Unsupervised Fault Diagnosis Platform Implementation for Self-Healing in Cellular Networks

2020 ◽  
Author(s):  
Yuxuan Zhang ◽  
Xian Zhang ◽  
Yaohua Sun
Keyword(s):  
Fault Diagnosis ◽  
Cellular Networks ◽  
Self Healing

scholarly journals Self-Organizing Network (SON) Functionalities for Mobility Management in WiFi Networks

2021 ◽  
Author(s):  
◽  
Lina Hao
Keyword(s):  
Cellular Networks ◽  
Mobility Management ◽  
Network Performance ◽  
Data Transfer ◽  
Critical Issue ◽  
Base Stations ◽  
Machine Learning Techniques ◽  
Self Healing ◽  
Wifi Networks ◽  
Self Organizing

<p>WiFi networks based on the IEEE 802.11 standard are widely used indoors or outdoors as simple and cost-effective wireless technology. However, the data connection is significantly disrupted when mobile stations (STAs) switch between access points (APs). Furthermore, high packet loss occurs during the switching period. Therefore, mobility is a critical issue that needs to be solved in WiFi networks.  In cellular networks, handover is used to keep ongoing data transfer when network clients switch between base stations. However, the handover algorithm is not supported in the 802.11 standard for WiFi networks. Self-Organizing Network (SON) functionality enables seamless handover in cellular networks, improving network performance. However, the SON functionality has not been fully researched in WiFi networks, especially for mobility management.  Motivated by the SON functionalities, a SON approach is proposed to automatically optimize the handover algorithms for WiFi networks. This approach focuses on the SON functionalities including self-configuration, self-optimization and self-healing using machine learning techniques to develop new algorithms for WiFi mobility management. The overall goal of this thesis is to optimize handover performance as well as enhance the network’s capabilities.</p>

On the Diagnostic Methods of Bayesian-Network in Smart Grid

2011 ◽  
Vol 71-78 ◽  
pp. 2424-2428
Author(s):  
Han Mei Hu ◽  
Jun Lei Zhao ◽  
Ping Wen Tu
Keyword(s):  
Fault Diagnosis ◽  
Smart Grid ◽  
Bayesian Network ◽  
Real Time ◽  
Fault Tolerant ◽  
Fault Isolation ◽  
Diagnostic Methods ◽  
Self Healing ◽  
Diagnosis Method ◽  
Time Accuracy

Aiming at the smart grid self-healing characteristics, puts forward a Bayesian network fault diagnosis method. According to the protection movement signal and the circuit breaker tripping signal, establish the face of components of the smart grid line fault diagnosis model. The fault diagnosis method is real-time and accuracy, and fault-tolerant ability etc. characteristics. This method not only satisfy intelligent power grid self-healing characteristics on fault diagnosis real-time, accuracy and automatic fault diagnosis of the requirements, but also provide the smart grid fault isolation and system of self recover with strong guarantee.

Active Learning-Based Fault Diagnosis in Self-Organizing Cellular Networks

2020 ◽  
Vol 24 (8) ◽  
pp. 1734-1737
Author(s):  
Meng Chen ◽  
Kun Zhu ◽  
Ran Wang ◽  
Dusit Niyato
Keyword(s):  
Fault Diagnosis ◽  
Active Learning ◽  
Cellular Networks ◽  
Self Organizing

scholarly journals Self-Organizing Network (SON) Functionalities for Mobility Management in WiFi Networks

2021 ◽  
Author(s):  
◽  
Lina Hao
Keyword(s):  
Cellular Networks ◽  
Mobility Management ◽  
Network Performance ◽  
Data Transfer ◽  
Critical Issue ◽  
Base Stations ◽  
Machine Learning Techniques ◽  
Self Healing ◽  
Wifi Networks ◽  
Self Organizing

<p>WiFi networks based on the IEEE 802.11 standard are widely used indoors or outdoors as simple and cost-effective wireless technology. However, the data connection is significantly disrupted when mobile stations (STAs) switch between access points (APs). Furthermore, high packet loss occurs during the switching period. Therefore, mobility is a critical issue that needs to be solved in WiFi networks.  In cellular networks, handover is used to keep ongoing data transfer when network clients switch between base stations. However, the handover algorithm is not supported in the 802.11 standard for WiFi networks. Self-Organizing Network (SON) functionality enables seamless handover in cellular networks, improving network performance. However, the SON functionality has not been fully researched in WiFi networks, especially for mobility management.  Motivated by the SON functionalities, a SON approach is proposed to automatically optimize the handover algorithms for WiFi networks. This approach focuses on the SON functionalities including self-configuration, self-optimization and self-healing using machine learning techniques to develop new algorithms for WiFi mobility management. The overall goal of this thesis is to optimize handover performance as well as enhance the network’s capabilities.</p>

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