2015
DOI: 10.1109/tetc.2015.2389614
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Computing on Base Station Behavior Using Erlang Measurement and Call Detail Record

Abstract: 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 ca… Show more

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Cited by 23 publications
(6 citation statements)
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References 28 publications
(34 reference statements)
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“…Contrary to prior studies, this paper provides a fundamentally different approach, i.e., a proactive approach, that builds on the lines of Big Data empowered Self Organizing Network (BSON) vision presented for 5G in [37] leveraging CDRs to simultaneously minimize energy consumption and ICI in emerging ultra-dense networks. Several studies have demonstrated the usefulness of using real-world CDRs data in the mobile network analysis and planning in comparison to analytical approaches [38], [39]. The authors in [38] have performed a spatio-temporal analysis of CDRs data collected from various base stations in China.…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…Contrary to prior studies, this paper provides a fundamentally different approach, i.e., a proactive approach, that builds on the lines of Big Data empowered Self Organizing Network (BSON) vision presented for 5G in [37] leveraging CDRs to simultaneously minimize energy consumption and ICI in emerging ultra-dense networks. Several studies have demonstrated the usefulness of using real-world CDRs data in the mobile network analysis and planning in comparison to analytical approaches [38], [39]. The authors in [38] have performed a spatio-temporal analysis of CDRs data collected from various base stations in China.…”
Section: Introductionmentioning
confidence: 99%
“…Several studies have demonstrated the usefulness of using real-world CDRs data in the mobile network analysis and planning in comparison to analytical approaches [38], [39]. The authors in [38] have performed a spatio-temporal analysis of CDRs data collected from various base stations in China. It has been concluded that call arrival patterns vary over time and locations and Poisson distribution model over 1 hour interval is inaccurate and it has been pointed out that advance machine learning algorithms can help model the phenomena more precisely.…”
Section: Introductionmentioning
confidence: 99%
“…e number of layers of the network is deep, the number of feature maps extracted from each layer is large, and the scale and semantic information are different. erefore, it is necessary to carry out effective feature reorganization on the feature maps extracted from each layer of the backbone network, and it is of certain significance to use the global semantic information of the image [23]. Suppose the convolution kernel size is as follows:…”
Section: Lexical Root Convolutionmentioning
confidence: 99%
“…As for traffic analysis, Zhang et al [33] first categorized the data set in the telecommunication networks into user-oriented and network-oriented. Thereafter it presents the two case studies in the temporal dimension and spatial dimension.…”
Section: Network Traffic Analysismentioning
confidence: 99%