面向5G需求的人群流量预测模型研究

胡铮; 袁浩; 朱新宁; 倪万里

doi:10.11959/j.issn.1000-436x.2019042

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面向5G需求的人群流量预测模型研究

专题：5G与AI | 更新时间：2024-06-05

- 面向5G需求的人群流量预测模型研究
- Research on crowd flows prediction model for 5G demand
- 通信学报 2019年40卷第2期页码：1-10
- 作者机构：
  
  北京邮电大学网络与交换技术国家重点实验室，北京 100876
- 作者简介：
  
  [ "胡铮（1980- ），男，贵州贵阳人，博士，北京邮电大学副教授，主要研究方向为行为认知与智能系统。" ]
  [ "袁浩（1994- ），男，河北衡水人，北京邮电大学硕士生，主要研究方向为交通大数据、轨迹数据挖掘与分析等。" ]
  [ "朱新宁（1970- ），女，上海人，博士，北京邮电大学副教授，主要研究方向为时空大数据挖掘。" ]
  [ "倪万里（1995- ），男，重庆人，北京邮电大学博士生，主要研究方向为边缘计算。" ]
- 基金信息：
  
  国家自然科学基金创新研究群体基金资助项目(61421061);海南省重大科技计划基金资助项目(ZDKJ201808)
- DOI：10.11959/j.issn.1000-436x.2019042
  中图分类号： TP393
- 网络首发：2019-02，
  
  纸质出版：2019-02-25
- 稿件说明：
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胡铮, 袁浩, 朱新宁, 等. 面向5G需求的人群流量预测模型研究[J]. 通信学报, 2019,40(2):1-10.

Zheng HU, Hao YUAN, Xinning ZHU, et al. Research on crowd flows prediction model for 5G demand[J]. Journal on Communications, 2019, 40(2): 1-10.
胡铮, 袁浩, 朱新宁, 等. 面向5G需求的人群流量预测模型研究[J]. 通信学报, 2019,40(2):1-10. DOI： 10.11959/j.issn.1000-436x.2019042.

Zheng HU, Hao YUAN, Xinning ZHU, et al. Research on crowd flows prediction model for 5G demand[J]. Journal on Communications, 2019, 40(2): 1-10. DOI： 10.11959/j.issn.1000-436x.2019042.

摘要

5G 网络中超密集基站的部署规划、多维资源管理、活跃/休眠切换等方面都依赖于对区域内用户数量的准确预测。针对这一需求，提出了一种基于移动网络用户位置信息的区域人群流量预测的深度时空网络模型。通过建模不同尺度的时空依赖关系，融合各种外部特征信息，并以短时局部流量信息降低对实时全局信息传输的要求，实现了城市范围的区域人群流量预测，对提高5G网络性能具有重要意义。通过基于呼叫详单数据的区域人群流量预测实验表明，与现有流量预测模型相比，所提模型具有更高的预测精度。

Abstract

The deployment and planning for ultra-dense base stations

multidimensional resource management

and on-off switching in 5G networks rely on the accurate prediction of crowd flows in the specific areas.A deep spatial-temporal network for regional crowd flows prediction was proposed

by using the spatial-temporal data acquired from mobile networks.A deep learning based method was used to model the spatial-temporal dependencies with different scales.External factors were combined further to predict citywide crowd flows.Only data from local regions was applied to model the closeness of properties of the crowd flows

in order to reduce the requirements for transmitting the globe data in real time.It is of importance for improving the performance of 5G networks.The proposed model was evaluated based on call detail record data set.The experiment results show that the proposed model outperforms the other prediction models in term of the prediction precision.

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references

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