Call arrival model for GSM network including handover

Yin

IEEE Trans. Emerg. Topics Comput.

et al. 2015

As the impressive development of wireless devices and growth of mobile users, telecommunication operators are thirsty for understanding the characteristics of mobile network behavior. Based on the big data generated in the telecommunication networks, telecommunication operators are able to obtain substantial insights by using big data analysis and computing techniques. This paper introduces the important aspects in this topic, including data set information, data analysis techniques and two case studies. We categorize the data set in the telecommunication networks into two types, user-oriented and network-oriented, and discuss the potential application. Then several important data analysis techniques are summarized and reviewed, from temporal and spatial analysis to data mining and statistical test. Finally, we present two case studies, using erlang measurement and call detail record, respectively, to understand the base station behavior. Interestingly, the 'Night Burst' phenomenon of college students is revealed by comparing the base stations location and real-world map, and we conclude that it is not proper to model the voice call arrivals as Poisson process.

Section: User-oriented and Network-oriented Datamentioning

confidence: 99%

Section: Case Study II : Call Detail Recordmentioning

confidence: 99%

Section: Case Study II : Call Detail Recordmentioning

confidence: 99%

See 1 more Smart Citation

Computing on Base Station Behavior Using Erlang Measurement and Call Detail Record

Yin

IEEE Trans. Emerg. Topics Comput.

et al. 2015

2013 19th IEEE International Conference on Networks (ICON)

“…where, p(n) is the probability that n UEs are ACTIVE, and ρ is given by ( (10), (12), and (13), the power consumption of BS, P BS (N ), serving N UEs, can then be calculated as,…”

Section: Base Station (Bs) Modelmentioning

confidence: 99%

“…Authors in [11] analyse the tradeoff between energy efficiency and system performance of femtocell deployment with sleep mode. In the field of modelling, authors in [12] model the call arrival for GSM network including handover events. Modeling of Cellular Digital Packet Data (CDPD) sleep-mode operation using M X /G/1/L queueing system is done in [13].…”

Section: Introductionmentioning

confidence: 99%

Power consumption modeling of femto-assisted cellular networks using Renewal Reward Process

Thakur

Sengupta

Murthy

2013

The demand for bandwidth in cellular networks is ever increasing and most of it originates from indoor users. Femtocells are low-power, low-cost home base stations that can be deployed by end users in homes/offices to satisfy this need without incurring significant deployment and running costs. Widespread deployment of femtocells appear a potential reality in near future, and hence, it is of interest to study its energy saving measures. One promising energy efficiency technique is the introduction of sleep mode on femtocells, allowing them to switch off their radio and associated processing when there are no active devices within their range. In this paper, we model sleep-mode behavior in femtocells using Renewal-Reward-Process and determine the lower bound on power consumption that can be achieved. Power consumption for user equipments and macrocell base station are also modeled to evaluate overall network power consumption. The analytical model is verified through extensive simulation.

Wireless Communications

User behavior modeling of voice communications: an empirical study

Büyükçorak

Kurt

Toprakkiran

2014

In this study, analysis and modeling of arrival and service processes are presented in a comprehensive fashion in order to determine statistical properties of voice traffic from end-user perspective in accordance with the queueing theory. For the first time in the literature, we introduce a user centric approach and examine these services considering both flow directions of voice traffic, the uplink and the downlink as opposed to existing studies with the network centric approach. In our study, we use experimental data composed of actual phone calls collected from 2G/3G networks. To achieve this, we designed and implemented a data collection system for mobile users and compared the results by using data from an operational cellular network. In order to determine the time correlation of voice calls, Hurst parameter estimation methods are used. On the basis of the outcomes, independency of call arrivals is shown. Additionally, it is shown that calls acquired from user and network centric approaches are both Poisson distributed. Next, looking at the problem from service process perspective, thorough analyses are performed to determine mathematical models that can best characterize call holding times. Maximum likelihood estimation and expectation maximization algorithm are used, and it is shown that the optimum mathematical model for the characterization of call holding times is the lognormal distribution family.