一种使用协作预测的自组织网络故障检测方法（英文）
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摘要
为了提升自组织网络的自动管理能力,实现有效的自治愈,研究了无线网络的小区故障问题,提出了一种基于协作预测的小区故障检测方法.通过利用改进的协同过滤算法,考虑了无线网络中用户的位置相关性,同时通过引入协作灰度预测模型,给出了用户运动过程的时间相关性.模拟了基站正常运行的场景,收集用户数据进行模型训练并选取阈值,在模拟的故障场景下有效地实现了故障的检测.仿真结果表明,所提方法在用户稀少的密集小基站网络中比传统故障检测方法具有更高的检测率,并保证了更低的通信开销和虚警率.在用户稀少的情况下,所提方法的故障检测率比传统研究方法提升了14%左右,同时,所提方法在邻居用户的帮助下能够检测到无活动用户的故障小区.
In order to improve the efficiency of automatic management and self-healing of the self-organizing network(SON), a cell outage problem is investigated and a cooperative prediction-based automatic cell outage detection algorithm is proposed. By the improved collaborative filtering prediction algorithm, the location correlation of users in the wireless network is considered. By incorporating the cooperative grey model prediction algorithm, the time correlation of users' motion trajectory is also introduced. Data of users in a normal scenario is simulated and collected for model training and threshold calculating and the outage cell can be effectively detected using the proposed approach. The simulation results demonstrate that the proposed scheme has a higher detection rate for different extents of outage while ensuring the lower communication overhead and false alarm rate than traditional outage detection methods. The detection rate of the proposed approach outperforms the traditional method by around 14%, especially when there are sparse users in the network, and it is able to detect the outage cell with no active users with the help of neighbor cells.
In order to improve the efficiency of automatic management and self-healing of the self-organizing network(SON), a cell outage problem is investigated and a cooperative prediction-based automatic cell outage detection algorithm is proposed. By the improved collaborative filtering prediction algorithm, the location correlation of users in the wireless network is considered. By incorporating the cooperative grey model prediction algorithm, the time correlation of users' motion trajectory is also introduced. Data of users in a normal scenario is simulated and collected for model training and threshold calculating and the outage cell can be effectively detected using the proposed approach. The simulation results demonstrate that the proposed scheme has a higher detection rate for different extents of outage while ensuring the lower communication overhead and false alarm rate than traditional outage detection methods. The detection rate of the proposed approach outperforms the traditional method by around 14%, especially when there are sparse users in the network, and it is able to detect the outage cell with no active users with the help of neighbor cells.
引文
[1] Liao Q,Wiczanowski M,Stańczak S.Toward cell outage detection with composite hypothesis testing [C]//IEEE International Conference on Communications (ICC).Ottawa,ON,Canada,2012:4883-4887.______DOI:10.1109/ICC.2012.6364384.
[2] De-La-bandera I,Barco R,Munoz P,et al.Cell outage detection based on handover statistics[J].IEEE Communications Letters,2015,19(7):1189-1192.DOI:10.1109/lcomm.2015.2426187.
[3] Turkka J,Chernogorov F,Brigatti K,et al.An approach for network outage detection from drive-testing databases[J].Journal of Computer Networks and Communications,2012,2012:1-13.DOI:10.1155/2012/163184.
[4] Zoha A,Saeed A,Ali I,et al.Data-driven analytics for automated cell outage detection in self-organizing networks [C]//2015 11th International Conference on the Design of Reliable Communication Networks (DRCN).Kansas City,MO,USA,2015:203-210.DOI:10.1109/DRCN.2015.7149014.
[5] Wang J,Phan N Q,Pan Z W,et al.An improved TCM-based approach for cell outage detection for self-healing in LTE HetNets [C]//2016 IEEE 83rd Vehicular Technology Conference (VTC Spring).Nanjing,China,2016:16125563-1-16125563-5.______DOI:10.1109/VTCSpring.2016.7504129.
[6] Xue W Q,Peng M G,Ma Y,et al.Classification-based approach for cell outage detection in self-healing heterogeneous networks[C]//2014 IEEE Wireless Communications and Networking Conference (WCNC).Istanbul,Turkey,2014:2822-2826.DOI:10.1109/WCNC.2014.6952896.
[7] Alias M,Saxena N,Roy A.Efficient cell outage detection in 5G HetNets using hidden Markov model[J].IEEE Communications Letters,2016,20(3):562-565.DOI:10.1109/lcomm.2016.2517070.
