View source for 17956975. INTELLIGENT MACHINE LEARNING (ML)-ENABLED END-TO-END (E2E) AUTOMATED ORCHESTRATION FOR COLLABORATIVE NEXT-GENERATION WIRELESS WIRELINE CONVERGENCE (WWC) simplified abstract (AT&T Intellectual Property I, L.P.)

=INTELLIGENT MACHINE LEARNING (ML)-ENABLED END-TO-END (E2E) AUTOMATED ORCHESTRATION FOR COLLABORATIVE NEXT-GENERATION WIRELESS WIRELINE CONVERGENCE (WWC)=


==Organization Name==

[[:Category:AT&T Intellectual Property I, L.P.|AT&T Intellectual Property I, L.P.]]


[[Category:AT&T Intellectual Property I, L.P.]]

==Inventor(s)==

[[:Category:Eshrat Huda of Hillsborough NJ (US)|Eshrat Huda of Hillsborough NJ (US)]][[Category:Eshrat Huda of Hillsborough NJ (US)]]

[[:Category:David H. Lu of Morganville NJ (US)|David H. Lu of Morganville NJ (US)]][[Category:David H. Lu of Morganville NJ (US)]]

[[:Category:Moshiur Rahman of Marlboro NJ (US)|Moshiur Rahman of Marlboro NJ (US)]][[Category:Moshiur Rahman of Marlboro NJ (US)]]

==INTELLIGENT MACHINE LEARNING (ML)-ENABLED END-TO-END (E2E) AUTOMATED ORCHESTRATION FOR COLLABORATIVE NEXT-GENERATION WIRELESS WIRELINE CONVERGENCE (WWC) - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17956975 titled 'INTELLIGENT MACHINE LEARNING (ML)-ENABLED END-TO-END (E2E) AUTOMATED ORCHESTRATION FOR COLLABORATIVE NEXT-GENERATION WIRELESS WIRELINE CONVERGENCE (WWC)

==Simplified Explanation==

The abstract describes a system that includes a cross-segment slice controller (CSSC) interfacing with a software-defined network (SDN) controller and a software-defined radio (SDR) controller. The system also includes a machine learning (ML) component and an intelligent end-to-end orchestration platform (IEOP) for dynamic cross-segment network slice management.

* The system includes a cross-segment slice controller (CSSC) that interfaces with an SDN controller and an SDR controller.
* A machine learning (ML) component is used to obtain and analyze data regarding the core network and the radio access network (RAN).
* An intelligent end-to-end orchestration platform (IEOP) coordinates with the SDN controller and the SDR controller via the CSSC to provide dynamic cross-segment network slice management.

== Potential Applications ==
This technology could be applied in telecommunications networks, specifically in managing network slices for improved performance and resource allocation.

== Problems Solved ==
This technology solves the challenge of efficiently managing network slices in a dynamic and intelligent manner to optimize network performance.

== Benefits ==
The benefits of this technology include enhanced network performance, improved resource allocation, and dynamic management of network slices based on real-time data analysis.

== Potential Commercial Applications ==
Potential commercial applications of this technology include telecommunications companies looking to optimize their network performance and resource allocation through intelligent network slice management.

== Possible Prior Art ==
One possible prior art could be existing systems that use machine learning and software-defined networking for network management, but the specific combination of components and functionalities described in this patent application may be novel.

=== What are the specific functionalities of the machine learning component in this system? ===

The machine learning component is configured to obtain and analyze data regarding the core network and the radio access network (RAN) to provide insights for dynamic cross-segment network slice management. It may use algorithms to predict network behavior, optimize resource allocation, and improve overall network performance.

=== How does the intelligent end-to-end orchestration platform (IEOP) interact with the SDN controller and the SDR controller? ===

The IEOP coordinates with the SDN controller and the SDR controller via the cross-segment slice controller (CSSC) based on outputs of the machine learning component. It uses these outputs to provide dynamic cross-segment network slice management, ensuring efficient communication and resource allocation between the core network and the radio access network.


==Original Abstract Submitted==

Aspects of the subject disclosure may include, for example, a system including a cross-segment slice controller (CSSC) configured to interface with a software-defined network (SDN) controller and a software-defined radio (SDR) controller. The SDN controller may be associated with a core network and the SDR controller may be associated with a radio access network (RAN). The system further includes a machine learning (ML) component configured to obtain and analyze data regarding the core network and the RAN, and an intelligent end-to-end (E2E) orchestration platform (IEOP) configured to coordinate with the SDN controller and the SDR controller via the CSSC based on outputs of the ML component to provide dynamic cross-segment network slice management. Other embodiments are disclosed.



[[Category:H04W28/08]]
[[Category:H04W28/02]]
[[Category:H04W28/26]]