Automatic diagnosis of mobile communication networks under imprecise parameters
Automatic diagnosis of mobile communication networks under imprecise parameters
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DOI：
10.1016/j.eswa.2007.09.030
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发表时间：
2009-01-01
影响因子：
8.5
通讯作者：
Lazaro, Pedro
中科院分区：
文献类型：
--
作者：
Barco, Raquel;Diez, Luis;Lazaro, Pedro
近年来，随着网络复杂性的不断提高，蜂窝网络的自组织成为网络管理的一个重要方面。自动故障识别，即诊断，是自愈中最困难的任务。提出了一种基于离散贝叶斯网络的蜂窝系统无线接入网故障诊断模型。通常，模型参数的不准确性是不可避免的（BN中离散症状和概率的区间限制）。为了提高贝叶斯网络的性能，提出了一种模拟人类推理过程中“连续性”的方法，称为光滑贝叶斯网络（SBNs）。SBN旨在降低诊断准确性对模型参数定义不精确性的敏感性。一个实证研究活动已在现场GSM/GPRS网络中进行，以评估所提出的技术的性能。结果表明，SBN优于传统的BN时，有模型参数的不准确性。(C)2007爱思唯尔有限公司保留所有权利。
In the last years, self-organization of cellular networks is becoming a crucial aspect of network management due to the increasing complexity of the networks. Automatic fault identification, i.e. diagnosis, is the most difficult task in self-healing. In this paper, a model based on discrete bayesian networks (BNs) is proposed for diagnosis of radio access networks of cellular systems. Normally, inaccuracies are unavoidable in the parameters of the model (interval limits for discretized symptoms and probabilities in the BN). In order to enhance the performance of BNs, a methodology to model the "continuity" in the human reasoning is presented, named smooth bayesian networks (SBNs). SBNs are intended to decrease the sensitivity of diagnosis accuracy to imprecision in the definition of the model parameters. An empirical research campaign has been carried out in a live GSM/GPRS network in order to assess the performance of the proposed techniques. Results have shown that SBNs outperform traditional BNs when there is inaccuracy in the model parameters. (C) 2007 Elsevier Ltd. All rights reserved.