17789917. DYNAMIC FUNCTIONAL SPLITTING SYSTEMS AND METHODS simplified abstract (RAKUTEN MOBILE, INC.)
Contents
- 1 DYNAMIC FUNCTIONAL SPLITTING SYSTEMS AND METHODS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 DYNAMIC FUNCTIONAL SPLITTING SYSTEMS AND METHODS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
DYNAMIC FUNCTIONAL SPLITTING SYSTEMS AND METHODS
Organization Name
Inventor(s)
Mohammed Soliman of Tokyo (JP)
Krishnan Venkataraghavan of Tokyo (JP)
DYNAMIC FUNCTIONAL SPLITTING SYSTEMS AND METHODS - A simplified explanation of the abstract
This abstract first appeared for US patent application 17789917 titled 'DYNAMIC FUNCTIONAL SPLITTING SYSTEMS AND METHODS
Simplified Explanation
The patent application describes a method for determining an optimal functional split for a radio access network (RAN) using machine learning techniques.
- Obtaining network data related to the performance of RAN elements with a current functional split.
- Analyzing the network data using a machine learning model to determine the best functional split for optimizing network performance.
- Outputting the determined optimal functional split for configuring the RAN elements.
Potential Applications
This technology could be applied in telecommunications companies to improve the efficiency and performance of their radio access networks.
Problems Solved
This technology helps in optimizing network performance and efficiency by determining the best functional split for RAN elements.
Benefits
- Improved network performance - Enhanced efficiency - Cost savings through optimized configurations
Potential Commercial Applications
"Optimizing Radio Access Network Performance with Machine Learning"
Possible Prior Art
There may be prior art related to optimizing network configurations using machine learning models in the field of telecommunications.
Unanswered Questions
How does the machine learning model adapt to changing network conditions over time?
The article does not specify how the machine learning model accounts for dynamic changes in network conditions.
What are the specific performance metrics used to determine the optimal functional split?
The article does not detail the exact performance metrics considered in the analysis.
Original Abstract Submitted
A method for determining an optimal functional split for a radio access network (RAN), includes: obtaining network data relating to performance of RAN elements configured with a current functional split; analyzing, by a machine learning model, the obtained network data to determine an optimum functional split, from among a predetermined plurality of functional splits, for optimizing network performance under current network conditions; and outputting the determined optimum functional split for configuring the RAN elements.
