Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (11A): 210800165-7.doi: 10.11896/jsjkx.210800165

• Computer Networ • Previous Articles     Next Articles

Traffic Prediction for Wireless Communication Networks with Multi-source and Cross-domain Data Fusion

MA Ji1, LIN Shang-jing2, LI Yue-ying2, ZHUANG Bei2, JIA Rui2, TIAN Jin1   

  1. 1 School of Networking and Communication Engineering,Jinling Institute of Technology,Nanjing 211169,China
    2 School of Electronic Engineering,Beijing University of Posts and Telecommunications,Beijing 100876,China
  • Online:2022-11-10 Published:2022-11-21
  • About author:MA Ji,born in 1982,Ph.D,lecturer.His main research interests include deep learning,distributed computing and edge computing.
  • Supported by:
    National Key R & D Program of China(2019YFC1511400),Key Laboratory of Universal Wireless Communications(BUPT),Ministry of Education(KFKT-2020102) and Fundamental Research Funds for the Central Universities(BUPT)( 2021RC07).

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Abstract: Precise prediction of wireless communication network traffic can assist operators in fine-tuned operations to efficiently allocate and deploy base station resources,and cater to a large number of emerging business needs.However,the highly complex temporal-spatial dependence and the influence of multi-source and cross-domain factors make accurate prediction of wireless communication traffic face huge challenges.Firstly,the correlation analysis of wireless communication traffic from temporal,spatial,social and natural attributes shows that wireless communication traffic has multi-source and cross-domain characteristics.Secondly,this paper proposes an improved dense fully connected network model MST-DenseNet.The model uses the convolution operation of a single DenseUnit structure to capture the spatial correlation of traffic,and uses multiple parallel DenseUnit structures to capture the temporal correlation of traffic on different scales.At the same time,considering the impact of cross-domain dataset on wireless traffic,this model integrates the temporal and spatial characteristics of communication traffic itself with the social and natural characteristics of cross-domain dataset to achieve accurate prediction of wireless communication traffic.Experiments show that,on the actual cellular dataset,MST-DenseNet has higher prediction accuracy compared with existing model.

Key words: Wireless traffic prediction, Multi-source cross-domain, Data fusion, Temporal-spatial dependence, Densenet

CLC Number: 

  • TN92
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