The volume and types of traffic data in mobile cellular networks have been increasing continuously. Meanwhile, traffic data change dynamically in several dimensions such as time and space. Thus, traffic modeling is essential for theoretical analysis and energy efficient design of future ultra-dense cellular networks. In this paper, the authors try to build a tractable and accurate model to describe the traffic variation pattern for a single base station in real cellular networks. Firstly a sinusoid superposition model is proposed for describing the temporal traffic variation of multiple base stations based on real data in a current cellular network. It shows that the mean traffic volume of many base stations in an area changes periodically and has three main frequency components. Then, lognormal distribution is verified for spatial modeling of real traffic data. The spatial traffic distributions at both spare time and busy time are analyzed. Moreover, the parameters of the model are presented in three typical regions: park, campus and central business district. Finally, an approach for combined spatial-temporal traffic modeling of single base station is proposed based on the temporal and spatial traffic distribution of multiple base stations. All the three models are evaluated through comparison with real data in current cellular networks. The results show that these models can accurately describe the variation pattern of real traffic data in cellular networks.
%0 Conference Paper
%1 7277444
%A Wang, Shuo
%A Zhang, Xing
%A Zhang, Jiaxin
%A Feng, Jian
%A Wang, Wenbo
%A Xin, Ke
%B Teletraffic Congress (ITC 27), 2015 27th International
%D 2015
%K Analytical_models Base_stations Data_models Distribution_functions Graphical_models Mathematical_model Mobile_communication busy_time_analysis cellular_radio energy_efficient_design frequency_components future_ultra-dense_cellular_networks itc itc27 log_normal_distribution lognormal_distribution mean_traffic_volume mobile_cellular_networks mobile_computing sinusoid_superposition_model spare_time_analysis spatial-temporal_traffic_modeling spatial_traffic_distribution telecommunication_traffic temporal_traffic_distribution temporal_traffic_variation traffic_data traffic_variation_pattern
%P 203-209
%R 10.1109/ITC.2015.31
%T An Approach for Spatial-Temporal Traffic Modeling in Mobile Cellular Networks
%U https://gitlab2.informatik.uni-wuerzburg.de/itc-conference/itc-conference-public/-/raw/master/itc27/7277444.pdf?inline=true
%X The volume and types of traffic data in mobile cellular networks have been increasing continuously. Meanwhile, traffic data change dynamically in several dimensions such as time and space. Thus, traffic modeling is essential for theoretical analysis and energy efficient design of future ultra-dense cellular networks. In this paper, the authors try to build a tractable and accurate model to describe the traffic variation pattern for a single base station in real cellular networks. Firstly a sinusoid superposition model is proposed for describing the temporal traffic variation of multiple base stations based on real data in a current cellular network. It shows that the mean traffic volume of many base stations in an area changes periodically and has three main frequency components. Then, lognormal distribution is verified for spatial modeling of real traffic data. The spatial traffic distributions at both spare time and busy time are analyzed. Moreover, the parameters of the model are presented in three typical regions: park, campus and central business district. Finally, an approach for combined spatial-temporal traffic modeling of single base station is proposed based on the temporal and spatial traffic distribution of multiple base stations. All the three models are evaluated through comparison with real data in current cellular networks. The results show that these models can accurately describe the variation pattern of real traffic data in cellular networks.
@inproceedings{7277444,
abstract = {The volume and types of traffic data in mobile cellular networks have been increasing continuously. Meanwhile, traffic data change dynamically in several dimensions such as time and space. Thus, traffic modeling is essential for theoretical analysis and energy efficient design of future ultra-dense cellular networks. In this paper, the authors try to build a tractable and accurate model to describe the traffic variation pattern for a single base station in real cellular networks. Firstly a sinusoid superposition model is proposed for describing the temporal traffic variation of multiple base stations based on real data in a current cellular network. It shows that the mean traffic volume of many base stations in an area changes periodically and has three main frequency components. Then, lognormal distribution is verified for spatial modeling of real traffic data. The spatial traffic distributions at both spare time and busy time are analyzed. Moreover, the parameters of the model are presented in three typical regions: park, campus and central business district. Finally, an approach for combined spatial-temporal traffic modeling of single base station is proposed based on the temporal and spatial traffic distribution of multiple base stations. All the three models are evaluated through comparison with real data in current cellular networks. The results show that these models can accurately describe the variation pattern of real traffic data in cellular networks.},
added-at = {2016-07-11T18:20:14.000+0200},
author = {Wang, Shuo and Zhang, Xing and Zhang, Jiaxin and Feng, Jian and Wang, Wenbo and Xin, Ke},
biburl = {https://www.bibsonomy.org/bibtex/27223e2e78eaf95214447a937368908f0/itc},
booktitle = {Teletraffic Congress (ITC 27), 2015 27th International},
doi = {10.1109/ITC.2015.31},
interhash = {3f639ee6322a222f21970637b4251365},
intrahash = {7223e2e78eaf95214447a937368908f0},
keywords = {Analytical_models Base_stations Data_models Distribution_functions Graphical_models Mathematical_model Mobile_communication busy_time_analysis cellular_radio energy_efficient_design frequency_components future_ultra-dense_cellular_networks itc itc27 log_normal_distribution lognormal_distribution mean_traffic_volume mobile_cellular_networks mobile_computing sinusoid_superposition_model spare_time_analysis spatial-temporal_traffic_modeling spatial_traffic_distribution telecommunication_traffic temporal_traffic_distribution temporal_traffic_variation traffic_data traffic_variation_pattern},
month = {Sept},
pages = {203-209},
timestamp = {2020-04-30T18:18:14.000+0200},
title = {An Approach for Spatial-Temporal Traffic Modeling in Mobile Cellular Networks},
url = {https://gitlab2.informatik.uni-wuerzburg.de/itc-conference/itc-conference-public/-/raw/master/itc27/7277444.pdf?inline=true},
year = 2015
}