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We present a large-scale cell based measurement analysis of the user behavior in a live operational HSDPA network. The motivations are: first, to understand the statistical properties of users in cells for refining network planning procedures; and second, to provide realistic traffic models for simulations of cellular packet-oriented networks. We provide an analysis of mean cell load over daytime, as well as models for short-term cell load in terms of user activity and throughput. Furthermore, an evaluation of user-sessions with respect to duration and mean throughput is given. Our findings lead to four different models reflecting realistic user-traffic load of cellular networks. To verify the concept we present respective simulations which investigate multiuser-scheduling in an LTEnetwork. They show that conventional simulation settings can lead to an overestimation of performance.

ARMA models are well-suited for capturing autocorrelations of time series. However, in the context of network traffic modeling they are rarely used for their often claimed inappropriateness for fitting Long Range Dependence (LRD) processes. This letter provides evidence that LRD effects can be well approximated by ARMA models; but only the classical fitting algorithms are inappropriate for this task. Accordingly, we propose a novel algorithm, which deploys a multi-scale fitting procedure. It achieves high accuracy up to an arbitrary cutoff lag, yielding parsimonious ARMA models. Our findings encourage a stronger integration of the ARMA framework into the field of network traffic modeling.Index Terms-Long-range dependece, traffic modeling, ARMA model, parsimoniousness.

SensorInput Processing Output Display, actuators, signals, control D iscussions of how to measure the performance of computer networks for various applications have been ongoing for over twenty years in the area of network research. The continual increase of data traffic volume has reached a point at which solving any network problem by over-provisioning is not suitable. The quest for alternatives makes it vital to have well-defined metrics for evaluating and sustaining the performance of networks.In this tutorial, we provide a detailed introduction of how delay can be measured in a network, especially considering the so-called stateful, reactive and non-symmetric network setups found in mobile cellular networks today. In this context, the term stateful refers to the different states a user session can assume in such networks. Different states correspond to connection types of different physical performance, resulting in different levels of performance, e.g., delay. Changes between the states are triggered by central network elements based on the traffic patterns. The network is reactive to user traffic. Different user patterns will result in different states and therefore different performance results. Given the ambiguous definitions of delay, we present the alternative definitions found in literature and standards, highlight the different parameters impacting the delay of a network packet, and help the reader begin by selecting the right definition of his/ her problem. We introduce active and passive measurements, discuss the best setup for delay measurements, introduce the need for time synchronized network measurement nodes to obtain one-way delay (OWD) results, and then combine the concepts and present a methodology accompanied by real world measurement examples. Detailed analysis of the results reveals the impact of different network settings and parameters in the network. We close the tutorial with a summary and present a flow chart to guide the reader toward an optimal setup for delay measurement.

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