The Fault Detection Strategies Used in Mobile Communication Network

Hong Wei Luo; Huai Bin Wang; Jin Feng Li

doi:10.4028/www.scientific.net/AMR.403-408.1786

Paper Titles

Protection Mechanisms and Security Operations of XS128 Flash Memory
p.1767

Research on P2P-VoD System Based on IPv6 Network
p.1772

Introduction of FECs and its Applications on Internet & Wireless Communications
p.1776

Study on Cost Estimate at Product Design Stage Based on Factor Analysis and Neural Network
p.1781

The Fault Detection Strategies Used in Mobile Communication Network
p.1786

Improved Algorithm Based on TFIDF in Text Classification
p.1791

Optical Surface Position Encode and Application Research
p.1795

The New Management Model of Real Estate Enterprise Base on Relationship Marketing
p.1799

Data Mining for ERW Welded Tube Scheduling Rules Based on Decision Tree
p.1804

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 403-408The Fault Detection Strategies Used in Mobile...

The Fault Detection Strategies Used in Mobile Communication Network

Abstract:

With the rapid development of information technology, the use of mobile communication tools to infiltrate every aspect of daily life and people is increasingly demanding for the quality of mobile communication network service. As the user base is increasing, mobile communications networks is also increasing pressure. In the traditional artificial methods about communication network fault detection, network managers need to do a lot of work and the process is tedious but not satisfied with the quality test. It is an effective means of fault management using software to manage the network. The mobile network fault detection system base on data mining not only reduces the workload of network managers and the test results more accurate, but also can reduce network management costs. In this paper, we introduce the fault detection strategies for the mobile communication network. By using the data mining algorithm, we can find the network default more easily.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 403-408)

Pages:

1786-1790

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.403-408.1786

Citation:

Cite this paper

Online since:

November 2011

Authors:

Hong Wei Luo, Huai Bin Wang, Jin Feng Li

Keywords:

Communication Network, Data Mining (DM), Expert System, Fault Detection

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Crosbie, Mark, and Gene Spafford. Applying Genetic Programnning to IntrusionDetection. The AAAI Fall Symposium on Genetic Programming, 2003. 1~8.

Google Scholar

[2] Qin Boping, Zhou Xianwei, Yang Jun&Song Cunyi. Grey-theory based intrusion detection model. Journal of Systems Engineering and Electronics, Vol. 17, No, 1, 2006, p.230~235.

DOI: 10.1016/s1004-4132(06)60040-6

Google Scholar

[3] Simom Haykin. Neural Networks A Comprehension Foundation. Second Edition. Peking: China Machine Press. 2004. 2 1~27.

Google Scholar

[4] Kornel Terplan., Web-based systems&network management[M]. FL: CRC Press, 1999.: 23~25.

Google Scholar

[5] R. Agrowal, et. al. Database Mining: A performance Perspective IEEE Transaction on knowledge and Date Engineering, 1993; (12): 91~95.

Google Scholar

[6] Steven Walczak, Knowledge Acquisition and Knowledge Representation With Class: the Object-Oriented Paradigm. Expert System with Applications, 1 998, 1 5: 235~244.

DOI: 10.1016/s0957-4174(98)00058-x

Google Scholar