Efficient Cell Outage Detection in 5G HetNets Using Hidden Markov Model
Efficient Cell Outage Detection in 5G HetNets Using Hidden Markov Model
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DOI：
10.1109/lcomm.2016.2517070
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发表时间：
2016-01
期刊：
影响因子：
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通讯作者：
Multazamah Alias;N. Saxena;A. Roy
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文献类型：
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作者：
Multazamah Alias;N. Saxena;A. Roy
下一代5G无线系统设想了具有大量异构小区的超密集网络。这使得这种异构网络（HetNets）的管理非常复杂，如果没有任何自动化过程，实际上是不可能的。自组织网络（SON）有望为5G无线网络的自动化管理提供自配置、自优化、自修复功能。电池中断检测被认为是一个关键问题，需要有效的自我检测过程。在这封信中，我们首先将5G基站（BSs）分为四种不同的状态。随后，我们探索了一个隐马尔可夫模型来自动捕获BSs的当前状态，并概率地估计单元中断。在典型的密集5G HetNets上的仿真结果表明，我们提出的策略能够以平均80%的准确率预测BS的状态，并在95%的时间内正确检测到蜂窝中断。
Next generation 5G wireless systems envision ultra dense networks with a huge number of heterogeneous cells. This makes the management of such heterogeneous networks (HetNets) very complex and practically impossible without any automated procedure. Self-organizing networks (SON) are expected to provide self-configuration, self-optimization, and self-healing functions for automated management of 5G wireless networks. Cell outage detection is identified as a critical problem that requires efficient self-detection process. In this letter, we first classify the 5G base stations (BSs) into four different states. Subsequently, we explore a hidden Markov model to automatically capture current states of the BSs and probabilistically estimate a cell outage. Simulation results on typical, dense 5G HetNets demonstrate that our proposed strategy is capable of predicting the state of a BS at an average of 80% accuracy, as well as correctly detecting a cell outage ~ 95% of the time.