Intel corporation (20240137288). DATA-CENTRIC SERVICE-BASED NETWORK ARCHITECTURE simplified abstract
Contents
- 1 DATA-CENTRIC SERVICE-BASED NETWORK ARCHITECTURE
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 DATA-CENTRIC SERVICE-BASED NETWORK ARCHITECTURE - 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 Original Abstract Submitted
DATA-CENTRIC SERVICE-BASED NETWORK ARCHITECTURE
Organization Name
Inventor(s)
Dawei Ying of Hillsboro OR (US)
DATA-CENTRIC SERVICE-BASED NETWORK ARCHITECTURE - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240137288 titled 'DATA-CENTRIC SERVICE-BASED NETWORK ARCHITECTURE
Simplified Explanation
The abstract describes a data-centric network and non-real-time RAN intelligence controller architecture. The data-centric network architecture provides data plane functions that serve as a shared database for control functions, user functions, and management functions for data plane resources in a network. The non-RT RIC provides functions via RAPPs, manages the RAPPs, performs conflict mitigation and security functions, monitors machine learning performance, provides a ML model catalog, provides interface terminations, and stores ML data and near-RT RIC related information in a database.
- Data-centric network architecture with data plane functions serving as a shared database for control, user, and management functions.
- Non-real-time RAN intelligence controller providing functions via RAPPs, managing RAPPs, performing conflict mitigation and security functions, monitoring machine learning performance, and providing a ML model catalog.
Potential Applications
The technology could be applied in telecommunications networks, IoT systems, and edge computing environments.
Problems Solved
The architecture helps in efficient data management, conflict mitigation, security enforcement, and machine learning model training and evaluation.
Benefits
The benefits include improved network performance, enhanced security, optimized resource management, and better machine learning model training.
Potential Commercial Applications
Potential commercial applications include network infrastructure providers, IoT solution providers, and edge computing service providers.
Possible Prior Art
One possible prior art could be existing data-centric network architectures with shared databases for control and management functions.
Unanswered Questions
How does the architecture handle scalability in large networks?
The abstract does not provide details on how the architecture addresses scalability challenges in large networks.
What are the specific security measures implemented in the non-real-time RAN intelligence controller?
The abstract mentions security functions, but it does not specify the exact security measures implemented in the architecture.
Original Abstract Submitted
a data-centric network and non-real-time (rt) ran intelligence controller (ric) architecture are described. the data-centric network architecture provides data plane functions (dpfs) that serve as a shared database for control functions, user functions and management functions for data plane resources in a network. the dpfs interact with control plane functions, user plane functions, management plane functions, compute plane functions, network exposure functions, and application functions of the nr network via a service interface. the non-rt ric provides functions via rapps, manages the rapps, performs conflict mitigation and security functions, monitors machine learning (ml) performance, provides a ml model catalog that contains ml model information, provides interface terminations and stores ml data and near-rt ric related information in a database. an ml training host trains and evaluates ml models in the catalog, obtains training and testing data from the database, and retrains and updates the ml models.
