1. Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing 100876, China
2. Technology Innovation Center, China Telecom Corporation Limited, Beijing 100032, China
Corresponding author (firstname.lastname@example.org
For the existence of user social patterns, the traffic characteristics and user behavior in complex cellular mobile network show clustering behavior regularity characterized by groups in multi-dimensional distributions, such as time, space, and content. Previous static, island-like network resource allocation methods lead to a massive waste of network resources. Thus, the use of user behavior characteristics can have a huge impact on energy efficiency and resource utilization. Based on data collection and measurements from real cellular mobile communication systems, we analyze the behavior of user group aggregation in spatial, time, and other dimensions, to obtain traffic distributions across base stations in the space, time, and spatio-temporal domains. The study shows that traffic distribution in the space domain conforms to a Log-normal distribution and that its parameters are related to typical region types. The number of users and the amount of traffic they produce show regularity over time, and the Sine superposition model can reflect changes in the traffic volumes in a network. Next, through joint analysis of the space and time domains, we obtain a spatio-temporal joint distribution model that can accurately predict changes in base station traffic. When compared with real data, it is shown that the accuracy of the model reaches over 93%. In order to characterize user group clustering more clearly, we use the Gini coefficient to present a mathematical definition and quantitative description of user group behavior. Finally, based on the proposed spatio-temporal model and user group behavior model, we propose several energy-efficient wireless network resource allocation methods, transmission control methods, and a base station hierarchical sleep strategy for exploring new approaches using user group behavior regularity to improve wireless network energy efficiency.
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