hendral
2011-12-02, 01:17 AM
This is a disertation.
This thesis explores the use of statistical learning for the automated healing process. In this context, the eectiveness of statistical learning for automated RRM has been investigated. This is achieved by modeling the functional relationships between the RRM parameters and Key Performance Indicators
(KPIs). A generic automated RRM architecture has been proposed.
This generic architecture has been used to study the application of statistical learning approach to auto-tuning and performance monitoring of the wireless networks.
The use of statistical learning in the automated healing of wireless networks introduces two important diculties: Firstly, the KPI measurements obtained from the network are noisy, hence this noise can partially mask the actual behaviour of KPIs. Secondly, these automated healing algorithms are
iterative. After each iteration the network performance is typically evaluated over the duration of a day with new network parameter settings. Hence, the iterative algorithms should achieve their QoS objective in a minimum number
of iterations. Automated healing methodology developped in this thesis, based on statistical modeling, addresses these two issues. The automated healing algorithms developped are computationaly light and converge in a few number of iterations. This enables the implemenation of these algorithms
in the Operation and Maintenance Center (OMC) in the on-line mode.
http://www.mediafire.com/?5nkih411a8zm8ti
My other thread you can find to:
http://www.finetopix.com/showthread.php?24087-System-Level-Analysis-of-LTE-Advanced-with-Emphasis-on-Multi-Component-Carrier-Mgmt&highlight=
No pass.
If need password. Password is pdklb
Pls share your reputation.
This thesis explores the use of statistical learning for the automated healing process. In this context, the eectiveness of statistical learning for automated RRM has been investigated. This is achieved by modeling the functional relationships between the RRM parameters and Key Performance Indicators
(KPIs). A generic automated RRM architecture has been proposed.
This generic architecture has been used to study the application of statistical learning approach to auto-tuning and performance monitoring of the wireless networks.
The use of statistical learning in the automated healing of wireless networks introduces two important diculties: Firstly, the KPI measurements obtained from the network are noisy, hence this noise can partially mask the actual behaviour of KPIs. Secondly, these automated healing algorithms are
iterative. After each iteration the network performance is typically evaluated over the duration of a day with new network parameter settings. Hence, the iterative algorithms should achieve their QoS objective in a minimum number
of iterations. Automated healing methodology developped in this thesis, based on statistical modeling, addresses these two issues. The automated healing algorithms developped are computationaly light and converge in a few number of iterations. This enables the implemenation of these algorithms
in the Operation and Maintenance Center (OMC) in the on-line mode.
http://www.mediafire.com/?5nkih411a8zm8ti
My other thread you can find to:
http://www.finetopix.com/showthread.php?24087-System-Level-Analysis-of-LTE-Advanced-with-Emphasis-on-Multi-Component-Carrier-Mgmt&highlight=
No pass.
If need password. Password is pdklb
Pls share your reputation.