View source for 18399314. DATA-CENTRIC SERVICE-BASED NETWORK ARCHITECTURE simplified abstract (Intel Corporation)

=DATA-CENTRIC SERVICE-BASED NETWORK ARCHITECTURE=


==Organization Name==

[[:Category:Intel Corporation|Intel Corporation]]


[[Category:Intel Corporation]]

==Inventor(s)==

[[:Category:Geng Wu of Portland OR (US)|Geng Wu of Portland OR (US)]][[Category:Geng Wu of Portland OR (US)]]

[[:Category:Leifeng Ruan of Beijing (CN)|Leifeng Ruan of Beijing (CN)]][[Category:Leifeng Ruan of Beijing (CN)]]

[[:Category:Qian Li of Beaverton OR (US)|Qian Li of Beaverton OR (US)]][[Category:Qian Li of Beaverton OR (US)]]

[[:Category:Dawei Ying of Hillsboro OR (US)|Dawei Ying of Hillsboro OR (US)]][[Category: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 18399314 titled 'DATA-CENTRIC SERVICE-BASED NETWORK ARCHITECTURE

==Simplified Explanation==

The data-centric network architecture and non-Real-Time RAN Intelligence Controller (RIC) described in the patent application provide a shared database for control functions, user functions, and management functions in a network. The non-RT RIC manages functions via rApps, performs conflict mitigation and security functions, monitors machine learning performance, and provides a ML model catalog.

* Data-centric network architecture with data plane functions serving as a shared database for control, user, and management functions.
* Non-RT RIC managing functions via rApps, performing conflict mitigation, security functions, and monitoring machine learning performance.
* ML training host training and evaluating ML models, obtaining data from the database, and retraining ML models.

== Potential Applications ==
This technology could be applied in telecommunications networks, specifically in the management and optimization of data plane resources.

== Problems Solved ==
This technology helps in efficiently managing and optimizing data plane resources in a network, improving overall network performance and reliability.

== Benefits ==
The benefits of this technology include enhanced network performance, improved resource management, and increased reliability in telecommunications networks.

== Potential Commercial Applications ==
Potential commercial applications of this technology include telecommunications companies looking to optimize their network resources and improve overall network performance.

== Possible Prior Art ==
One possible prior art could be the use of data-centric network architectures in other industries such as cloud computing or data centers.

=== Unanswered Questions ===

=== How does the non-RT RIC handle security functions in the network? ===
The abstract mentions that the non-RT RIC performs security functions, but it does not provide specific details on how these functions are implemented.

=== What types of machine learning models are included in the ML model catalog? ===
The abstract mentions a ML model catalog, but it does not specify the types of machine learning models that are included in this catalog.


==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.



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