[8] Wang W,Zhang J,Zhang Q.Cooperative cell outage detection in self-organizing femtocell networks [C]//Proceedings IEEE INFOCOM.Turin,Italy,2013:782-790.DOI:10.1109/INFCOM.2013.6566865.
[9] Onireti O,Zoha A,Moysen J,et al.A cell outage management framework for dense heterogeneous networks[J].IEEE Transactions on Vehicular Technology,2016,65(4):2097-2113.DOI:10.1109/tvt.2015.2431371.
[10] Mueller C M,Kaschub M,Blankenhorn C,et al.A cell outage detection algorithm using neighbor cell list reports[M]//Self-Organizing Systems.Berlin,Germany:Springer,2008:218-229.DOI:10.1007/978-3-540-92157-8_19.
[11] Zhang T,Feng L,Yu P,et al.A handover statistics based approach for cell outage detection in self-organized heterogeneous networks [C]//IFIP/IEEE Symposium on Integrated Network and Service Management (IM).Lisbon,Portugal,2017:628-631.______DOI:10.23919/INM.2017.7987346.
[12] Hamalainen S,Sanneck H,Sartori C.LTE self-organizing networks(SON):Network management automation for operational efficiency[M].Beijing:China Machine Press,2013.
[13] 3rd Generation Partnership Project.Technical specification group radio access network;Envolved universal terrestrial radio access (E-UTRA);Further advancements for E-UTRA physical layer aspects,3GPP TR 36.201 [S].3rd Generation Partnership Project,2012.
[2] De-La-bandera I,Barco R,Munoz P,et al.Cell outage detection based on handover statistics[J].IEEE Communications Letters,2015,19(7):1189-1192.DOI:10.1109/lcomm.2015.2426187.
[3] Turkka J,Chernogorov F,Brigatti K,et al.An approach for network outage detection from drive-testing databases[J].Journal of Computer Networks and Communications,2012,2012:1-13.DOI:10.1155/2012/163184.
[4] Zoha A,Saeed A,Ali I,et al.Data-driven analytics for automated cell outage detection in self-organizing networks [C]//2015 11th International Conference on the Design of Reliable Communication Networks (DRCN).Kansas City,MO,USA,2015:203-210.DOI:10.1109/DRCN.2015.7149014.
[5] Wang J,Phan N Q,Pan Z W,et al.An improved TCM-based approach for cell outage detection for self-healing in LTE HetNets [C]//2016 IEEE 83rd Vehicular Technology Conference (VTC Spring).Nanjing,China,2016:16125563-1-16125563-5.______DOI:10.1109/VTCSpring.2016.7504129.
[6] Xue W Q,Peng M G,Ma Y,et al.Classification-based approach for cell outage detection in self-healing heterogeneous networks[C]//2014 IEEE Wireless Communications and Networking Conference (WCNC).Istanbul,Turkey,2014:2822-2826.DOI:10.1109/WCNC.2014.6952896.
[7] Alias M,Saxena N,Roy A.Efficient cell outage detection in 5G HetNets using hidden Markov model[J].IEEE Communications Letters,2016,20(3):562-565.DOI:10.1109/lcomm.2016.2517070.
[8] Wang W,Zhang J,Zhang Q.Cooperative cell outage detection in self-organizing femtocell networks [C]//Proceedings IEEE INFOCOM.Turin,Italy,2013:782-790.DOI:10.1109/INFCOM.2013.6566865.
[9] Onireti O,Zoha A,Moysen J,et al.A cell outage management framework for dense heterogeneous networks[J].IEEE Transactions on Vehicular Technology,2016,65(4):2097-2113.DOI:10.1109/tvt.2015.2431371.
[10] Mueller C M,Kaschub M,Blankenhorn C,et al.A cell outage detection algorithm using neighbor cell list reports[M]//Self-Organizing Systems.Berlin,Germany:Springer,2008:218-229.DOI:10.1007/978-3-540-92157-8_19.
[11] Zhang T,Feng L,Yu P,et al.A handover statistics based approach for cell outage detection in self-organized heterogeneous networks [C]//IFIP/IEEE Symposium on Integrated Network and Service Management (IM).Lisbon,Portugal,2017:628-631.______DOI:10.23919/INM.2017.7987346.
[12] Hamalainen S,Sanneck H,Sartori C.LTE self-organizing networks(SON):Network management automation for operational efficiency[M].Beijing:China Machine Press,2013.
[13] 3rd Generation Partnership Project.Technical specification group radio access network;Envolved universal terrestrial radio access (E-UTRA);Further advancements for E-UTRA physical layer aspects,3GPP TR 36.201 [S].3rd Generation Partnership Project,2012.