AUTONOMAS FAULT DIAGNOSIS SYSTEM FOR CELLULAR NETWORKS BASED ON HIDDEN MARKOV MODEL

@inproceedings{AbdelMoez2015AUTONOMASFD,
  title={AUTONOMAS FAULT DIAGNOSIS SYSTEM FOR CELLULAR NETWORKS BASED ON HIDDEN MARKOV MODEL},
  author={Omar AbdelMoez and Asem M. Ali and Talaat Abdelhamid},
  year={2015}
}
Automated diagnosis and Troubleshooting (TS) in Radio Access Networks (RAN) of cellularsystems are basic management tasks, which are required to guarantee efficient use of networkresources. In this paper, we investigate the usage of machine learning techniques: stochasticmethods and discriminant analysis for automating these TS tasks. Our proposed framework is basedon Hidden Markov Model (HMM), Principle Component Analysis (PCA) and Fisher LinearDiscriminant (FLD) techniques. In a learning… 

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SHOWING 1-10 OF 27 REFERENCES
Automated Diagnosis for UMTS Networks Using Bayesian Network Approach
TLDR
Results for the automated diagnosis using both network simulator and real UMTS network measurements illustrate the efficiency of the proposed TS approach and its importance to mobile network operators.
Comparison of probabilistic models used for diagnosis in cellular networks
TLDR
A probabilistic diagnosis model based on discrete Bayesian networks (BNs) is proposed for radio access part of a mobile communication system and a BN structure is selected for diagnosis in cellular networks.
Demo: SONVer: SON verification for operational cellular networks
TLDR
SONVer is presented, a tool that performs SON verification, using anomaly detection and diagnosis techniques that operate within a specified spatial scope larger than an individual cell, and indicates the root cause for anomalous conditions.
Automated troubleshooting of a mobile communication network using Bayesian networks
TLDR
A new method for automating the troubleshooting task is presented, thereby freeing a large portion of resources for other purposes, and positive results from trials in a commercial network are presented.
Mechanical Fault Diagnostics of Onload Tap Changer Within Power Transformers Based on Hidden Markov Model
Online monitoring of the mechanical performance of onload tap changers (OLTCs) within high-voltage (HV) power transformers is of utmost significance for a safe, stable, and reliable operation of the
A new bearing fault detection and diagnosis scheme based on hidden Markov modeling of vibration signals
  • H. Ocak, K. Loparo
  • Engineering
    2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221)
  • 2001
TLDR
A new bearing fault detection and diagnosis scheme based on hidden Markov modeling (HMM) of vibration signals that allows for online detection of faults by monitoring the probabilities of the pre-trained HMM for the normal case and the diagnosis of the fault by the HMM that gives the highest probability.
GSM System Engineering
From the Publisher: Take a comprehensive look at the land-based infrastructure and networking of the global system for mobile communications with this practical guide. You'll see the complete
Fundamentals of Cellular Network Planning and Optimisation: 2G/2.5G/3G... Evolution to 4G
TLDR
Fundamentals of Cellular Network Planning and Optimisation covers end-to-end network planning and optimisation aspects from second generation GSM to third generation WCDMA networks including GPRS and EDGE networks.
Introduction To Telecommunications Network Engineering
TLDR
This chapter discusses the development of the global system for Mobile Communications, GSM, and PCS-1900, and the basic concept of a Transmission System, which was established in this chapter.
Heart diseases diagnosis using HMM
TLDR
A diagnostic technique for heart diseases using heart sounds is suggested and wavelet decomposition and mel cepstrum are used for feature extraction and classification of the different heart diseases is done using hidden Markov models (HMM).
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