浏览全部资源
扫码关注微信
南京邮电大学江苏省无线通信重点实验室,江苏 南京 210003
[ "朱晓荣(1977- ),女,山东临沂人,博士,南京邮电大学教授、博士生导师,主要研究方向为5G网络、异构网络、无线传感器网络等无线资源管理、跨层优化算法及协议设计、性能评估及建模分析等。" ]
[ "沈瑶(1994- ),女,江苏常州人,南京邮电大学硕士生,主要研究方向为5G网络优化、无线大数据处理等。" ]
网络出版日期:2019-03,
纸质出版日期:2019-03-25
移动端阅览
朱晓荣, 沈瑶. 基于数据挖掘的RPMA低功耗广域网网络规划方法[J]. 通信学报, 2019,40(3):28-35.
Xiaorong ZHU, Yao SHEN. RPMA low-power wide-area network planning method based on data mining[J]. Journal on communications, 2019, 40(3): 28-35.
朱晓荣, 沈瑶. 基于数据挖掘的RPMA低功耗广域网网络规划方法[J]. 通信学报, 2019,40(3):28-35. DOI: 10.11959/j.issn.1000-436x.2019050.
Xiaorong ZHU, Yao SHEN. RPMA low-power wide-area network planning method based on data mining[J]. Journal on communications, 2019, 40(3): 28-35. DOI: 10.11959/j.issn.1000-436x.2019050.
针对RPMA低功耗广域网基站密度大、业务分布不均匀等特点,提出了一种基于数据挖掘的网络规划方法。首先,利用提升回归树算法建立了信号质量预测模型,用于提取网络的覆盖分布空间模式;然后,针对覆盖分布空间模式,采用加权k-centroids分簇算法得到适应当前模式的最优基站部署;最后,根据总目标函数判定得到最终的基站拓扑。通过真实数据集的仿真实验结果表明,与传统的网络规划方法相比,所提的方法很好地提升低功耗广域网网络的覆盖质量。
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.
PATEL D , WON M . Experimental study on low power wide area networks (LPWAN) for mobile internet of things [C ] // Vehicular Technology Conference . IEEE , 2017 : 1 - 5 .
HERNANDEZ D M , PERALTA G , MANERO L , et al . Energy and coverage study of LPWAN schemes for Industry 4.0 [C ] // Electronics,Control,Measurement,Signals and their Application to Mechatronics . IEEE , 2017 : 1 - 6 .
KRUPKA L , VOJTECH L , NERUDA M . The issue of LPWAN technology coexistence in IoT environment [C ] // International Conference on Mechatronics-Mechatronika . IEEE , 2016 : 1 - 8 .
YU G J , YEH K Y . A k-means based small cell deployment algorithmfor wireless access networks [C ] // International Conference on Networking and Network Applications . IEEE , 2016 : 393 - 398 .
WANG S , ZHAO W , WANG C . Budgeted cell planning for cellular networks with small cells [J ] . IEEE Transactions on Vehicular Technology , 2015 , 64 ( 10 ): 4797 - 4806 .
AMALDI E , CAPONE A , MALUCELLI F . Planning UMTS base station location:optimization models with power control and algorithms [J ] . IEEE Transactions on Wireless Communications , 2003 , 2 ( 5 ): 939 - 952 .
LEE C Y , KANG H G . Cell planning with capacity expansion in mobile communications:a tabu search approach [J ] . IEEE Transactions on Vehicular Technology , 2000 , 49 ( 5 ): 1678 - 1691 .
GHAZZAI H , YAACOUB E , ALOUINI M S , et al . Optimized LTE cell planning with varying spatial and temporal user densities [J ] . IEEE Transactions on Vehicular Technology , 2016 , 65 ( 3 ): 1575 - 1589 .
YANG Z H , CHEN M , WEN Y P , et al . Cell planning based on minimized power consumption for LTE networks [C ] // IEEE Wireless Communications and Networking Conference . IEEE , 2016 : 1 - 6 .
WANG S , RAN C . Rethinking cellular network planning and optimization [J ] . IEEE Wireless Communications , 2016 , 23 ( 2 ): 118 - 125 .
ISTV S , FAZEKAS P . An algorithm for automatic base station placement in cellular network deployment [C ] // EUNICE/IFIP WG 6.6 Conference on Networked Services and Applications:Engineering,Control and Management . Springer-Verlag , 2010 : 21 - 30 .
FRIEDMAN J H . Greedy function approximation:a gradient boosting machine [J ] . The Annals of Statistics , 2001 , 29 ( 5 ): 1189 - 1232 .
WEN R , YAN W , ZHANG A N . Weighted clustering of spatial pattern for optimal logistics hub deployment [C ] // IEEE International Conference on Big Data . IEEE , 2016 : 3792 - 3797 .
KANUNGO T , MOUNT D M , NETANYAHU N S , et al . An efficient k-means clustering algorithm:analysis and implementation [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2002 , 40 ( 7 ): 881 - 892 .
0
浏览量
694
下载量
0
CSCD
关联资源
相关文章
相关作者
相关机构