Research on crowd flows prediction model for 5G demand
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 spatia...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2019-02-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2019042/ |
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| _version_ | 1841539441196269568 |
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| author | Zheng HU Hao YUAN Xinning ZHU Wanli NI |
| author_facet | Zheng HU Hao YUAN Xinning ZHU Wanli NI |
| author_sort | Zheng HU |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-909599843df04d61bc808f54b9cf843f |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2019-02-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-909599843df04d61bc808f54b9cf843f2025-01-14T07:16:15ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2019-02-014011059724878Research on crowd flows prediction model for 5G demandZheng HUHao YUANXinning ZHUWanli NIThe 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.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2019042/5G networkscrowd flows predictiondeep neural networksspatial-temporal data mining |
| spellingShingle | Zheng HU Hao YUAN Xinning ZHU Wanli NI Research on crowd flows prediction model for 5G demand Tongxin xuebao 5G networks crowd flows prediction deep neural networks spatial-temporal data mining |
| title | Research on crowd flows prediction model for 5G demand |
| title_full | Research on crowd flows prediction model for 5G demand |
| title_fullStr | Research on crowd flows prediction model for 5G demand |
| title_full_unstemmed | Research on crowd flows prediction model for 5G demand |
| title_short | Research on crowd flows prediction model for 5G demand |
| title_sort | research on crowd flows prediction model for 5g demand |
| topic | 5G networks crowd flows prediction deep neural networks spatial-temporal data mining |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2019042/ |
| work_keys_str_mv | AT zhenghu researchoncrowdflowspredictionmodelfor5gdemand AT haoyuan researchoncrowdflowspredictionmodelfor5gdemand AT xinningzhu researchoncrowdflowspredictionmodelfor5gdemand AT wanlini researchoncrowdflowspredictionmodelfor5gdemand |