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由于用户社会属性的存在,复杂蜂窝移动网络的业务特征和用户行为在时域、空域和内容等多维度上的分布都呈现出以群体为特征的聚集行为规律.以往静态、孤岛式的网络资源配置方法造成了网络资源的巨大浪费,因此利用用户群体行为特征规律将存在巨大的能效和资源利用提升空间.基于对实际运营的蜂窝移动通信系统中数据的采集和测量,首先从空间、时间等多个维度对用户群体聚集行为进行了深入分析研究,得到了基站流量在空域、时域和空–时联合的分布规律.研究表明,业务在空间符合Log-normal分布,其参数与典型区域类型有关;用户数及其产生的业务量随着时间变化具有明显的规律性,正弦叠加模型能够很好地反映出现网实际业务量的变化情况.其次,通过对空域和时域的联合分析,得到了能精准预测基站业务变化的空–时联合分布模型.与实际数据对比发现,该模型准确度可以达到93%以上.为了更明确地表征用户群体聚集行为,利用经济学中的基尼系数对用户群体聚集行为进行了数学定义和定量描述.最后,基于所提出的业务空–时模型和用户群体行为聚集模型,提出了几种高能效的无线网络资源配置方法、传输控制方法和基站分级休眠策略,探索利用用户群体行为规律提升无线网络能效的新途径.
Due to the existence of social attributes of users, the distribution characteristics of multi-dimensions of business features and user behaviors of complex cellular mobile networks in the time domain, airspace and content all exhibit a behavior of aggregated behavior characterized by groups. In the past, static, island-shaped network resources Configuration method has caused a huge waste of network resources, so there will be huge space for energy efficiency and resource utilization by using the characteristics of user group behavior.Based on the collection and measurement of data in the actual operation of cellular mobile communication system, first from space, time, etc. The distribution of base station traffic in the airspace, time domain and space-time combination is studied in many dimensions.The results show that the traffic is log-normal distribution in space, and its parameters are similar to the typical regional types The number of users and the traffic generated by them have obvious regularity over time, and the sinusoidal superposition model can well reflect the changes of actual network traffic.Secondly, through the joint analysis of airspace and time domain, An air-time joint distribution model that can accurately predict the change of the base station service is compared with the actual data We find that the accuracy of the model can reach more than 93% .In order to more clearly characterize the aggregate behavior of user groups, we use the Gini coefficient in economics to define and quantitatively describe the aggregate behavior of user groups.Finally, - time models and user group behavior aggregation models, several energy efficient wireless network resource allocation methods, transmission control methods and base station hierarchical dormancy strategies are put forward to explore new ways to enhance the energy efficiency of wireless networks by using user group behavior rules.