RPMA low-power wide-area network planning method based on data mining
A network planning method based on data mining was proposed for RPMA low-power wide-area network with large density of base stations and uneven traffic distribution.First,a signal quality prediction model was established by using the boosting regression trees algorithm,which was used to extract the...
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Format: | Article |
Language: | zho |
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Editorial Department of Journal on Communications
2019-03-01
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Series: | Tongxin xuebao |
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Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2019050/ |
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author | Xiaorong ZHU Yao SHEN |
author_facet | Xiaorong ZHU Yao SHEN |
author_sort | Xiaorong ZHU |
collection | DOAJ |
description | A network planning method based on data mining was proposed for RPMA low-power wide-area network with large density of base stations and uneven traffic distribution.First,a signal quality prediction model was established by using the boosting regression trees algorithm,which was used to extract the coverage distribution spacial pattern of the network.Then ,the weighted k-centroids clustering algorithm was utilized to obtain the optimal base station deployment for the current spacial pattern.Finally,according to the total objective function,the best base station topology was determined.Experiment results with the real data sets show that compared with the traditional network planning method,the proposed method can improve the coverage of low-power wide-area networks. |
format | Article |
id | doaj-art-a82658483f1b4e0b87efef53319d1515 |
institution | Kabale University |
issn | 1000-436X |
language | zho |
publishDate | 2019-03-01 |
publisher | Editorial Department of Journal on Communications |
record_format | Article |
series | Tongxin xuebao |
spelling | doaj-art-a82658483f1b4e0b87efef53319d15152025-01-14T07:16:27ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2019-03-0140283559725532RPMA low-power wide-area network planning method based on data miningXiaorong ZHUYao SHENA network planning method based on data mining was proposed for RPMA low-power wide-area network with large density of base stations and uneven traffic distribution.First,a signal quality prediction model was established by using the boosting regression trees algorithm,which was used to extract the coverage distribution spacial pattern of the network.Then ,the weighted k-centroids clustering algorithm was utilized to obtain the optimal base station deployment for the current spacial pattern.Finally,according to the total objective function,the best base station topology was determined.Experiment results with the real data sets show that compared with the traditional network planning method,the proposed method can improve the coverage of low-power wide-area networks.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2019050/low power wide area networkboosting regression treesweighted k-centroidsbase station deployment |
spellingShingle | Xiaorong ZHU Yao SHEN RPMA low-power wide-area network planning method based on data mining Tongxin xuebao low power wide area network boosting regression trees weighted k-centroids base station deployment |
title | RPMA low-power wide-area network planning method based on data mining |
title_full | RPMA low-power wide-area network planning method based on data mining |
title_fullStr | RPMA low-power wide-area network planning method based on data mining |
title_full_unstemmed | RPMA low-power wide-area network planning method based on data mining |
title_short | RPMA low-power wide-area network planning method based on data mining |
title_sort | rpma low power wide area network planning method based on data mining |
topic | low power wide area network boosting regression trees weighted k-centroids base station deployment |
url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2019050/ |
work_keys_str_mv | AT xiaorongzhu rpmalowpowerwideareanetworkplanningmethodbasedondatamining AT yaoshen rpmalowpowerwideareanetworkplanningmethodbasedondatamining |