Research on crowd flows prediction model for 5G demand

Zheng HU; Hao YUAN; Xinning ZHU; Wanli NI

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

您当前的位置：

首页 >

文章列表页 >

Research on crowd flows prediction model for 5G demand

Topics:5G and AI | 更新时间：2024-06-05

- Research on crowd flows prediction model for 5G demand
- Journal on Communications Vol. 40, Issue 2, Pages: 1-10(2019)
- 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：
- 基金信息：
  
  The National Natural Science Foundation for Creative Research Groups of China(61421061);The Major Science and Technology Plan Project of Hainan Provinc(ZDKJ201808)
- DOI：10.11959/j.issn.1000-436x.2019042
  CLC： TP393
- Online First：2019-02，
  
  Published：25 February 2019
- 稿件说明：
移动端阅览
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：

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.

关键词

Keywords

references

CHEN M , QIAN Y , HAO Y , et al . Data-driven computing and caching in 5G networks:architecture and delay analysis [J ] . IEEE Wireless Communications , 2018 , 25 ( 1 ): 70 - 75 .

CHEN N C , XIE W , WELSCH R E , et al . Comprehensive predictions of tourists' next visit location based on call detail records using machine learning and deep learning methods [C ] // IEEE International Congress on Big Data (BigData Congress) . 2017 : 1 - 6 .

FENG M , MAO S , JIANG T . Base station on-off switching in 5G wireless networks:approaches and challenges [J ] . IEEE Wireless Communications , 2017 , 24 ( 4 ): 46 - 54 .

OUGHTON E , FRIAS Z , RUSSELL T , et al . Towards 5G:scenario-based assessment of the future supply and demand for mobile telecommunications infrastructure [J ] . Technological Forecasting and Social Change , 2018 ,S0040162517313525.

KUMAR S V . Traffic flow prediction using kalman filtering technique [J ] . Procedia Engineering , 2017 , 187 : 582 - 587 .

LI X , PAN G , WU Z , et al . Prediction of urban human mobility using large-scale taxi traces and its applications [J ] . Frontiers of Computer Science in China , 2012 , 6 ( 1 ): 111 - 121 .

MATIAS M , LUIS , GAMA , et al . Predicting taxi-passenger demand using streaming data [J ] . IEEE Transactions on Intelligent Transportation Systems , 2013 , 14 ( 3 ): 1393 - 1402 .

TONG Y , CHEN Y , ZHOU Z , et al . The simpler the beter:a unified approach to predicting original taxi demands based on large-scale online platforms [C ] // ACM SIGKDD International Conference on Knowledge Discovery ＆ Data Mining . ACM , 2017 : 1653 - 1662 .

WU F , WANG H , LI Z . Interpreting traffic dynamics using ubiquitous urban data [C ] // ACM Sigspatial International Conference on Advances in Geographic Information Systems . 2016 , 69 ( 3 ): 1 - 4 .

YAO H , WU F , KE J , et al . Deep multi-view spatial-temporal network for taxi demand prediction [C ] // AAAI Conference on Artificial Intelligence . 2018 : 1 - 8 .

LIU Y , ZHENG H , FENG X , et al . Short-term traffic flow prediction with Conv-LSTM [C ] // International Conference on Wireless Communications ＆ Signal Processing . 2017 : 1 - 6 .

POLSON N G , SOKOLOV V O . Deep learning for short-term traffic flow prediction [J ] . Transportation Research Part C:Emerging Technologies , 2017 , 79 : 1 - 17 .

ZHANG J , ZHENG Y , QI D . Deep spatio-temporal residual networks for citywide crowd flows prediction [C ] // AAAI . 2017 : 1655 - 1661 .

ZHANG J , ZHENG Y , QI D , et al . DNN-based prediction model for spatio-temporal data [C ] // Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems . 2016 :92.

HUANG C , ZHANG J , ZHENG Y , et al . DeepCrime:attentive hierarchical recurrent networks for crime prediction [C ] // The 27th ACM International Conference . 2018 : 1423 - 1432 .

ZHANG J , ZHENG Y , QI D , et al . Predicting citywide crowd flows using deep spatio-temporal residual networks [J ] . Artificial Intelligence , 2018 :S0004370218300973.

GROVER A , LESKOVEC J . Node2vec:scalable feature learning for networks [C ] // ACM SIGKDD Conference on know Ledge Discovery and Data Mining . 2016 : 855 - 864 .

WU W , WANG Y , GOMES J B , et al . Oscillation resolution for mobile phone cellular tower data to enable mobility modelling [C ] // IEEE International Conference on Mobile Data Management.IEEE Computer Society . 2014 : 321 - 328 .

SHAD S A , CHEN E , BAO T . Cell oscillation resolution in mobility profile building [J ] . International Journal of Computer Science Issues , 2012 , 9 ( 3 ).

XIAOLEI M , ZHUANG D , ZHENGBING H , et al . Learning traffic as images:a deep convolutional neural network for large-scale transportation network speed prediction [J ] . Sensors , 2017 , 17 ( 4 ): 818 - 833 .

Views

2723

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

No data

Related Author

No data

Related Institution

No data

AI问答

⁰