot of work has been done on Self-Similarity of Ethernet and Ad-Hoc Network's data traffic. But Self-Similarity in LTE and LTE-A data traffic has been left un-explored. Real world LTE and LTE-A data traffic collected from a live network has been studied and Self-Similarity property is also evaluated. This work emphasizes the existence of Self-Similarity in LTE and LTE-A data traffic which can facilitate the future research on forecasting data traffic. A degree of Self-Similarity is observed in live LTE and LTE-A networks and a comparison is made between LTE, LTE-A and previously researched Ethernet data traffics. Modeling and understanding the user traffic is of utmost importance to optimize the network. It is important to model and evaluate the incoming user traffic in our current generation of mobile communication systems which dominated data traffic more than the traditional voice traffic. This work will address the validity of considering Poisson Arrival for modeling user arrival traffic in live LTE network.