View source for 18390043. Optimizing Usage of Power Using Switch Off of Cells simplified abstract (Nokia Solutions and Networks Oy)

=Optimizing Usage of Power Using Switch Off of Cells=


==Organization Name==

[[:Category:Nokia Solutions and Networks Oy|Nokia Solutions and Networks Oy]]


[[Category:Nokia Solutions and Networks Oy]]

==Inventor(s)==

[[:Category:Lorenzo Maggi of Paris (FR)|Lorenzo Maggi of Paris (FR)]][[Category:Lorenzo Maggi of Paris (FR)]]

==Optimizing Usage of Power Using Switch Off of Cells - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18390043 titled 'Optimizing Usage of Power Using Switch Off of Cells

The method described in the abstract involves determining a search region for finding a pair of thresholds in a Bayesian model, with a minimum threshold deactivating cells and a maximum threshold activating cells. The search region starts with values corresponding to activated cells and increases. Key performance indicators associated with the cells are retrieved and values of the thresholds from the latest deployment period are converted and updated based on the indicators.

* Bayesian model threshold optimization method
* Search region defined for minimum and maximum thresholds
* Key performance indicators retrieved and used for updating thresholds
* Values from latest deployment period converted and updated
* Optimization based on cell activation and deactivation thresholds

Potential Applications:
- Network optimization in telecommunications
- Resource allocation in cloud computing
- Anomaly detection in cybersecurity

Problems Solved:
- Efficient threshold optimization in Bayesian models
- Improved performance based on key indicators
- Automated updating of thresholds for better results

Benefits:
- Enhanced network performance
- Cost-effective resource allocation
- Increased cybersecurity measures

Commercial Applications:
Title: "Dynamic Threshold Optimization for Enhanced Network Performance"
This technology can be utilized in telecommunications companies, cloud service providers, and cybersecurity firms to optimize thresholds for improved efficiency and performance. The market implications include cost savings, enhanced customer satisfaction, and competitive advantages in the industry.

Questions about Bayesian Model Threshold Optimization:

1. How does this method improve network performance compared to traditional threshold setting techniques?
This method dynamically adjusts thresholds based on key performance indicators, leading to optimized network performance.

2. What are the potential drawbacks of using automated threshold optimization in Bayesian models?
Automated optimization may require continuous monitoring and adjustment to ensure optimal performance.


==Original Abstract Submitted==

A method includes determining, for a Bayesian model, a search region for determining a pair of thresholds having a minimum threshold and a maximum threshold, wherein the minimum threshold value deactivating at least one cell of a group of cells and the maximum value activating at least one cell of the group, defining the search region to have a starting point in which the pair of thresholds has values corresponding to the cells included in the group being activated during a deployment period and the values increase in the search region, retrieving a key performance indicator, associated with the group of cells, collected during latest deployment period, retrieving a value of the minimum threshold and a value of the maximum threshold applied during the latest deployment period and converting the retrieved values, and updating based on the key performance indicator and the retrieved and converted values.



[[Category:H04B17/15]]
[[Category:H04L41/14]]
[[Category:H04L41/5009]]
[[Category:CPC_H04B17/15]]