Time-correlation analysis of GSM telephone traffic in dense population district

Yin, Dandan; Zhang, Sihai; Zhou, Wuyang; Zheng, Yi

doi:10.1109/wcsp.2014.6992111

Cited by 5 publications

(1 citation statement)

References 12 publications

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“…In this section, we re-consider the time-correlation issue of call arrivals, using CDR data of large-scale base stations from a dense population district in one large northern China city, based on our previous work [30]. We first verify the long-range in (24 hours) time-correlation of call arrivals but find call arrivals in a minute is uncorrelated to the number of call arrivals in another minute in short-term.…”

Section: Case Study II : Call Detail Recordmentioning

confidence: 99%

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

Zhang

Yin

Zhang

et al. 2015

IEEE Trans. Emerg. Topics Comput.

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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.

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Section: Case Study II : Call Detail Recordmentioning

confidence: 99%

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

Zhang

Yin

Zhang

et al. 2015

IEEE Trans. Emerg. Topics Comput.

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Closed-Form Expressions for the Quantile Function of the Chi Square Distribution Using the Hybrid of Quantile Mechanics and Spline Interpolation

Okagbue

Adamu

Anake

2020

Wireless Pers Commun

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Chi square distribution is a continuous probability distribution primarily used in hypothesis testing, contingency analysis, and construction of confidence limits in inferential statistics but not necessarily in the modeling of real-life phenomena.The closed-form expression for the quantile function (QF) of Chi square is not available because the cumulative distribution function cannot be transformed to yield the QF and consequently places limitations on the use of the QF. Researchers have over the years proposed approximations that improve over time in terms of speed, computational efficiency, and precision, and so on. However, most of the available closed-form expressions (quantile Search Log inClosed-Form Expressions for the Quantile Function of the Chi Square Dis...

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Performance Analysis of Mobile Cellular Networks with MMPP Call Arrival Patterns

Castellanos-Lopez¹,

Cruz-Perez²,

Hernandez-Valdez³

et al. 2018

2018 9th IFIP International Conference on New Technologies, Mobility and Security (NTMS)

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Time-correlation analysis of GSM telephone traffic in dense population district

Cited by 5 publications

References 12 publications

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

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

Closed-Form Expressions for the Quantile Function of the Chi Square Distribution Using the Hybrid of Quantile Mechanics and Spline Interpolation

Performance Analysis of Mobile Cellular Networks with MMPP Call Arrival Patterns

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