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蜂窝网络中故障管理的位置感知:一种集成方法。

Location-Awareness for Failure Management in Cellular Networks: An Integrated Approach.

机构信息

Departamento de Ingeniería de Comunicaciones, Campus de Teatinos s/n, Universidad de Málaga, Andalucía Tech, 29071 Málaga, Spain.

Faculty of Information Technology, University of Jyväskylä, 40014 Jyväskylä, Finland.

出版信息

Sensors (Basel). 2021 Feb 22;21(4):1501. doi: 10.3390/s21041501.

Abstract

Recent years have seen the proliferation of different techniques for outdoor and, especially, indoor positioning. Still being a field in development, localization is expected to be fully pervasive in the next few years. Although the development of such techniques is driven by the commercialization of location-based services (e.g., navigation), its application to support cellular management is considered to be a key approach for improving its resilience and performance. When different approaches have been defined for integrating location information into the failure management activities, they commonly ignore the increase in the dimensionality of the data as well as their integration into the complete flow of networks failure management. Taking this into account, the present work proposes a complete integrated approach for location-aware failure management, covering the gathering of network and positioning data, the generation of metrics, the reduction in the dimensionality of such data, and the application of inference mechanisms. The proposed scheme is then evaluated by system-level simulation in ultra-dense scenarios, showing the capabilities of the approach to increase the reliability of the supported diagnosis process as well as reducing its computational cost.

摘要

近年来,出现了许多用于室外定位,尤其是室内定位的不同技术。尽管定位仍处于发展阶段,但预计在未来几年内将完全普及。尽管此类技术的发展是由基于位置的服务(例如导航)的商业化推动的,但将其应用于支持蜂窝网络管理被认为是提高其弹性和性能的关键方法。当为将位置信息集成到故障管理活动中定义了不同的方法时,它们通常会忽略数据维度的增加以及将其集成到网络故障管理的完整流程中。有鉴于此,本工作提出了一种完整的基于位置感知的故障管理集成方法,涵盖了网络和定位数据的收集、指标的生成、数据的降维和推理机制的应用。然后,通过超密集场景中的系统级仿真对所提出的方案进行了评估,展示了该方法提高所支持诊断过程的可靠性以及降低其计算成本的能力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/def1/7926715/3bca31dff84a/sensors-21-01501-g001.jpg

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